From d0cf987a0a19fe0f37352480f378eca559739808 Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Thu, 18 Nov 2021 18:10:28 +0100 Subject: [PATCH 01/14] update badges --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 50c8e85..45017c2 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -[![Python package](https://github.com/stefanbschneider/mobile-env/actions/workflows/python-package.yml/badge.svg)](https://github.com/stefanbschneider/mobile-env/actions/workflows/python-package.yml) -[![Documentation Status](https://readthedocs.org/projects/mobile-env/badge/?version=latest)](https://mobile-env.readthedocs.io/en/latest/?badge=latest) -[![Publish](https://github.com/stefanbschneider/mobile-env/actions/workflows/python-publish.yml/badge.svg)](https://github.com/stefanbschneider/mobile-env/actions/workflows/python-publish.yml) +[![CI](https://github.com/stefanbschneider/mobile-env/actions/workflows/python-package.yml/badge.svg)](https://github.com/stefanbschneider/mobile-env/actions/workflows/python-package.yml) +[![PyPI](https://github.com/stefanbschneider/mobile-env/actions/workflows/python-publish.yml/badge.svg)](https://github.com/stefanbschneider/mobile-env/actions/workflows/python-publish.yml) +[![Documentation](https://readthedocs.org/projects/mobile-env/badge/?version=latest)](https://mobile-env.readthedocs.io/en/latest/?badge=latest) [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/stefanbschneider/mobile-env/blob/master/examples/tutorial.ipynb) From 3f182f24770c9801ed20ec546d107e0ae1e5fad9 Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Wed, 24 Nov 2021 12:03:29 +0100 Subject: [PATCH 02/14] add disclaimer for base station icon --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 45017c2..d3e2c14 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,8 @@ To maximize QoE globally, the policy must recognize that (1) the data rate of an

+
+ Base station icon by Clea Doltz from the Noun Project

From 2950cfe8aa52c02c6c645c41a7b315c4cb4c7b4f Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Wed, 24 Nov 2021 12:36:13 +0100 Subject: [PATCH 03/14] fix type hint; set default episode length to 100 --- mobile_env/core/base.py | 6 +++--- mobile_env/{baselines/greedy.py => wrappers/__init__.py} | 0 2 files changed, 3 insertions(+), 3 deletions(-) rename mobile_env/{baselines/greedy.py => wrappers/__init__.py} (100%) diff --git a/mobile_env/core/base.py b/mobile_env/core/base.py index b196125..1dcb896 100644 --- a/mobile_env/core/base.py +++ b/mobile_env/core/base.py @@ -91,7 +91,7 @@ def default_config(cls): """Set default configuration of environment dynamics.""" # set up configuration of environment width, height = 200, 200 - ep_time = 250 + ep_time = 100 config = { # environment parameters: "width": width, @@ -203,8 +203,8 @@ def check_connectivity(self, bs: BaseStation, ue: UserEquipment) -> bool: snr = self.channel.snr(bs, ue) return snr > ue.snr_threshold - def available_connections(self, ue: UserEquipment) -> Dict: - """Returns dict of what basestations users could connect to.""" + def available_connections(self, ue: UserEquipment) -> Set: + """Returns set of what base stations users could connect to.""" stations = self.stations.values() return {bs for bs in stations if self.check_connectivity(bs, ue)} diff --git a/mobile_env/baselines/greedy.py b/mobile_env/wrappers/__init__.py similarity index 100% rename from mobile_env/baselines/greedy.py rename to mobile_env/wrappers/__init__.py From 456a1197cb94d7b7613951b813e0a0a2afd2b92e Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Wed, 24 Nov 2021 13:17:29 +0100 Subject: [PATCH 04/14] reduce training times, install dependencies in colab --- examples/tutorial.ipynb | 2156 ++++++++++++++++++++++++++++++++++++++- 1 file changed, 2113 insertions(+), 43 deletions(-) diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb index 1e1fe43..b6f03d1 100644 --- a/examples/tutorial.ipynb +++ b/examples/tutorial.ipynb @@ -1,35 +1,94 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, - "id": "bd4dee92-ee4a-42b3-a616-844800ddc662", - "metadata": {}, - "outputs": [], + "cell_type": "markdown", "source": [ - "!pip install mobile-env\n", + "# mobile-env: An Open Environment for Autonomous Coordination in Mobile Networks\n", "\n", - "# Load the TensorBoard notebook extension\n", - "%load_ext tensorboard" - ] + "This Google Colaboratory notebook gives an introduction on how to use *mobile-env*,\n", + "which is an open environment for autonomous coordination in mobile networks.\n", + "This notebook demonstrates how to train and evaluate different coordination approaches\n", + "and how to use different reinforcement learning frameworks with mobile-env.\n", + "\n", + "First, we train a multi-agent policy with **RLlib**.\n", + "\n", + "Second, we train a central policy with **stable-baselines3**." + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", 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First, we train a multi-agent policy with **RLlib**. Second, we train a central policy with **stable-baselines3**." + "import mobile_env\n", + "\n", + "# Load the TensorBoard notebook extension\n", + "%load_ext tensorboard\n" ] }, { @@ -37,35 +96,144 @@ "id": "413e1df5-1374-442a-ae6a-87303e05a746", "metadata": {}, "source": [ - "# Multi-Agent Control Setting with RLlib:" + "## Multi-Agent Control with RLlib" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "226d3a42-5dbb-47c2-b3e2-6ff0731a1c56", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: ray[rllib] in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (1.8.0)\n", + "Requirement already satisfied: protobuf>=3.15.3 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from ray[rllib]) (3.19.1)\n", + "Requirement already satisfied: redis>=3.5.0 in 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"Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow) (3.1.1)\n" + ] + } + ], "source": [ - "!pip install -U ray[rllib]" + "# install RLlib and tensorflow\n", + "!pip install ray[rllib]\n", + "!pip install tensorflow" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "527b006d-5584-43b0-8a1c-9eeebeffbbc9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-11-24 12:32:13,571\tINFO worker.py:832 -- Calling ray.init() again after it has already been called.\n" + ] + } + ], "source": [ "import ray\n", "from ray.rllib.agents import ppo\n", "\n", + "# train for 2000 episodes\n", "stop = {\n", - " \"episodes_total\": 2000\n", + " # \"episodes_total\": 2000,\n", + " \"timesteps_total\": 50000,\n", "}\n", "\n", "config = {\n", - " # enviroment configuration:\n", + " # environment configuration:\n", " \"env\": \"mobile-small-ma-v0\",\n", "\n", " # agent configuration:\n", @@ -77,7 +245,7 @@ "}\n", "save_dir = \".\"\n", "\n", - "ray.shutdown()\n", + "# set available CPUs (and GPUs) and init ray\n", "ray.init(\n", " num_cpus=3,\n", " include_dashboard=False,\n", @@ -91,12 +259,14 @@ "id": "6d055b48-a34f-4a8e-9aa3-6b2300eb0207", "metadata": {}, "source": [ - "To use multi-agent policies of RLlib, we must first register our custom OpenAI Gym environment. The RLlibMAWrapper class can be used to wrap the default multi-agent simulation so that it conforms with RLlib's MultiAgentEnv. Now, the environment defines an action and observation space for each user equipment (UE), attributes rewards per UE (per agent) and returns partial observations (no global knowledge for agents)." + "To use multi-agent policies of RLlib,\n", + "we must first register mobile-env as custom OpenAI Gym environment.\n", + "The RLlibMAWrapper class can be used to wrap the default multi-agent simulation so that it conforms with RLlib's MultiAgentEnv. Now, the environment defines an action and observation space for each user equipment (UE), attributes rewards per UE (per agent) and returns partial observations (no global knowledge for agents)." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "090762b7-1ab4-4f12-8146-a4153677e1b5", "metadata": {}, "outputs": [], @@ -123,10 +293,1863 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "e0c7a216-3e69-4c37-a443-f92cb79665d4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:32:13 (running for 00:00:00.14)
Memory usage on this node: 9.8/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 PENDING)
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Trial name status loc
PPO_mobile-small-ma-v0_2b82d_00000PENDING


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:32:43 (running for 00:00:30.03)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:32:44 (running for 00:00:31.07)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:32:49 (running for 00:00:36.09)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:32:54 (running for 00:00:41.10)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:32:59 (running for 00:00:46.12)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:33:04 (running for 00:00:51.21)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:33:09 (running for 00:00:56.26)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:33:14 (running for 00:01:01.36)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:33:20 (running for 00:01:06.40)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 20000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-33-20\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: -82.58529022334926\n", + " episode_reward_mean: -197.16972293866527\n", + " episode_reward_min: -324.75028304970056\n", + " episodes_this_iter: 40\n", + " episodes_total: 40\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.3763474225997925\n", + " entropy_coeff: 0.0\n", + " kl: 0.01018717885017395\n", + " model: {}\n", + " policy_loss: -0.0061570750549435616\n", + " total_loss: 166.27658081054688\n", + " vf_explained_var: 0.015568939968943596\n", + " vf_loss: 166.2806854248047\n", + " num_agent_steps_sampled: 20000\n", + " num_agent_steps_trained: 20000\n", + " num_steps_sampled: 4000\n", + " num_steps_trained: 4000\n", + " iterations_since_restore: 1\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 71.25283018867924\n", + " ram_util_percent: 90.52264150943395\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: -3.834527690352243\n", + " policy_reward_mean:\n", + " shared_policy: -39.43394458773305\n", + " policy_reward_min:\n", + " shared_policy: -93.07092813608908\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.19319149209879924\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.112792979235175\n", + " mean_inference_ms: 1.576185941338718\n", + " mean_raw_obs_processing_ms: 0.3873457734671788\n", + " time_since_restore: 36.927772760391235\n", + " time_this_iter_s: 36.927772760391235\n", + " time_total_s: 36.927772760391235\n", + " timers:\n", + " learn_throughput: 198.974\n", + " learn_time_ms: 20103.152\n", + " load_throughput: 0.0\n", + " load_time_ms: 0.0\n", + " sample_throughput: 237.752\n", + " sample_time_ms: 16824.248\n", + " update_time_ms: 3.997\n", + " timestamp: 1637753600\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 4000\n", + " training_iteration: 1\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:33:25 (running for 00:01:12.07)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:33:30 (running for 00:01:17.11)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:33:36 (running for 00:01:23.13)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:33:41 (running for 00:01:28.14)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:33:46 (running for 00:01:33.21)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:33:51 (running for 00:01:38.26)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:33:56 (running for 00:01:43.28)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 40000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-33-57\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 26.011023298586956\n", + " episode_reward_mean: -156.0066308067777\n", + " episode_reward_min: -324.75028304970056\n", + " episodes_this_iter: 40\n", + " episodes_total: 80\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.351000428199768\n", + " entropy_coeff: 0.0\n", + " kl: 0.010089628398418427\n", + " model: {}\n", + " policy_loss: -0.005375070031732321\n", + " total_loss: 83.70623779296875\n", + " vf_explained_var: -0.1662183254957199\n", + " vf_loss: 83.70960235595703\n", + " num_agent_steps_sampled: 40000\n", + " num_agent_steps_trained: 40000\n", + " num_steps_sampled: 8000\n", + " num_steps_trained: 8000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 2\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 71.45192307692307\n", + " ram_util_percent: 90.70192307692308\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 12.767251795492593\n", + " policy_reward_mean:\n", + " shared_policy: -31.20132616135554\n", + " policy_reward_min:\n", + " shared_policy: -93.07092813608908\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.19299227772912464\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.246199761827716\n", + " mean_inference_ms: 1.5824175243585503\n", + " mean_raw_obs_processing_ms: 0.4094397045639676\n", + " time_since_restore: 74.06447577476501\n", + " time_this_iter_s: 37.13670301437378\n", + " time_total_s: 74.06447577476501\n", + " timers:\n", + " learn_throughput: 203.794\n", + " learn_time_ms: 19627.669\n", + " load_throughput: 0.0\n", + " load_time_ms: 0.0\n", + " sample_throughput: 145.531\n", + " sample_time_ms: 27485.54\n", + " update_time_ms: 1.999\n", + " timestamp: 1637753637\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 8000\n", + " training_iteration: 2\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:02 (running for 00:01:49.27)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:07 (running for 00:01:54.34)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:13 (running for 00:01:59.41)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:18 (running for 00:02:04.48)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:23 (running for 00:02:09.57)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:28 (running for 00:02:14.65)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:33 (running for 00:02:19.69)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 60000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-34-37\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 26.011023298586956\n", + " episode_reward_mean: -123.1233896979559\n", + " episode_reward_min: -324.75028304970056\n", + " episodes_this_iter: 40\n", + " episodes_total: 120\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.3054527044296265\n", + " entropy_coeff: 0.0\n", + " kl: 0.011907270178198814\n", + " model: {}\n", + " policy_loss: -0.005937955342233181\n", + " total_loss: 80.03804016113281\n", + " vf_explained_var: -0.3122124671936035\n", + " vf_loss: 80.0416030883789\n", + " num_agent_steps_sampled: 60000\n", + " num_agent_steps_trained: 60000\n", + " num_steps_sampled: 12000\n", + " num_steps_trained: 12000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 3\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 75.89818181818183\n", + " ram_util_percent: 90.58909090909088\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 12.767251795492593\n", + " policy_reward_mean:\n", + " shared_policy: -24.62467793959118\n", + " policy_reward_min:\n", + " shared_policy: -94.77753739714078\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.19796336530225817\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.415375718354524\n", + " mean_inference_ms: 1.6054616484128978\n", + " mean_raw_obs_processing_ms: 0.4138493305141289\n", + " time_since_restore: 113.5621988773346\n", + " time_this_iter_s: 39.49772310256958\n", + " time_total_s: 113.5621988773346\n", + " timers:\n", + " learn_throughput: 201.113\n", + " learn_time_ms: 19889.329\n", + " load_throughput: 0.0\n", + " load_time_ms: 0.0\n", + " sample_throughput: 128.645\n", + " sample_time_ms: 31093.221\n", + " update_time_ms: 1.332\n", + " timestamp: 1637753677\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 12000\n", + " training_iteration: 3\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:38 (running for 00:02:24.82)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:43 (running for 00:02:29.84)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:49 (running for 00:02:35.89)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:54 (running for 00:02:40.90)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:34:59 (running for 00:02:45.97)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:35:04 (running for 00:02:51.02)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:35:09 (running for 00:02:56.10)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:35:14 (running for 00:03:01.15)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 80000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-35-14\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 26.011023298586956\n", + " episode_reward_mean: -87.9172788083747\n", + " episode_reward_min: -308.37925235206995\n", + " episodes_this_iter: 40\n", + " episodes_total: 160\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.272882342338562\n", + " entropy_coeff: 0.0\n", + " kl: 0.009863310493528843\n", + " model: {}\n", + " policy_loss: -0.006680862046778202\n", + " total_loss: 65.5313949584961\n", + " vf_explained_var: -0.3546501100063324\n", + " vf_loss: 65.5361099243164\n", + " num_agent_steps_sampled: 80000\n", + " num_agent_steps_trained: 80000\n", + " num_steps_sampled: 16000\n", + " num_steps_trained: 16000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 4\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 71.10377358490568\n", + " ram_util_percent: 89.30377358490568\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 14.752608853599225\n", + " policy_reward_mean:\n", + " shared_policy: -17.58345576167494\n", + " policy_reward_min:\n", + " shared_policy: -94.77753739714078\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.19151596104695695\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.6273575326303185\n", + " mean_inference_ms: 1.6293983809364516\n", + " mean_raw_obs_processing_ms: 0.45260545459694024\n", + " time_since_restore: 150.9406440258026\n", + " time_this_iter_s: 37.37844514846802\n", + " time_total_s: 150.9406440258026\n", + " timers:\n", + " learn_throughput: 206.708\n", + " learn_time_ms: 19350.995\n", + " load_throughput: 0.0\n", + " load_time_ms: 0.0\n", + " sample_throughput: 119.932\n", + " sample_time_ms: 33352.117\n", + " update_time_ms: 1.749\n", + " timestamp: 1637753714\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 16000\n", + " training_iteration: 4\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:35:19 (running for 00:03:06.31)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:35:24 (running for 00:03:11.33)
Memory usage on this node: 10.4/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:35:29 (running for 00:03:16.35)
Memory usage on this node: 10.4/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:35:34 (running for 00:03:21.36)
Memory usage on this node: 10.4/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:35:40 (running for 00:03:26.44)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:35:45 (running for 00:03:31.67)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:35:50 (running for 00:03:36.79)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 100000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-35-54\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 27.92399798089297\n", + " episode_reward_mean: -74.02029784688384\n", + " episode_reward_min: -308.37925235206995\n", + " episodes_this_iter: 40\n", + " episodes_total: 200\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.2363429069519043\n", + " entropy_coeff: 0.0\n", + " kl: 0.011199514381587505\n", + " model: {}\n", + " policy_loss: -0.007194239646196365\n", + " total_loss: 75.12138366699219\n", + " vf_explained_var: -0.16546496748924255\n", + " vf_loss: 75.12632751464844\n", + " num_agent_steps_sampled: 100000\n", + " num_agent_steps_trained: 100000\n", + " num_steps_sampled: 20000\n", + " num_steps_trained: 20000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 5\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 72.9981818181818\n", + " ram_util_percent: 88.1690909090909\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 37.153219674741614\n", + " policy_reward_mean:\n", + " shared_policy: -14.804059569376767\n", + " policy_reward_min:\n", + " shared_policy: -86.23027703452362\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.18999314552745905\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.700066712468343\n", + " mean_inference_ms: 1.6190240744022761\n", + " mean_raw_obs_processing_ms: 0.46591634268105053\n", + " time_since_restore: 190.2505166530609\n", + " time_this_iter_s: 39.3098726272583\n", + " time_total_s: 190.2505166530609\n", + " timers:\n", + " learn_throughput: 199.692\n", + " learn_time_ms: 20030.896\n", + " load_throughput: 0.0\n", + " load_time_ms: 0.0\n", + " sample_throughput: 119.218\n", + " sample_time_ms: 33551.868\n", + " update_time_ms: 1.399\n", + " timestamp: 1637753754\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 20000\n", + " training_iteration: 5\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:35:56 (running for 00:03:42.62)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:02 (running for 00:03:48.64)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:07 (running for 00:03:53.68)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:12 (running for 00:03:58.98)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:17 (running for 00:04:04.20)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:22 (running for 00:04:09.33)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:27 (running for 00:04:14.38)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:33 (running for 00:04:19.54)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:38 (running for 00:04:24.57)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 120000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-36-38\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 32.00511647140962\n", + " episode_reward_mean: -63.05783606921642\n", + " episode_reward_min: -166.7868669415777\n", + " episodes_this_iter: 40\n", + " episodes_total: 240\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.2105662822723389\n", + " entropy_coeff: 0.0\n", + " kl: 0.008601279929280281\n", + " model: {}\n", + " policy_loss: -0.005161717534065247\n", + " total_loss: 66.13565063476562\n", + " vf_explained_var: -0.18167948722839355\n", + " vf_loss: 66.13909149169922\n", + " num_agent_steps_sampled: 120000\n", + " num_agent_steps_trained: 120000\n", + " num_steps_sampled: 24000\n", + " num_steps_trained: 24000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 6\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 80.84666666666666\n", + " ram_util_percent: 88.13499999999999\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 37.153219674741614\n", + " policy_reward_mean:\n", + " shared_policy: -12.611567213843283\n", + " policy_reward_min:\n", + " shared_policy: -77.32211039258436\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.18781763969726537\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.793050669285408\n", + " mean_inference_ms: 1.6410687220957636\n", + " mean_raw_obs_processing_ms: 0.4787712232489312\n", + " time_since_restore: 234.50923705101013\n", + " time_this_iter_s: 44.25872039794922\n", + " time_total_s: 234.50923705101013\n", + " timers:\n", + " learn_throughput: 197.207\n", + " learn_time_ms: 20283.272\n", + " load_throughput: 0.0\n", + " load_time_ms: 0.0\n", + " sample_throughput: 112.522\n", + " sample_time_ms: 35548.465\n", + " update_time_ms: 1.832\n", + " timestamp: 1637753798\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 24000\n", + " training_iteration: 6\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:43 (running for 00:04:29.98)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:48 (running for 00:04:35.01)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:53 (running for 00:04:40.05)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:36:58 (running for 00:04:45.08)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:37:03 (running for 00:04:50.16)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:37:08 (running for 00:04:55.22)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 140000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-37-12\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 37.517350407532646\n", + " episode_reward_mean: -56.8993966162799\n", + " episode_reward_min: -159.39671116929244\n", + " episodes_this_iter: 40\n", + " episodes_total: 280\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.1945053339004517\n", + " entropy_coeff: 0.0\n", + " kl: 0.008428691886365414\n", + " model: {}\n", + " policy_loss: -0.005386861972510815\n", + " total_loss: 67.3160629272461\n", + " vf_explained_var: -0.18060088157653809\n", + " vf_loss: 67.31977081298828\n", + " num_agent_steps_sampled: 140000\n", + " num_agent_steps_trained: 140000\n", + " num_steps_sampled: 28000\n", + " num_steps_trained: 28000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 7\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 66.20416666666667\n", + " ram_util_percent: 88.14791666666667\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 32.550057156570304\n", + " policy_reward_mean:\n", + " shared_policy: -11.37987932325598\n", + " policy_reward_min:\n", + " shared_policy: -79.64947249900509\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.18606896569713288\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.828355809131563\n", + " mean_inference_ms: 1.661827741816298\n", + " mean_raw_obs_processing_ms: 0.483841240195652\n", + " time_since_restore: 268.3320059776306\n", + " time_this_iter_s: 33.82276892662048\n", + " time_total_s: 268.3320059776306\n", + " timers:\n", + " learn_throughput: 201.684\n", + " learn_time_ms: 19832.969\n", + " load_throughput: 0.0\n", + " load_time_ms: 0.0\n", + " sample_throughput: 111.287\n", + " sample_time_ms: 35943.133\n", + " update_time_ms: 1.855\n", + " timestamp: 1637753832\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 28000\n", + " training_iteration: 7\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:37:14 (running for 00:05:00.86)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:37:19 (running for 00:05:05.90)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:37:24 (running for 00:05:10.97)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:37:29 (running for 00:05:16.02)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:37:34 (running for 00:05:21.09)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:37:39 (running for 00:05:26.15)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:37:44 (running for 00:05:31.29)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:37:49 (running for 00:05:36.36)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 160000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-37-51\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 37.517350407532646\n", + " episode_reward_mean: -44.28550081510158\n", + " episode_reward_min: -307.40419640021577\n", + " episodes_this_iter: 40\n", + " episodes_total: 320\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.1653368473052979\n", + " entropy_coeff: 0.0\n", + " kl: 0.006505147088319063\n", + " model: {}\n", + " policy_loss: -0.004781945608556271\n", + " total_loss: 54.027320861816406\n", + " vf_explained_var: -0.2776322066783905\n", + " vf_loss: 54.03080368041992\n", + " num_agent_steps_sampled: 160000\n", + " num_agent_steps_trained: 160000\n", + " num_steps_sampled: 32000\n", + " num_steps_trained: 32000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 8\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 76.41666666666667\n", + " ram_util_percent: 88.45370370370368\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 30.326552942312613\n", + " policy_reward_mean:\n", + " shared_policy: -8.857100163020313\n", + " policy_reward_min:\n", + " shared_policy: -89.39424177690312\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.18519630440233528\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.812173943664939\n", + " mean_inference_ms: 1.6537263106465048\n", + " mean_raw_obs_processing_ms: 0.4824498669355951\n", + " time_since_restore: 306.9676413536072\n", + " time_this_iter_s: 38.63563537597656\n", + " time_total_s: 306.9676413536072\n", + " timers:\n", + " learn_throughput: 199.034\n", + " learn_time_ms: 20097.052\n", + " load_throughput: 0.0\n", + " load_time_ms: 0.0\n", + " sample_throughput: 112.102\n", + " sample_time_ms: 35681.645\n", + " update_time_ms: 2.623\n", + " timestamp: 1637753871\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 32000\n", + " training_iteration: 8\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:37:55 (running for 00:05:41.65)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:00 (running for 00:05:46.68)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:05 (running for 00:05:51.74)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:10 (running for 00:05:56.78)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:15 (running for 00:06:01.87)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:20 (running for 00:06:06.96)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:25 (running for 00:06:12.04)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 180000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-38-30\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 28.014085671833037\n", + " episode_reward_mean: -36.26332603764277\n", + " episode_reward_min: -307.40419640021577\n", + " episodes_this_iter: 40\n", + " episodes_total: 360\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.1505690813064575\n", + " entropy_coeff: 0.0\n", + " kl: 0.00772960064932704\n", + " model: {}\n", + " policy_loss: -0.005320595111697912\n", + " total_loss: 52.63880157470703\n", + " vf_explained_var: -0.18861554563045502\n", + " vf_loss: 52.642581939697266\n", + " num_agent_steps_sampled: 180000\n", + " num_agent_steps_trained: 180000\n", + " num_steps_sampled: 36000\n", + " num_steps_trained: 36000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 9\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 74.44363636363634\n", + " ram_util_percent: 90.69272727272724\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 27.129546212046442\n", + " policy_reward_mean:\n", + " shared_policy: -7.252665207528553\n", + " policy_reward_min:\n", + " shared_policy: -89.39424177690312\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.18029056039080502\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.759358082997057\n", + " mean_inference_ms: 1.630454783909031\n", + " mean_raw_obs_processing_ms: 0.4768866479305913\n", + " time_since_restore: 345.8968811035156\n", + " time_this_iter_s: 38.92923974990845\n", + " time_total_s: 345.8968811035156\n", + " timers:\n", + " learn_throughput: 197.844\n", + " learn_time_ms: 20217.977\n", + " load_throughput: 0.0\n", + " load_time_ms: 0.0\n", + " sample_throughput: 110.665\n", + " sample_time_ms: 36145.207\n", + " update_time_ms: 2.332\n", + " timestamp: 1637753910\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 36000\n", + " training_iteration: 9\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:31 (running for 00:06:17.60)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:36 (running for 00:06:22.66)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:41 (running for 00:06:27.71)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:46 (running for 00:06:32.77)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:51 (running for 00:06:37.81)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:38:56 (running for 00:06:42.89)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:01 (running for 00:06:47.94)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 200000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-39-05\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 33.72066006899857\n", + " episode_reward_mean: -32.099061836934574\n", + " episode_reward_min: -301.0544155509032\n", + " episodes_this_iter: 40\n", + " episodes_total: 400\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.1299378871917725\n", + " entropy_coeff: 0.0\n", + " kl: 0.006103357300162315\n", + " model: {}\n", + " policy_loss: -0.003299871226772666\n", + " total_loss: 57.985862731933594\n", + " vf_explained_var: -0.1961873322725296\n", + " vf_loss: 57.98794174194336\n", + " num_agent_steps_sampled: 200000\n", + " num_agent_steps_trained: 200000\n", + " num_steps_sampled: 40000\n", + " num_steps_trained: 40000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 10\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 68.914\n", + " ram_util_percent: 91.88799999999999\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 42.1388282746424\n", + " policy_reward_mean:\n", + " shared_policy: -6.419812367386916\n", + " policy_reward_min:\n", + " shared_policy: -89.39424177690312\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.17717857106820847\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.731957824979423\n", + " mean_inference_ms: 1.6149166619945021\n", + " mean_raw_obs_processing_ms: 0.4761121303364078\n", + " time_since_restore: 381.2746181488037\n", + " time_this_iter_s: 35.377737045288086\n", + " time_total_s: 381.2746181488037\n", + " timers:\n", + " learn_throughput: 200.092\n", + " learn_time_ms: 19990.789\n", + " load_throughput: 0.0\n", + " load_time_ms: 0.0\n", + " sample_throughput: 109.893\n", + " sample_time_ms: 36398.999\n", + " update_time_ms: 2.499\n", + " timestamp: 1637753945\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 40000\n", + " training_iteration: 10\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:06 (running for 00:06:53.05)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:11 (running for 00:06:58.08)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:16 (running for 00:07:03.14)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:21 (running for 00:07:08.20)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:26 (running for 00:07:13.25)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:31 (running for 00:07:18.26)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:37 (running for 00:07:23.41)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:42 (running for 00:07:28.49)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 220000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-39-42\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 76.60983866128029\n", + " episode_reward_mean: -25.00437293296074\n", + " episode_reward_min: -156.68041022451237\n", + " episodes_this_iter: 40\n", + " episodes_total: 440\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.111107349395752\n", + " entropy_coeff: 0.0\n", + " kl: 0.008636608719825745\n", + " model: {}\n", + " policy_loss: -0.005253782961517572\n", + " total_loss: 63.161590576171875\n", + " vf_explained_var: -0.20226168632507324\n", + " vf_loss: 63.165122985839844\n", + " num_agent_steps_sampled: 220000\n", + " num_agent_steps_trained: 220000\n", + " num_steps_sampled: 44000\n", + " num_steps_trained: 44000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 11\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 71.51346153846154\n", + " ram_util_percent: 91.61153846153846\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 42.1388282746424\n", + " policy_reward_mean:\n", + " shared_policy: -5.000874586592149\n", + " policy_reward_min:\n", + " shared_policy: -100.0\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.1742830208595818\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.721386447912501\n", + " mean_inference_ms: 1.6068779376316087\n", + " mean_raw_obs_processing_ms: 0.4762444523177788\n", + " time_since_restore: 418.4226624965668\n", + " time_this_iter_s: 37.14804434776306\n", + " time_total_s: 418.4226624965668\n", + " timers:\n", + " learn_throughput: 201.059\n", + " learn_time_ms: 19894.672\n", + " load_throughput: 0.0\n", + " load_time_ms: 0.0\n", + " sample_throughput: 104.388\n", + " sample_time_ms: 38318.573\n", + " update_time_ms: 2.499\n", + " timestamp: 1637753982\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 44000\n", + " training_iteration: 11\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:47 (running for 00:07:33.95)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:52 (running for 00:07:39.04)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:39:57 (running for 00:07:44.15)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:40:02 (running for 00:07:49.21)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:40:07 (running for 00:07:54.27)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:40:12 (running for 00:07:59.33)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:40:18 (running for 00:08:04.52)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:40:23 (running for 00:08:09.59)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 240000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-40-26\n", + " done: false\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 76.60983866128029\n", + " episode_reward_mean: -25.139173555377642\n", + " episode_reward_min: -158.71299775589793\n", + " episodes_this_iter: 40\n", + " episodes_total: 480\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.100955843925476\n", + " entropy_coeff: 0.0\n", + " kl: 0.005644019227474928\n", + " model: {}\n", + " policy_loss: -0.0038178933318704367\n", + " total_loss: 64.17167663574219\n", + " vf_explained_var: -0.1336476355791092\n", + " vf_loss: 64.17436218261719\n", + " num_agent_steps_sampled: 240000\n", + " num_agent_steps_trained: 240000\n", + " num_steps_sampled: 48000\n", + " num_steps_trained: 48000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 12\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 79.65000000000002\n", + " ram_util_percent: 90.49666666666663\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 38.073590681104506\n", + " policy_reward_mean:\n", + " shared_policy: -5.027834711075529\n", + " policy_reward_min:\n", + " shared_policy: -100.0\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.17415585588546253\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 6.786551093850811\n", + " mean_inference_ms: 1.6200170457173022\n", + " mean_raw_obs_processing_ms: 0.47818855945121946\n", + " time_since_restore: 461.5246617794037\n", + " time_this_iter_s: 43.101999282836914\n", + " time_total_s: 461.5246617794037\n", + " timers:\n", + " learn_throughput: 200.668\n", + " learn_time_ms: 19933.453\n", + " load_throughput: 39794155.598\n", + " load_time_ms: 0.101\n", + " sample_throughput: 103.134\n", + " sample_time_ms: 38784.546\n", + " update_time_ms: 2.799\n", + " timestamp: 1637754026\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 48000\n", + " training_iteration: 12\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:40:29 (running for 00:08:15.51)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:40:34 (running for 00:08:20.55)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:40:39 (running for 00:08:25.62)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:40:44 (running for 00:08:30.74)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:40:50 (running for 00:08:36.40)
Memory usage on this node: 11.0/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:40:55 (running for 00:08:41.53)
Memory usage on this node: 11.2/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:41:00 (running for 00:08:46.72)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:41:05 (running for 00:08:51.87)
Memory usage on this node: 11.2/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:41:10 (running for 00:08:56.95)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:41:15 (running for 00:09:02.01)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:41:20 (running for 00:09:07.10)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result for PPO_mobile-small-ma-v0_2b82d_00000:\n", + " agent_timesteps_total: 260000\n", + " custom_metrics: {}\n", + " date: 2021-11-24_12-41-23\n", + " done: true\n", + " episode_len_mean: 100.0\n", + " episode_media: {}\n", + " episode_reward_max: 76.60983866128029\n", + " episode_reward_mean: -35.461493594127866\n", + " episode_reward_min: -158.71299775589793\n", + " episodes_this_iter: 40\n", + " episodes_total: 520\n", + " experiment_id: 4e540bfa436c40ce885eae70b7b6ac0e\n", + " hostname: nb-stschn\n", + " info:\n", + " learner:\n", + " shared_policy:\n", + " custom_metrics: {}\n", + " learner_stats:\n", + " cur_kl_coeff: 0.20000000298023224\n", + " cur_lr: 4.999999873689376e-05\n", + " entropy: 1.0943677425384521\n", + " entropy_coeff: 0.0\n", + " kl: 0.006255448330193758\n", + " model: {}\n", + " policy_loss: -0.0035820447374135256\n", + " total_loss: 86.11984252929688\n", + " vf_explained_var: -0.22441518306732178\n", + " vf_loss: 86.12218475341797\n", + " num_agent_steps_sampled: 260000\n", + " num_agent_steps_trained: 260000\n", + " num_steps_sampled: 52000\n", + " num_steps_trained: 52000\n", + " num_steps_trained_this_iter: 0\n", + " iterations_since_restore: 13\n", + " node_ip: 127.0.0.1\n", + " num_healthy_workers: 2\n", + " off_policy_estimator: {}\n", + " perf:\n", + " cpu_util_percent: 88.65921052631579\n", + " ram_util_percent: 93.24473684210528\n", + " pid: 7956\n", + " policy_reward_max:\n", + " shared_policy: 26.976727403439014\n", + " policy_reward_mean:\n", + " shared_policy: -7.092298718825573\n", + " policy_reward_min:\n", + " shared_policy: -100.0\n", + " sampler_perf:\n", + " mean_action_processing_ms: 0.17699619737379052\n", + " mean_env_render_ms: 0.0\n", + " mean_env_wait_ms: 7.028548368627724\n", + " mean_inference_ms: 1.6635352514718473\n", + " mean_raw_obs_processing_ms: 0.4924915758962375\n", + " time_since_restore: 518.4748079776764\n", + " time_this_iter_s: 56.950146198272705\n", + " time_total_s: 518.4748079776764\n", + " timers:\n", + " learn_throughput: 197.005\n", + " learn_time_ms: 20304.029\n", + " load_throughput: 39794155.598\n", + " load_time_ms: 0.101\n", + " sample_throughput: 99.508\n", + " sample_time_ms: 40197.757\n", + " update_time_ms: 3.198\n", + " timestamp: 1637754083\n", + " timesteps_since_restore: 0\n", + " timesteps_this_iter: 0\n", + " timesteps_total: 52000\n", + " training_iteration: 13\n", + " trial_id: 2b82d_00000\n", + " \n" + ] + }, + { + "data": { + "text/plain": "", + "text/html": "== Status ==
Current time: 2021-11-24 12:41:23 (running for 00:09:09.61)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 TERMINATED)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000TERMINATED127.0.0.1:7956 13 518.47552000-35.4615 76.6098 -158.713 100


" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-11-24 12:41:23,617\tINFO tune.py:630 -- Total run time: 550.02 seconds (549.56 seconds for the tuning loop).\n" + ] + } + ], "source": [ "analysis = ray.tune.run(ppo.PPOTrainer, config=config, local_dir=save_dir, stop=stop, checkpoint_at_end=True)" ] @@ -141,10 +2164,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "f4069ab5-1ca5-4965-9e70-92dd70080105", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "Launching TensorBoard..." + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "", + "text/html": "\n \n \n " + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%tensorboard --logdir ray_results" ] @@ -159,10 +2198,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "bfc933bc-4983-49ba-a7f4-8032886bd045", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-11-24 12:41:23,766\tINFO ppo.py:166 -- In multi-agent mode, policies will be optimized sequentially by the multi-GPU optimizer. Consider setting simple_optimizer=True if this doesn't work for you.\n", + "2021-11-24 12:41:23,766\tINFO trainer.py:770 -- Current log_level is WARN. For more information, set 'log_level': 'INFO' / 'DEBUG' or use the -v and -vv flags.\n" + ] + }, + { + "ename": "RayActorError", + "evalue": "The actor died because of an error raised in its creation task, \u001B[36mray::RolloutWorker.__init__()\u001B[39m (pid=18304, ip=127.0.0.1)\n File \"python\\ray\\_raylet.pyx\", line 565, in ray._raylet.execute_task\n File \"python\\ray\\_raylet.pyx\", line 569, in ray._raylet.execute_task\n File \"python\\ray\\_raylet.pyx\", line 519, in ray._raylet.execute_task.function_executor\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\_private\\function_manager.py\", line 576, in actor_method_executor\n return method(__ray_actor, *args, **kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 584, in __init__\n self._build_policy_map(\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 1384, in _build_policy_map\n self.policy_map.create_policy(name, orig_cls, obs_space, act_space,\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\policy\\policy_map.py\", line 123, in create_policy\n sess = self.session_creator()\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\worker_set.py\", line 323, in session_creator\n return tf1.Session(\nAttributeError: 'NoneType' object has no attribute 'Session'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mRayActorError\u001B[0m Traceback (most recent call last)", + "\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_1344/335420695.py\u001B[0m in \u001B[0;36m\u001B[1;34m\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[0mcheckpoint\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0manalysis\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_last_checkpoint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 2\u001B[1;33m \u001B[0mmodel\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mppo\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mPPOTrainer\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mconfig\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mconfig\u001B[0m\u001B[1;33m,\u001B[0m 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"\u001B[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\agents\\trainer.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, config, env, logger_creator)\u001B[0m\n\u001B[0;32m 621\u001B[0m \u001B[0mlogger_creator\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdefault_logger_creator\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 622\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 623\u001B[1;33m \u001B[0msuper\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__init__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mconfig\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlogger_creator\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 624\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 625\u001B[0m \u001B[1;33m@\u001B[0m\u001B[0mclassmethod\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n", + "\u001B[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\tune\\trainable.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, config, logger_creator)\u001B[0m\n\u001B[0;32m 105\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 106\u001B[0m \u001B[0mstart_time\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtime\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtime\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 107\u001B[1;33m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msetup\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcopy\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdeepcopy\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mconfig\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 108\u001B[0m \u001B[0msetup_time\u001B[0m \u001B[1;33m=\u001B[0m 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setup.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n", + "\u001B[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\agents\\trainer_template.py\u001B[0m in \u001B[0;36m_init\u001B[1;34m(self, config, env_creator)\u001B[0m\n\u001B[0;32m 169\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 170\u001B[0m \u001B[1;31m# Creating all workers (excluding evaluation workers).\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 171\u001B[1;33m self.workers = self._make_workers(\n\u001B[0m\u001B[0;32m 172\u001B[0m \u001B[0menv_creator\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0menv_creator\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 173\u001B[0m \u001B[0mvalidate_env\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mvalidate_env\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n", + "\u001B[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\agents\\trainer.py\u001B[0m in \u001B[0;36m_make_workers\u001B[1;34m(self, env_creator, validate_env, policy_class, config, num_workers)\u001B[0m\n\u001B[0;32m 856\u001B[0m \u001B[0mWorkerSet\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mThe\u001B[0m \u001B[0mcreated\u001B[0m \u001B[0mWorkerSet\u001B[0m\u001B[1;33m.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 857\u001B[0m \"\"\"\n\u001B[1;32m--> 858\u001B[1;33m return WorkerSet(\n\u001B[0m\u001B[0;32m 859\u001B[0m \u001B[0menv_creator\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0menv_creator\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 860\u001B[0m \u001B[0mvalidate_env\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mvalidate_env\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n", + "\u001B[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\worker_set.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, env_creator, validate_env, policy_class, trainer_config, num_workers, logdir, _setup)\u001B[0m\n\u001B[0;32m 85\u001B[0m (not trainer_config.get(\"observation_space\") or\n\u001B[0;32m 86\u001B[0m not trainer_config.get(\"action_space\")):\n\u001B[1;32m---> 87\u001B[1;33m remote_spaces = ray.get(self.remote_workers(\n\u001B[0m\u001B[0;32m 88\u001B[0m \u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mforeach_policy\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mremote\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 89\u001B[0m lambda p, pid: (pid, p.observation_space, p.action_space)))\n", + "\u001B[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\_private\\client_mode_hook.py\u001B[0m in \u001B[0;36mwrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m 103\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mfunc\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__name__\u001B[0m \u001B[1;33m!=\u001B[0m \u001B[1;34m\"init\"\u001B[0m \u001B[1;32mor\u001B[0m \u001B[0mis_client_mode_enabled_by_default\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 104\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mgetattr\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mray\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mfunc\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__name__\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 105\u001B[1;33m 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\"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\_private\\function_manager.py\", line 576, in actor_method_executor\n return method(__ray_actor, *args, **kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 584, in __init__\n self._build_policy_map(\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 1384, in _build_policy_map\n self.policy_map.create_policy(name, orig_cls, obs_space, act_space,\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\policy\\policy_map.py\", line 123, in create_policy\n sess = self.session_creator()\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\worker_set.py\", line 323, in session_creator\n return tf1.Session(\nAttributeError: 'NoneType' object has no attribute 'Session'" + ] + } + ], "source": [ "checkpoint = analysis.get_last_checkpoint()\n", "model = ppo.PPOTrainer(config=config, env='mobile-small-ma-v0')\n", @@ -174,7 +2243,8 @@ "id": "62cff06a-46bb-4634-98fb-9386ef30dca2", "metadata": {}, "source": [ - "Mobile-Env provides a render() function to visualize the simulation. In Google Colaboratory the better-looking 'human' mode is unavailable (only available locally). Still, we can visualize the final policy as RGB images:" + "Mobile-Env provides a render() function to visualize the simulation.\n", + "In Google Colaboratory the better-looking 'human' mode is unavailable (only available locally). Still, we can visualize the final policy as RGB images:" ] }, { @@ -262,7 +2332,7 @@ "\n", "# train PPO agent on environment\n", "model = PPO(MlpPolicy, env, verbose=1, tensorboard_log='ppo_central_tensorboard')\n", - "model.learn(total_timesteps=500000)" + "model.learn(total_timesteps=50000)" ] }, { @@ -332,9 +2402,9 @@ ], "metadata": { "kernelspec": { - "display_name": "mobile", + "name": "python3", "language": "python", - "name": "mobile" + "display_name": "Python 3 (ipykernel)" }, "language_info": { "codemirror_mode": { @@ -351,4 +2421,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file From 47a98ce5f228fd83743f0a75114210166e0c9296 Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Wed, 24 Nov 2021 17:11:58 +0100 Subject: [PATCH 05/14] move default bs and ue config to base scenario --- mobile_env/core/arrival.py | 2 +- mobile_env/core/base.py | 16 ++++++++++++++++ mobile_env/scenarios/large.py | 18 ------------------ mobile_env/scenarios/medium.py | 18 ------------------ mobile_env/scenarios/small.py | 23 ++--------------------- 5 files changed, 19 insertions(+), 58 deletions(-) diff --git a/mobile_env/core/arrival.py b/mobile_env/core/arrival.py index f059675..e6a0493 100644 --- a/mobile_env/core/arrival.py +++ b/mobile_env/core/arrival.py @@ -31,7 +31,7 @@ def departure(self, ue: UserEquipment) -> int: class NoDeparture(Arrival): - """Alle UEs immediately request service and do not depart thereafter.""" + """All UEs immediately request service and do not depart thereafter.""" def __init__(self, **kwargs): super().__init__(**kwargs) diff --git a/mobile_env/core/base.py b/mobile_env/core/base.py index 1dcb896..1ef312b 100644 --- a/mobile_env/core/base.py +++ b/mobile_env/core/base.py @@ -106,6 +106,22 @@ def default_config(cls): "movement": RandomWaypointMovement, "utility": BoundedLogUtility, "handler": MComCentralHandler, + + # default cell config + "bs": { + "bw": 9e6, + "freq": 2500, + "tx": 30, + "height": 50 + }, + + # default UE config + "ue": { + "velocity": 1.5, + "snr_tr": 2e-8, + "noise": 1e-9, + "height": 1.5, + }, } # set up default configuration parameters for arrival pattern, ... diff --git a/mobile_env/scenarios/large.py b/mobile_env/scenarios/large.py index 0728c1f..75ef291 100644 --- a/mobile_env/scenarios/large.py +++ b/mobile_env/scenarios/large.py @@ -36,21 +36,3 @@ def __init__(self, config={}): ] super().__init__(stations, ues, config) - - @classmethod - def default_config(cls): - config = super().default_config() - config.update({ - "bs": {"bw": 9e6, "freq": 2500, "tx": 30, "height": 50} - }) - config.update( - { - "ue": { - "velocity": 1.5, - "snr_tr": 2e-8, - "noise": 1e-9, - "height": 1.5, - } - } - ) - return config diff --git a/mobile_env/scenarios/medium.py b/mobile_env/scenarios/medium.py index 639aa6f..f0f13e0 100644 --- a/mobile_env/scenarios/medium.py +++ b/mobile_env/scenarios/medium.py @@ -23,21 +23,3 @@ def __init__(self, config={}): ] super().__init__(stations, ues, config) - - @classmethod - def default_config(cls): - config = super().default_config() - config.update({ - "bs": {"bw": 9e6, "freq": 2500, "tx": 30, "height": 50} - }) - config.update( - { - "ue": { - "velocity": 1.5, - "snr_tr": 2e-8, - "noise": 1e-9, - "height": 1.5, - } - } - ) - return config diff --git a/mobile_env/scenarios/small.py b/mobile_env/scenarios/small.py index 19a93f9..3030d3c 100644 --- a/mobile_env/scenarios/small.py +++ b/mobile_env/scenarios/small.py @@ -7,11 +7,10 @@ def __init__(self, config={}): # set unspecified parameters to default configuration config = {**self.default_config(), **config} - stations = [(110, 130), (65, 80), (120, 30)] - stations = [(x, y) for x, y in stations] + station_pos = [(110, 130), (65, 80), (120, 30)] stations = [ BaseStation(bs_id, pos, **config["bs"]) - for bs_id, pos in enumerate(stations) + for bs_id, pos in enumerate(station_pos) ] num_ues = 5 ues = [ @@ -20,21 +19,3 @@ def __init__(self, config={}): ] super().__init__(stations, ues, config) - - @classmethod - def default_config(cls): - config = super().default_config() - config.update({ - "bs": {"bw": 9e6, "freq": 2500, "tx": 30, "height": 50} - }) - config.update( - { - "ue": { - "velocity": 1.5, - "snr_tr": 2e-8, - "noise": 1e-9, - "height": 1.5, - } - } - ) - return config From 944662930aa22d06aa1bbf575d90018687f2ffaf Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Wed, 24 Nov 2021 17:12:13 +0100 Subject: [PATCH 06/14] start demo notebook --- examples/demo.ipynb | 493 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 493 insertions(+) create mode 100644 examples/demo.ipynb diff --git a/examples/demo.ipynb b/examples/demo.ipynb new file mode 100644 index 0000000..29a24ba --- /dev/null +++ b/examples/demo.ipynb @@ -0,0 +1,493 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Demonstrating `mobile-env`\n", + "\n", + "`mobile-env` is a simple and open environment for training, testing, and evaluating autonomous coordination\n", + "approaches for mobile networks.\n", + "\n", + "* `mobile-env` is written in pure Python and can be installed easily via PyPI\n", + "* It allows simulating various scenarios with moving users in mobile networks\n", + "* `mobile-env` implements the OpenAI Gym interface such that it can be used with all common frameworks for reinforcement learning\n", + "* It supports both centralized, single-agent control and multi-agent control\n", + "* It can be configured easily (e.g., adjusting number and movement of users, properties of cells, etc.)\n", + "* It is also easy to extend `mobile-env`, e.g., implementing different observations, actions, or reward\n", + "\n", + "As such `mobile-env` is a simple platform to evaluate and compare different coordination approaches in a meaningful way.\n", + "\n", + "---\n", + "\n", + "This demonstration consists of the following steps:\n", + "\n", + "1. Installation and usage of `mobile-env` with a dummy actions\n", + "2. Configuration of `mobile-env` and adjustment of the observation space\n", + "3. Training a reinforcement learning agent with [`stable-baselines3`](https://github.com/DLR-RM/stable-baselines3)\n", + "4. Training multi-agent PPO with [Ray RLlib](https://docs.ray.io/en/latest/rllib.html)\n", + "\n", + "\n", + "## Step 1: Install and Use `mobile-env` With Dummy Actions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: mobile-env in c:\\users\\stefan\\git-repos\\work\\mobile-env (2.0.0.dev0)\n", + "Requirement already satisfied: gym>=0.19.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (0.19.0)\n", + "Requirement already satisfied: matplotlib==3.5.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (3.5.0)\n", + "Requirement already satisfied: numpy==1.21.4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (1.21.4)\n", + "Requirement already satisfied: pygame==2.1.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (2.1.0)\n", + "Requirement already satisfied: shapely==1.8.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (1.8.0)\n", + "Requirement already satisfied: svgpath2mpl==1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (1.0.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.4.7)\n", + "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (8.4.0)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (0.10.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in 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(1.6.0)\n", + "Requirement already satisfied: six in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from cycler>=0.10->matplotlib==3.5.0->mobile-env) (1.15.0)\n", + "Requirement already satisfied: setuptools in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (46.1.3)\n", + "Requirement already satisfied: tomli>=1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (1.2.2)\n" + ] + } + ], + "source": [ + "# installation via PyPI\n", + "import time\n", + "!pip install mobile-env" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Small environment with 5 users and 3 cells.\n" + ] + } + ], + "source": [ + "import gym\n", + "import matplotlib.pyplot as plt\n", + "import mobile_env\n", + "\n", + "\n", + "# create a small mobile environment for a single, centralized control agent\n", + "env = gym.make(\"mobile-small-central-v0\")\n", + "\n", + "print(f\"\\nSmall environment with {env.NUM_USERS} users and {env.NUM_STATIONS} cells.\")" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Here, we consider a small scenario with 5 users and 3 cells.\n", + "As the users move around, the goal is to connect the users to suitable cells,\n", + "ensuring that all users have a good Quality of Experience (QoE).\n", + "\n", + "Connecting to more cells increases data rate for a user but also increases competition for resources\n", + "and may decrease rates of other users.\n", + "Therefore, a good coordination policy needs to balance this trade-off, depending on available resources and\n", + "users' positions.\n", + "\n", + "To measure QoE, the `mobile-env` defaults to a logarithmic utility function of the users' data rate,\n", + "where -20 indicates bad QoE and +20 indicates good QoE. This can be easily configured and changed.\n", + "\n", + "To get started, we will use random dummy actions." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from IPython import display\n", + "\n", + "# TODO: why do UEs move differently everytime?\n", + "# isn't there a default seed 0 that should lead to reproducible behavior?\n", + "\n", + "# run the simulation for 10 time steps\n", + "done = False\n", + "obs = env.reset()\n", + "for _ in range(10):\n", + " # here, use random dummy actions by sampling from the action space\n", + " dummy_action = env.action_space.sample()\n", + " obs, reward, done, info = env.step(dummy_action)\n", + " # render the environment\n", + " plt.imshow(env.render(mode='rgb_array'))\n", + " display.display(plt.gcf())\n", + " display.clear_output(wait=True)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The rendered environment shows the three cells as cell towers with circles indicating their range.\n", + "The five moving users are shown as small circles, where the number indicates the user ID and\n", + "the color represents the user's current QoE (red = bad, yellow = ok, green = good).\n", + "\n", + "A line between a user and a cell indicate that the user is connected to the cell.\n", + "Again, the color indicates the QoE that's achieved via the connection.\n", + "Note that users can connect to multiple cells simultaneously using coordinated multipoint (CoMP).\n", + "\n", + "Before, training a reinforcement learning agent to properly control cell selection,\n", + "we will look at some configuration options of `mobile-env`.\n", + "\n", + "\n", + "\n", + "## Step 2: Configure `mobile-env`\n", + "\n", + "### Predefined Scenarios\n", + "\n", + "We can choose between one of the [three predefined scenarios](https://mobile-env.readthedocs.io/en/latest/components.html#scenarios):\n", + "Small, medium, and large\n", + "\n", + "By default, these are available for either a single, centralized agent (e.g., \"mobile-small-central-v0\")\n", + "or multiple agents (e.g., \"mobile-small-ma-v0\"), which affects the observations, actions, and reward." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "{'width': 200,\n 'height': 200,\n 'EP_MAX_TIME': 100,\n 'seed': 0,\n 'reset_rng_episode': False,\n 'arrival': mobile_env.core.arrival.NoDeparture,\n 'channel': mobile_env.core.channels.OkumuraHata,\n 'scheduler': mobile_env.core.schedules.ResourceFair,\n 'movement': mobile_env.core.movement.RandomWaypointMovement,\n 'utility': mobile_env.core.utilities.BoundedLogUtility,\n 'handler': mobile_env.handlers.central.MComCentralHandler,\n 'bs': {'bw': 9000000.0, 'freq': 2500, 'tx': 30, 'height': 50},\n 'ue': {'velocity': 1.5, 'snr_tr': 2e-08, 'noise': 1e-09, 'height': 1.5},\n 'arrival_params': {'ep_time': 100, 'reset_rng_episode': False},\n 'channel_params': {},\n 'scheduler_params': {},\n 'movement_params': {'width': 200, 'height': 200, 'reset_rng_episode': False},\n 'utility_params': {'lower': -20, 'upper': 20, 'coeffs': (10, 0, 10)}}" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# predefined small scenarios\n", + "from mobile_env.scenarios.small import MComSmall\n", + "\n", + "# easy access to the default configuration\n", + "MComSmall.default_config()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# render the small environment (same as above)\n", + "env = gym.make(\"mobile-small-central-v0\")\n", + "env.reset()\n", + "# random first step\n", + "env.step(env.action_space.sample())\n", + "plt.imshow(env.render(mode='rgb_array'))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# create and render the medium environment\n", + "env = gym.make(\"mobile-medium-central-v0\")\n", + "env.reset()\n", + "# random first step\n", + "env.step(env.action_space.sample())\n", + "plt.imshow(env.render(mode='rgb_array'))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# create and render the large environment\n", + "env = gym.make(\"mobile-large-central-v0\")\n", + "env.reset()\n", + "# random first step\n", + "env.step(env.action_space.sample())\n", + "plt.imshow(env.render(mode='rgb_array'))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Custom Scenario\n", + "\n", + "It is also easy to define a custom scenario by subclassing `mobile-env`'s `MComCore` base class.\n", + "\n", + "Here, we create a custom scenario with two cells and three users,\n", + "where one user is stationary and the other two users move quickly with 5 m/s.\n", + "\n", + "We also configure the environment to simulate each episode with identical user positions and movement.\n", + "By default, users appear and move randomly in each episode.\n", + "We also set the episode length to 10 (instead of default 100 steps)." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'width': 200, 'height': 200, 'EP_MAX_TIME': 10, 'seed': 42, 'reset_rng_episode': True, 'arrival': , 'channel': , 'scheduler': , 'movement': , 'utility': , 'handler': , 'bs': {'bw': 9000000.0, 'freq': 2500, 'tx': 30, 'height': 50}, 'ue': {'velocity': 5, 'snr_tr': 2e-08, 'noise': 1e-09, 'height': 1.5}, 'arrival_params': {'ep_time': 100, 'reset_rng_episode': False}, 'channel_params': {}, 'scheduler_params': {}, 'movement_params': {'width': 200, 'height': 200, 'reset_rng_episode': False}, 'utility_params': {'lower': -20, 'upper': 20, 'coeffs': (10, 0, 10)}}\n" + ] + }, + { + "ename": "KeyError", + "evalue": "0", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_8292/2394862195.py\u001B[0m in \u001B[0;36m\u001B[1;34m\u001B[0m\n\u001B[0;32m 51\u001B[0m \u001B[0menv\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mreset\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 52\u001B[0m \u001B[1;31m# random first step\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 53\u001B[1;33m \u001B[0menv\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mstep\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0menv\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0maction_space\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msample\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 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], + "source": [ + "from mobile_env.core.base import MComCore\n", + "from mobile_env.core.entities import BaseStation, UserEquipment\n", + "\n", + "\n", + "class CustomEnv(MComCore):\n", + " # overwrite the default config\n", + " @classmethod\n", + " def default_config(cls):\n", + " config = super().default_config()\n", + " config.update({\n", + " # 10 steps per episode\n", + " \"EP_MAX_TIME\": 10,\n", + " # identical episodes\n", + " \"seed\": 42,\n", + " 'reset_rng_episode': True,\n", + " })\n", + " # faster user movement\n", + " config[\"ue\"].update({\n", + " \"velocity\": 5,\n", + " })\n", + " return config\n", + "\n", + " # configure users and cells in the constructor\n", + " def __init__(self, config={}):\n", + " # load default config defined above; overwrite with custom params\n", + " env_config = self.default_config()\n", + " env_config.update(config)\n", + "\n", + " # two cells next to each other; unpack config defaults for other params\n", + " stations = [\n", + " BaseStation(bs_id=1, pos=(50, 100), **env_config[\"bs\"]),\n", + " BaseStation(bs_id=2, pos=(100, 100), **env_config[\"bs\"])\n", + " ]\n", + "\n", + " # users\n", + " users = [\n", + " # stationary user --> set velocity to 0\n", + " UserEquipment(ue_id=1, velocity=0, snr_tr=env_config[\"ue\"][\"snr_tr\"], noise=env_config[\"ue\"][\"noise\"],\n", + " height=env_config[\"ue\"][\"height\"]),\n", + " # two fast moving users with config defaults\n", + " UserEquipment(ue_id=2, **env_config[\"ue\"]),\n", + " UserEquipment(ue_id=3, **env_config[\"ue\"]),\n", + " ]\n", + "\n", + " super().__init__(stations, users, config)\n", + "\n", + "\n", + "# init and render the custom scenario\n", + "env = CustomEnv()\n", + "env.reset()\n", + "# random first step\n", + "env.step(env.action_space.sample())\n", + "plt.imshow(env.render(mode='rgb_array'))\n", + "# FIXME: this breaks if I run it multiple times" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 80583fb5177aa17a6f32155c281de5d23e7a951f Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Wed, 24 Nov 2021 17:27:08 +0100 Subject: [PATCH 07/14] adjust demo notebook --- examples/demo.ipynb | 41 ++++++++++++++++++++--------------------- 1 file changed, 20 insertions(+), 21 deletions(-) diff --git a/examples/demo.ipynb b/examples/demo.ipynb index 29a24ba..698e1e7 100644 --- a/examples/demo.ipynb +++ b/examples/demo.ipynb @@ -370,27 +370,25 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'width': 200, 'height': 200, 'EP_MAX_TIME': 10, 'seed': 42, 'reset_rng_episode': True, 'arrival': , 'channel': , 'scheduler': , 'movement': , 'utility': , 'handler': , 'bs': {'bw': 9000000.0, 'freq': 2500, 'tx': 30, 'height': 50}, 'ue': {'velocity': 5, 'snr_tr': 2e-08, 'noise': 1e-09, 'height': 1.5}, 'arrival_params': {'ep_time': 100, 'reset_rng_episode': False}, 'channel_params': {}, 'scheduler_params': {}, 'movement_params': {'width': 200, 'height': 200, 'reset_rng_episode': False}, 'utility_params': {'lower': -20, 'upper': 20, 'coeffs': (10, 0, 10)}}\n" - ] + "data": { + "text/plain": "" + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" }, { - "ename": "KeyError", - "evalue": "0", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", - "\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_8292/2394862195.py\u001B[0m in \u001B[0;36m\u001B[1;34m\u001B[0m\n\u001B[0;32m 51\u001B[0m \u001B[0menv\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mreset\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 52\u001B[0m \u001B[1;31m# random first step\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 53\u001B[1;33m \u001B[0menv\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mstep\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0menv\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0maction_space\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msample\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -424,8 +422,8 @@ "\n", " # two cells next to each other; unpack config defaults for other params\n", " stations = [\n", - " BaseStation(bs_id=1, pos=(50, 100), **env_config[\"bs\"]),\n", - " BaseStation(bs_id=2, pos=(100, 100), **env_config[\"bs\"])\n", + " BaseStation(bs_id=0, pos=(50, 100), **env_config[\"bs\"]),\n", + " BaseStation(bs_id=1, pos=(100, 100), **env_config[\"bs\"])\n", " ]\n", "\n", " # users\n", @@ -447,7 +445,8 @@ "# random first step\n", "env.step(env.action_space.sample())\n", "plt.imshow(env.render(mode='rgb_array'))\n", - "# FIXME: this breaks if I run it multiple times" + "\n", + "# FIXME: this breaks if I start BS IDs at 1, not at 0. why do I need these IDs anyways? why are they not set automatically?" ], "metadata": { "collapsed": false, @@ -458,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "outputs": [], "source": [], "metadata": { From 32501c71731b239ebcc77d54b1da91f75bfbefb3 Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Thu, 25 Nov 2021 09:57:14 +0100 Subject: [PATCH 08/14] replace deprecated abstractclassmethod --- mobile_env/handlers/handler.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/mobile_env/handlers/handler.py b/mobile_env/handlers/handler.py index c572ae1..b6b9ee0 100644 --- a/mobile_env/handlers/handler.py +++ b/mobile_env/handlers/handler.py @@ -1,5 +1,5 @@ +import abc from typing import Dict -from abc import abstractclassmethod from gym.spaces.space import Space @@ -7,27 +7,32 @@ class Handler: """Defines Gym interface methods called by core simulation.""" - @abstractclassmethod + @classmethod + @abc.abstractmethod def action_space(cls, env) -> Space: """Defines action space for passed environment.""" pass - @abstractclassmethod + @classmethod + @abc.abstractmethod def observation_space(cls, env) -> Space: """Defines observation space for passed environment.""" pass - @abstractclassmethod + @classmethod + @abc.abstractmethod def action(cls, env, action) -> Dict[int, int]: """Transform passed action(s) to dict shape expected by simulation.""" pass - @abstractclassmethod + @classmethod + @abc.abstractmethod def observation(cls, env): """Computes observations for agent.""" pass - @abstractclassmethod + @classmethod + @abc.abstractmethod def reward(cls, env): """Computes rewards for agent.""" pass From 21b049ec1bf3f6a9e2156996f76028cf832d976d Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Thu, 2 Dec 2021 21:49:07 +0100 Subject: [PATCH 09/14] minor edits in demo --- examples/demo.ipynb | 112 ++++++++++++++++++++++++++------------------ setup.py | 4 +- 2 files changed, 69 insertions(+), 47 deletions(-) diff --git a/examples/demo.ipynb b/examples/demo.ipynb index 698e1e7..2cf9d03 100644 --- a/examples/demo.ipynb +++ b/examples/demo.ipynb @@ -12,18 +12,20 @@ "# Demonstrating `mobile-env`\n", "\n", "`mobile-env` is a simple and open environment for training, testing, and evaluating autonomous coordination\n", - "approaches for mobile networks.\n", + "approaches for wireless mobile networks.\n", "\n", - "* `mobile-env` is written in pure Python and can be installed easily via PyPI\n", - "* It allows simulating various scenarios with moving users in mobile networks\n", - "* `mobile-env` implements the OpenAI Gym interface such that it can be used with all common frameworks for reinforcement learning\n", + "* `mobile-env` is written in pure Python and can be installed easily via [PyPI](https://pypi.org/project/mobile-env/)\n", + "* It allows simulating various scenarios with moving users in a cellular network with multiple base stations\n", + "* `mobile-env` implements the [OpenAI Gym](https://gym.openai.com/) interface such that it can be used with all common frameworks for reinforcement learning\n", "* It supports both centralized, single-agent control and multi-agent control\n", "* It can be configured easily (e.g., adjusting number and movement of users, properties of cells, etc.)\n", "* It is also easy to extend `mobile-env`, e.g., implementing different observations, actions, or reward\n", "\n", "As such `mobile-env` is a simple platform to evaluate and compare different coordination approaches in a meaningful way.\n", "\n", - "---\n", + "\n", + "\n", + "## Demonstration Steps\n", "\n", "This demonstration consists of the following steps:\n", "\n", @@ -33,12 +35,14 @@ "4. Training multi-agent PPO with [Ray RLlib](https://docs.ray.io/en/latest/rllib.html)\n", "\n", "\n", - "## Step 1: Install and Use `mobile-env` With Dummy Actions" + "### Step 1: Install and Test `mobile-env` With Dummy Actions\n", + "\n", + "Installing `mobile_env` via PyPI is very simple:" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "outputs": [ { "name": "stdout", @@ -51,24 +55,23 @@ "Requirement already satisfied: pygame==2.1.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (2.1.0)\n", "Requirement already satisfied: shapely==1.8.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (1.8.0)\n", "Requirement already satisfied: svgpath2mpl==1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (1.0.0)\n", + "Requirement already satisfied: setuptools-scm>=4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (6.3.2)\n", "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.4.7)\n", - "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (8.4.0)\n", - "Requirement already satisfied: cycler>=0.10 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (0.10.0)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (1.3.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (8.4.0)\n", "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (4.28.1)\n", "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.8.1)\n", - "Requirement already satisfied: setuptools-scm>=4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (6.3.2)\n", "Requirement already satisfied: packaging>=20.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (21.3)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (0.10.0)\n", "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from gym>=0.19.0->mobile-env) (1.6.0)\n", "Requirement already satisfied: six in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from cycler>=0.10->matplotlib==3.5.0->mobile-env) (1.15.0)\n", - "Requirement already satisfied: setuptools in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (46.1.3)\n", - "Requirement already satisfied: tomli>=1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (1.2.2)\n" + "Requirement already satisfied: tomli>=1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (1.2.2)\n", + "Requirement already satisfied: setuptools in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (46.1.3)\n" ] } ], "source": [ "# installation via PyPI\n", - "import time\n", "!pip install mobile-env" ], "metadata": { @@ -78,14 +81,30 @@ } } }, + { + "cell_type": "markdown", + "source": [ + "`mobile_env` comes with a set of predefined scenarios,\n", + " registered as Gym environments,\n", + " that can be used out of the box for quick experimentation." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "pygame 2.1.0 (SDL 2.0.16, Python 3.8.2)\n", + "Hello from the pygame community. https://www.pygame.org/contribute.html\n", "\n", "Small environment with 5 users and 3 cells.\n" ] @@ -94,6 +113,7 @@ "source": [ "import gym\n", "import matplotlib.pyplot as plt\n", + "# importing mobile_env automatically registers the predefined scenarios in Gym\n", "import mobile_env\n", "\n", "\n", @@ -116,12 +136,13 @@ "As the users move around, the goal is to connect the users to suitable cells,\n", "ensuring that all users have a good Quality of Experience (QoE).\n", "\n", - "Connecting to more cells increases data rate for a user but also increases competition for resources\n", - "and may decrease rates of other users.\n", + "Using coordinated multipoint (CoMP), users can connect to multiple cells simultaneously.\n", + "Connecting to more cells increases the user's data rate but also increases competition for resources\n", + "and may decrease data rates of other users.\n", "Therefore, a good coordination policy needs to balance this trade-off, depending on available resources and\n", - "users' positions.\n", + "users' positions (affecting their path loss).\n", "\n", - "To measure QoE, the `mobile-env` defaults to a logarithmic utility function of the users' data rate,\n", + "To measure QoE, `mobile-env` defaults to a logarithmic utility function of the users' data rate,\n", "where -20 indicates bad QoE and +20 indicates good QoE. This can be easily configured and changed.\n", "\n", "To get started, we will use random dummy actions." @@ -135,12 +156,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "outputs": [ { "data": { "text/plain": "
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\n" 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\n" }, "metadata": { "needs_background": "light" @@ -151,9 +172,6 @@ "source": [ "from IPython import display\n", "\n", - "# TODO: why do UEs move differently everytime?\n", - "# isn't there a default seed 0 that should lead to reproducible behavior?\n", - "\n", "# run the simulation for 10 time steps\n", "done = False\n", "obs = env.reset()\n", @@ -161,6 +179,7 @@ " # here, use random dummy actions by sampling from the action space\n", " dummy_action = env.action_space.sample()\n", " obs, reward, done, info = env.step(dummy_action)\n", + "\n", " # render the environment\n", " plt.imshow(env.render(mode='rgb_array'))\n", " display.display(plt.gcf())\n", @@ -180,24 +199,24 @@ "The five moving users are shown as small circles, where the number indicates the user ID and\n", "the color represents the user's current QoE (red = bad, yellow = ok, green = good).\n", "\n", - "A line between a user and a cell indicate that the user is connected to the cell.\n", + "A line between a user and a cell indicates that the user is connected to the cell.\n", "Again, the color indicates the QoE that's achieved via the connection.\n", "Note that users can connect to multiple cells simultaneously using coordinated multipoint (CoMP).\n", "\n", - "Before, training a reinforcement learning agent to properly control cell selection,\n", + "Before training a reinforcement learning agent to properly control cell selection,\n", "we will look at some configuration options of `mobile-env`.\n", "\n", "\n", "\n", - "## Step 2: Configure `mobile-env`\n", + "### Step 2: Configure `mobile-env`\n", "\n", - "### Predefined Scenarios\n", + "#### Predefined Scenarios\n", "\n", "We can choose between one of the [three predefined scenarios](https://mobile-env.readthedocs.io/en/latest/components.html#scenarios):\n", "Small, medium, and large\n", "\n", - "By default, these are available for either a single, centralized agent (e.g., \"mobile-small-central-v0\")\n", - "or multiple agents (e.g., \"mobile-small-ma-v0\"), which affects the observations, actions, and reward." + "By default, these are available for either a single, centralized agent (e.g., `\"mobile-small-central-v0\"`)\n", + "or multiple agents (e.g., `\"mobile-small-ma-v0\"`), which affects the observations, actions, and reward." ], "metadata": { "collapsed": false, @@ -235,20 +254,20 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "outputs": [ { "data": { - "text/plain": "" + "text/plain": "Text(0.5, 1.0, 'mobile-small-central-v0')" }, - "execution_count": 10, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -262,7 +281,8 @@ "env.reset()\n", "# random first step\n", "env.step(env.action_space.sample())\n", - "plt.imshow(env.render(mode='rgb_array'))" + "plt.imshow(env.render(mode='rgb_array'))\n", + "plt.title(\"mobile-small-central-v0\")" ], "metadata": { "collapsed": false, @@ -273,20 +293,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "outputs": [ { "data": { - "text/plain": "" + "text/plain": "Text(0.5, 1.0, 'mobile-medium-central-v0')" }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", - "image/png": 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lvvrqK0JDQ+nVqxdubm4sX74crVZLly5daNSoET/88AMKhYJatWoxefLkGjFRqKxh7yyEiJEkyQv4W5KkS3e/KYQQd4x+ISRJmgZMAwpdpOYmIiKCy5cvM2HChDIbT29vb8aOHVuuDLuGDRsSGBhYarpzPkIIVCoVTZo0oWPHjjXigqgMOTk5GAwGHB0dK30sy5cvZ+3atWzduhVv78rNDXJyckwLtvk3ibuvg+PHj/Pss8/y448/0rlz5yLbWL9+PeHh4bz55psVjquvCD/99BNJSUksXryYevXq8cUXX/DFF18wa9YsZs6cycSJE/nhhx/IyckhIyODxo0bo1KpMBgMeHp64ubmxqxZs3j22Wfx9vbGxsaGkJAQbt26xeuvv84bb7xRLjmFe5HL5Tz66KNcvXq1gCtx69athIWF0aRJE3x8fDh27BgBAQEmuWWj0Yi3tzeOjo4kJiaa41SVC0dHR5566ik+++wz00x9y5Yt+Pr6mp4sIO8pw8fHh0GDBlG7dm3kcjlz5szh6tWrbNq0iYCAADQaDR07duTIkSPVfhzFUSkfuxAi5s7vROBPoC2QcMcFw53fRX5qQohFQojWQojWVe1+aNq0KX379sXGxqbM+ygUCjw8PMo1W7SxscHDw6Ncj2N79uxhy5YtD4V/ff/+/WzatKmQJkhFmDBhAl988UWpKedlYePGjYwYMYLr168Dee6XDh060KFDByRJolWrVnz99dc0bdq02DZiYmK4evVqgS/93eh0OpYsWcLu3bsrPd67iYqKolu3bvTr14969eoRGBhIZGQkKpWKY8eOER0dTXp6Ol999RWxsbH4+vrSrFkzjh8/Tnp6OuvXr+fgwYNERETw/PPPs3fvXhwcHLC2tua9994zCXxVFEmSsLOzKxRQcPXqVW7fvs2GDRs4dOgQXl5epKSkkJSUhFwux8vLi4yMDOLj46t1PUGv15Oeno5Wq8XW1tY0Ably5QpxcXEEBQWhUqlM30dvb2+aNGnC3r17Wb16tUlWeMuWLfTs2RMbGxusrKy4fv06dnZ2Zrn2zUGFDbskSfaSJDnm/w30Bc4BG4FJdzabBGyo7CDvRghBTEwMt2/fLnPyiYuLi+luWxO4ePEif//9NwaDgUaNGtGyZcuHItKmYcOGZjuWwMBAunbtWuqNNT/dvaRroV27djz//PMF1Bjv9uk6OzvTs2fPEp+0ZsyYwYoVK4p11eh0OlavXs3OnTtLHG956dChA9u2bePLL79ky5YtHDx4kGvXrrF06VLTNr6+vtSuXRtra2tef/117OzssLOzY9myZbz11lumrOB69eoV0Kkxx2Ti7vN/909YWBjt2rWjdevWJhdlvuTt9evXOXr0KD179qRfv37cuHGj2hLJkpOTWb9+PadPny4w7tzcXDQaDXv37uXYsWNkZ2djNBpxdXVl8ODBDBgwgEuXLpGZmckvv/xCkyZNaNWqFZGRkdSqVYuJEycSGxtb5kLyVU1lbpXewJ93vhwKYKUQYrskSeHAGkmSpgK3gLGVH2ZBwsPD0Wg0+Pv7V4n7Ijc3l7S0NDw9PatkNrF8+XL27dtH27ZtS5QEeNAoTlagKomLi+Pll19m8uTJxWrmBAcHV3pmqlAoSrwWbG1tWbp0abE64UIIbty4YRpPWa/bgQMHMnDgQA4fPkx4eDhGoxF/f3/OnTtHr169CAkJISsrC1tbW/r06YOjoyOTJk0iIiKCefPmcevWLQIDA+nduzdKpZKOHTsSFBREz549+fTTT5kzZ06lXDEGg4HVq1eTnJzMzz//TI8ePfD09GTQoEGsWrUKIQSPPPII2dnZ/PXXX8hkMkaOHIlMJuOPP/4AYPDgwdXmhvTx8WHKlClkZWXxyy+/kJyczJo1a5g8eTItW7bk2LFjGAwGlEole/bsISgoiHXr1iGEYPz48cTGxnLu3DlUKhU6nY4uXbpw6tQpvv76a/r3719j1skeuMxTIQTp6ekYjUbc3Nyq5IK4cOECe/fuZdSoUZX27xZFTEwMqampNGzYsFxPESqViiNHjtCgQYNKL3o9SBgMBpKSknBycirk346NjeXFF1/kiSeeoG/fvvdphKVjNBqZMGECMpmM5cuXPxRPaBbuLw9V5qkkSaYY6KoiMDCQnj174uLiUiXt+/n5FSjDVVY0Gg2RkZF4e3v/pwx7dHQ0Q4cOZdq0aTzzzDMF3vP19WXFihU1fuFZkiRmz55dalifwWDgwoULBAUF4eDgUI0jtPAw8cAZ9urAwcHBrLri8P+yBJUxQC4uLkyYMMHs4X9FPbXVJENpNBpNPtB7MVf8szk+n5LIX7AtrX+9Xk94eDg+Pj4Ww26hwlgMezWQlJTE22+/zZgxY+jRo0eF25EkqVyRPaUhhCAtLY0zZ84QERFBdnY2Dg4ONG/enCZNmuDq6lrA0JWluEdVkF9zs6qip4QQHD9+HLVafV8q3hgMBj755BOcnJx45plnGDFihMWoW6gUFsNeDWi1WqKiokhLK1Eyp1oxGAwcPXqURYsWYWdnR+vWrfH39yc9PZ3ff/+dX375hWnTptG+fXvTOkBCQgIbNmygb9++1K1bt9rGqlAoKrXAVxZycnLIycm5LzK/Qgji4uJQq9Wm/ksqGF0VyOXyGhOqV14e5LE7ODhUyU38gVs8fRDJT182d6WWksg3FHXq1Ck0AxVCsHPnTr755htmz56NSqVi165dZGVl4ejoSO/evXF0dOSLL75g5syZ9OvXz1TY98qVK4SEhFRJhubt27c5ceIEffv2LTG64MKFC0RGRtKnTx+zPDncHaZ3Pyoc5SfI5Ke1r1u3jsuXL1db/zKZDCcnpwdSIVEul+Pg4FAja8mWhe7du9OtW7cK7ftQLZ4+iEiSVOmKMeXl9OnTJCcnExAQUMCwCyG4du0av/32G3PmzGH79u3o9XrGjh2Ln58fUVFRbNy4EblczuzZs/n111+pV68eQUFB2NnZ0bx58yob8+7du1mwYAFNmjQp8YlgzZo17Nixgw4dOphlIf1+68XcWw1o3Lhx920sFh4OLDP2OwghOHv2LDk5ObRr1w6ZTMa6deu4evUqL7zwQrHxybm5uSQnJ+Pt7V2tfufSyMzMRKvV4u7uXsBoCSH4/PPPsbGxQaPRoNFoGDFiBH/88QfXr1+nXr16DBs2jD///BN7e3uUSiUqlYo5c+ZU+dNGWloaUVFRNGjQoMQbYXx8PKmpqdSvX7/GJJ1ZsFDdVLVsb5UihCAiIgK1Wm32drdt28bmzZtNfs3ExETi4+NN/1+8eJHjx4+X6L/7+++/GTx4MBcuXCj03vXr11m0aBHx8fHV7gN0cnLCw8Oj0ExUp9Nx9uxZmjdvzrFjxxg5ciQLFizAxcWFwYMHY2dnx5dffsmjjz5KeHg4jRo14vz588Wm0psTV1dXmjZtWurTjY+PT7lzAB4UtFotixYtIiEhAcibOORf+2q1mszMTFQqVYF99Ho92dnZ97UMoNFoJCcnp8A6wd2v569f6PV6cnJyyM3NrRHjValUpnEIIVCr1eh0OtNrBoOB7OxssrOz0el0GAwG0/EYDAbTPvnv1xQeGMOemZlZ7v3ulmct6v1Vq1axYsUKU2p19+7dGTx4sMl18fLLL/Pbb7+VWLqqcePGTJ06tci49BMnTvDOO+/Qr18/jh07Vq7xVxVJSUlotVp0Oh3Ozs7Ex8ebkn6+/vprZDIZNjY2REdH4+HhgV6vN13s5qS0z+e/Sm5uLosWLTIttH/00UemIuDPP/88w4cP548//ihw/g4dOkTnzp3R6/WFUvvzI5/OnTtX6HVzkpKSwmeffca7775rulaMRiP79+9n4cKFLF26lOzsbFauXMnChQv5/PPPyc7ONusYykNmZiZfffUVr776KhqNxlTg5Nlnn2Xr1q2m7S5fvsyLL77IL7/8wqVLl4iNjeWbb77h888/Z9euXcTHx/PRRx/x9ddf88svv9SY67nGG3ZJkhg2bBgeHh7l2u+ff/5h2rRpJCUlFdvuZ599xhdffGFaMFMoFCZtZsjTvba3ty/R/xoUFMSzzz5b5PgGDRrEmjVr6N69e5UnVZWV/AW6/N8ymQyDwYC1tbXpx2g0IpPJ0Gg05ObmVokPOikpiWnTppldNOtBx8HBgT/++IM6deoAednGN27cYP/+/dy4cYPz589z6tQpnn76aUaMGEGbNm2YN28eN27cYPbs2bRr144WLVrQokULPv/8c4YNG0avXr2YMmUKzzzzDAcOHKB9+/YFjJc5cHNzY8qUKaxZs4b4+HggL9Jo5syZ+Pj4EBYWRlxcHJ999hkeHh4cO3aMb7/91qxjKA9OTk48+eST7Nixgxs3bpCYmEj37t05fvw433//vWm7uLg4jh07ZnqS9PX15YUXXsDFxYWlS5fy448/smLFCjIyMti4ceN9O557eSAMu7Ozc7kfu3NyckhJSSnWhSBJEt7e3vj4+FTZwpm9vT2dO3dm4cKFxSY8paSkMG/ePM6ePVslY7gXDw8PHB0dMRqN3L59G0mSkMlkODo68vjjj2Nvb49CocDHx4dLly5x7NgxHBwczK6ZYzAYSElJIScnp9z7pqenM2fOHNavX2/WMV24cIEdO3bcVyEnmUxGnTp1CuQr5ObmkpCQQG5uLomJiURGRpKSksL169dxdHRkypQpuLm58fHHHxMdHc3o0aNxcXEhNjaWixcv8sgjj/Dll1+ybNkyUxRUcbo6FUUul2NtbU1ycnKBGXt8fDyvvfYaAwcOZOnSpdjY2PD++++za9eu+yLXm5WVRVRUFAkJCVhbW5OamopGo+Gnn37iwoULDB06lKioKNN1mW93Fi9ezMCBA7l16xYxMTEsWbIEpVJJZmamSVsqIiKC8PDwaj+moqjxhr047q7QUhSDBw/m999/L6DoVxNJTU1lw4YNXLp0qfSNzYCVlRXNmzfn6NGjBAQE8NtvvzFr1iycnJxYuHAhLi4uzJo1i0WLFtGrVy+0Wi3NmjUz+8Kwj48Pv//+O0OGDCn3vjk5OWzYsMHsX6KYmBguX75co3ylkCeuNmbMmAJ1CwYOHEifPn0IDw83+ePzfcORkZEFwv+aNWtGq1atGDhwIKdPn+a1114z++dpNBrRarVA3k1bp9OZvpuDBw8mMDAQmUzGqFGjWLp0KaGhofdljeT8+fMsW7aMv/76yzRevV5PvXr1mDt3LleuXCEtLY2rV6+i1WqpVasWS5YsYfTo0SQmJnLq1ClGjhyJvb09H330EWfPnqVNmzaMGTMGnU5nelq53zywUTHp6enMmDGD/v37M2nSpNJ3uE9kZ2dz9uxZGjVqVKQsrMFgIC0tDUdHx2Ijb8yJEILo6Gj+97//MWnSJA4fPoyLiwsxMTF89913zJo1C39/f1JTU2nXrh0rV67k448/rtInm/KSLwpmb29v1nh6jUaDTqcrUl/8frFs2TIyMjKYNWsWCxcu5MCBA7Rt2xa9Xk9CQgLx8fE8/vjjLFu2DB8fH+Li4kwL9YMHD+bEiRNMnTqV5s2bs3jxYl577TU2bNhAx44dzTrOW7dusWDBAuLi4ujVqxdCCIKCgli1apXJlz5q1Cj27Nljyuv45JNPqkRkrywkJiby0UcfERsbS7t27ZgzZw4Ahw8f5s8//+Sdd97hww8/pFu3bvz1119oNBoaNGhAbm4uZ86cwcnJic6dO9OqVSvmzZuHo6Mjnp6e1VqIpaSomAfWsGdmZjJ37lx69erFqFGjqmhklefQoUNMmzaNhQsX0rt373Ltq9PpiImJwcvLy6wXixCCw4cP8+233zJx4kRsbW354osvUKvVWFlZ8dJLL5GTk8Pvv//OrFmzaN++vVmMekZGBhkZGfj5+dW4iBYhBDk5OSYjZG9vj4ODQ7XfzNRqNW+88QazZ882a2WxdevWsXr1ahwdHfnll1/M1q6F+8dDadjvHre5RKBu3LiBWq2mYcOGZpuxZWZmcuTIEdq0aVPuBdSzZ8/y6KOP8s477zBixAizjCcfo9HIuXPn2LNnD1FRUabSZjqdjiZNmuDr60ufPn1o3Lix2Yzbxx9/zIYNG9i0aVO5F8OrCiEESUlJbN68mf379xcouNyhQweGDRuGl5dXtc3gs7KyGDNmDF999RX16tUzW7vm/r5YuP880JmnxV2QVXFxXr58mYyMDMLCwszWppOTU4V1wv38/JgxYwYtWrQw23jykclkNGnSBCsrK9588008PDxYtWoVjz76KDdv3uTpp58mJCTErOe5T58+ZlEtvHr1KlFRUXTp0qVSvmIhBKdOnWL+/PmEhYUxffp00/Umk8n4559/eP7555k9e7bZnlpKw8HBgfXr15vdLWcx5v8tavyMXa/X8/TTT/Pqq6+adQZzL/mJBkajsdQQx4cFo9HIZ599RmxsLNHR0Rw+fJiuXbua9N5feumlaq1HWVbefvttdu7cydatWysVRnrhwgVef/11Jk2aRFpaGvv27TMZVI1GQ6dOnfD19WXp0qXMnTuXFi1a/CeuCwsPBg/0jB0wGdyqJL8ob1WQH9rn5ORUadldo9FIamoqtra2lS7DFRsby++//46trS2TJ09GLpfTvn171qxZg1qt5tFHH8Xf379SfVQF06ZNY/jw4ZVaOFWr1SxevJixY8dy7do1YmJiePbZZ03FVTIyMli9ejWpqamMGTOGn3/+mQULFlT5ArfRaCQuLq7KI3Pycxks/D/345y4uLhUSUGfGm/Y5XI5y5YtqzFRChUhOjqaMWPG8Mwzz1Q6gicrK4uxY8fSq1cvXn/99Uq1FRcXh0ajwdnZmUOHDnHr1i2sra3RarXEx8cTFxdnFsNuNBrR6/UoFAqzfI4VrUCVjxDC5HYLDAxk+/btTJs2jZ9//tmU8enj48OsWbP44IMPaNGiBXq9ntOnT9OuXbtKj78kMjMz6dmzJ0OHDq0y/XkLNYfOnTubPUIJHgBXzMNARkYGixYtok+fPpVWR8zNzeWnn36iUaNGlSraAXlurvT0dHJzczl27BjNmjXD0dGRa9eukZKSQp8+fcxS2OPYsWO8++67fPTRRzRt2rTS7VWWfDmJ6Oho1Go1YWFhHDlyhKSkJLp06WKqqyuTyQgNDeX06dP4+vqiUCiYMmVKlc7qcnNz+eqrr3j00Uf/U+UPLZSfSomASZL0iyRJiZIknbvrNTdJkv6WJOnqnd+ud16XJEn6SpKka5IknZEkqaX5DuPBxdnZmZdfftkskrc2NjbMmjWr0kYd8iI/8vVg1q5dy8WLF/Hy8qJjx44MGTLEbNWa7O3tqVWrVomaOyWh1+u5ceNGufWCSiIuLg5PT0+ioqIIDg5Gr9czePBg1q1bx4YNG6hXrx5XrlzBx8eH5ORkPDw8SE5ONlv/xWFjY8Mrr7xiMeoWKkVZnouXAP3veW0usFsIEQrsvvM/wAAg9M7PNOB7KokQgoyMjGpVRzQYDGg0mir369cULl26xMCBAzl37lyRdUUrS8OGDVm0aFGFqy7FxsYyatQoVq5cabYxubi4kJGRgZeXF/Hx8fj7+xMXF0ePHj3o1KkTiYmJ1KpVi/T0dJydncnMzMTZ2dls/ReH0WgkLS2tWtQ0LTy8lGrYhRD7gdR7Xh4G/Hbn79+A4Xe9vlTkcQRwkSSpUjn9BoOBMWPGcO7cudI3NhM3btxgxYoVpKbee9jmQQiB0Wg0ixJcZdsyGAxs27aNmJgYzp07Z0pPNyf5ImIVdWG4u7sze/ZsunfvbrYxNW/enGvXrtGlSxd27tzJkCFDiIyMpE6dOgQFBXHjxg3GjRvHrl27aNmyJREREWaN6S+OzMxMOnfuzPXr16u0HwsPNxVdyfIWQsTd+TseyM8L9gOi7tou+s5rFUaSJNq2bVut6ohOTk7Url3brIWj7+bXX39l5syZhXS1K4JWq+X5558voEhXHq5fv46/vz9jx46lb9++nD9/vtJjMjf29vZMnjy5WCG18iJJEqGhoSaZW2trazZt2sTMmTNZs2YNy5cvZ8qUKezcuRODwYCtrS1ZWVlVWj0qHysrKzp16lRht5UFC2CGqBghhJAkqdzTRUmSppHnrikxdVoul/PBBx9UfIAVwNvbu0o1LKpCD7ui7Xl4eDBx4kR8fX3x8PCokrqX165d45dffuHpp5+mdu3aZm+/Ijg5OTFlyhS+/PJLZs6cyalTpzhw4ADHjx9HoVBw6NAh9Ho9I0aM4Ndff2Xy5MmVDi8tC/b29ixatKjK+7HwcFOmqBhJkuoAm4UQje/8fxnoLoSIu+Nq2SuEqC9J0o93/l5173YltV9To2IMBgOZmZlcvnyZxMRE7OzsaNCgAd7e3igUigo/lucbdnPonJuzrXyMRiNGoxG5XM6tW7fIzc2lXr16FQ5VPHjwIK+++ioLFy6kdesiF/FNCCHYv38/GRkZDBo0yOyaMkIIDAYDMpkMIQS7d+9m8eLFzJgxg9WrV5uq5FhZWfHEE0/w5Zdf8sQTTzBgwIBqCbkVQqDRaFAqlQ90iK+FqqcqEpQ2ApOAj+/83nDX67MkSVoNtAMySjPqZSH/5lNdyQP54W5Lly7lwIED+Pj4UKtWLbKzs1m8eDG1atXiySefpGHDhhUa071GuDI6HlVRBCM8PJxbt24xbNgwzp8/T0ZGBnXr1q2woWnfvj3btm0r84x39erVxMTE0L9/f7Mbdp1Ox8aNG/Hz86NDhw707t2bwMBAzpw5Q3h4ODY2NshkMnQ6HVeuXGHevHmEhIRUm5HNyckx6aeHhoZWuJ3SJmyW5KSHm7KEO64C/gXqS5IULUnSVPIMeh9Jkq4Cve/8D7AVuAFcAxYDMys7QIPBwKxZs7hx40ZlmyoTQggSEhKYO3cuOTk5zJ49m44dO2JtbU1AQAAzZsygbdu2vP/+++zbt88skTM5OTm8+OKLrFu3zgxHUHkcHR1xc3NDJpOZygVWxsAqFAqcnJzK3MY777zDd999VyXFwSVJwtXV1ZS1KpPJCA4O5t9//2Xy5MlkZWWRkZFhkjT29/evViVKmUxGSEhIqXVfSyMtLY1u3boxZswYBg8ezEsvvcTgwYPp3r27pWrVf4BSZ+xCiPHFvNWriG0F8ExlB3VPm8THx1dbVRu1Ws38+fOpX78+QUFBfPfddzRs2JCGDRsSFxfHDz/8QIsWLXjsscf45ptvCAwMJCgoqFIzIKPRSExMDCkpKZUau0qlYvHixbRu3ZpOnTqVe3+DwYBWq6VBgwY0bNgQoFTjmu86UCgUZtGVya9sVVVYWVnRq9f/X7pCCE6cOMGFCxdITEzE19cXg8HA4cOHiY+P5/jx43Tp0qXaZrh2dnZ89dVXZmln1qxZ1K9fn0ceeYRt27bx/PPPs2fPniqL9rJQc3ggJAVWrVpVbWJUx48fJz09nT59+rB06VLeeOMNYmNjOXXqFGq1mjZt2phKjg0dOpQlS5bw5ptvVmp26ejoyPLlyyv9uK9Sqfjjjz8wGAy0adOG69ev4+vrW2Ytilu3bvHPP/8wePBgfHx8yrSPWq1m7dq1NGjQoNR0+0uXLnH16lX69euHUqlEr9fz999/4+fnd98yUvV6PatWreL06dM89dRT1K1bF51Oh1KpZPfu3SxdupS2bdtWWYRUVWFjY8PIkSN5+eWXSU9Pp1u3brz00kuFblArV64kOjra7EVLLJROfuWmzp07m73tGm/YJUmq9GNpWTEajfz9998MHDiQbdu28dhjj3Hw4EFOnDhBixYtSElJ4fTp08ybN4958+bxzDPPsH37dlJTUys1y5QkySxuBzc3N9avX4+dnR3Z2dns3buXdu3a0bJl2RKAnZ2dqVu3brnE0BQKBcHBwWU6/vXr1/PXX3/RunVrjEYjNjY2fPTRR3Ts2PG+GXaZTEbHjh3Jzc0lNzfXVF5OqVQydOhQOnbsWK2uGIPBwIkTJwgLC6uUodVoNPzvf//jjz/+YPny5WzZsoUvv/yykN5Njx49SExMZOvWrQwfPtwMR2ChrMTGxrJr167/pmE3GAy8/vrrzJgxo8pD5XJycrh58yZDhgwhPj4eX19ffvvtN8aPH8/SpUvp3LkzOp2OyMhIwsLCuHXrFjY2NmRlZVWJ+yA2NpYvv/ySRx99tEyGTyaTmYSj8mdsRZXjKw53d/dyJwEplUq6dOlS5HsGg4G4uDhcXFxwcHDgySefZNiwYWg0GrZs2ULfvn1ZtGhRtWR0FodcLmfcuHGMGzeOBQsWoNFoCA4OZuTIkVXi4y+N7Oxspk+fzooVKypVFyA7O5t9+/YRHBzMxo0buXLlCgaDgezs7ALXhK+vL7a2tri4uFRKWM1C+TEYDFXm4qvxhl0IQURERIHivFWFXq9Hp9Mhk8lMJ1ypVJKWlkZ6ejpXr14lKCiIlJQU7Ozs0Gq1yGSyKpM7yMzM5OjRo/Ts2ZPg4GAuXbpEgwYNTIUqhBDk5uZiZWVVyFUll8vvWz3JfBISEhgxYgSTJ0/mmWeewcvLCy8vL3Jzc+nbty/+/v5VloiTH9YIeeeiLF+gxMREXnrpJRwcHO6bDr2dnR0ffPBBmV1hxeHm5lZssW9LRMzDT40PlJXL5axZs4ZGjRpVeV/29vZ4enqSk5ODlZUVer3eZEQfffRRhgwZwtGjR2nVqhU3b97Ey8sLjUZTZYkr9erVY8uWLfTu3ZuUlBTCw8MLLLDmG87Vq1cX2C8hIYG1a9cSF1fxSNP8oteJiYno9XouXbpUZHv5cfRFhdc5OzszZcqUQgu5NjY2hIaGVnl25Z49e9i2bVuZk7eysrL47bffOHz48H0zflZWVgwcOLDSmdaSJCGTyYr8sRj2h58ab9glScLR0bFa/JxKpZIOHTpw+PBh2rdvz8aNG5kxYwa2trbs37+f5ORkpk6dyvXr19HpdFhbW2Nra1tlcgcymcw0e/Tz82P8+PEFHpetra1p0qRJoUfoEydOMH36dP79998K920wGNi7dy/h4eFoNBq2bdtGUUlkv//+O8899xw5OTmF3rO3t2fGjBlFpuLHxcVx7dq1MoWLZmdnk5qaWu7sWjc3t3LVVnVycuKZZ56hZ8+e5erHgoWaRo037AaDgXnz5hETE1Mt/fXs2ZMzZ87QsGFD1Go1W7ZsQalUsmHDBo4dO4ZSqWTVqlWMGjWK1atXM3bsWORyORqNxuwyAXejUChwcXEp4CJwdXXls88+KyTh265dO1auXImVlRWffvopWVlZRbZpNBpZsmQJa9euzRu7ECCMYNQgl3Lp06sTqSmJLF++nCFDhtC+fftCbeQb3fLG83/yySfMmDGjTGqShw8fZtOmTeWqKiRJEq1ataJDhw5ljjZydna+77NZo9FIcnJylVdQsvBwU+MNuxCCv//+m8TExGrpz8fHh6effprFixczePBgOnTowLp16wgICODYsWNkZmYyZ84ctm3bRlBQEF27dmX79u3s2LGjQv3lp++b86bg7u5Ov379iI6OZsOGDUXOpvP7/vvvv9m7d++d/3XoVBcxqvYhcnbiZnsKV/ubxNw+Q2BgQJEVfZ544gl+++23ckdwTJw4kRdeeKFMEU+NGzemQ4cOpptaSe6fyvDSSy9VWwRWcWRnZzN8+PBqS8iz8HBS4xdP5XI5a9eurZK6gEUhSZIpIWXdunW0bduWtLQ06tevj1Kp5MCBA7i7uxMSEsITTzyBlZUVwcHBFZrpCSE4ePAgOTk59O3b1+zupsmTJzN27NhiXUVyuZzvvvvuzozWwNmTm/j+h8U8PqErzo627PwnAo1Wz1OP98FKlgHCE+45zoqOuU2bNmXeNr/ohDAKsq7eIu3MJYw6PfaBvri2aITcRmmWmXZNKNytVCoZOHCgaW3HgoWKcP+v5FKQJKlcflJzIJfL6dq1K8HBwbz11lsolUr27dtH/fr1SU1N5e2336Zly5YmQ1CZGGxra2uzhT0ZDAZiYmJwdnbG2dkZW1vbEhcoJUn6/1BDfSINgqFfr6ZoNDoatA7FycmOtX/+S1paCgG66yB3Bao2BPDKlSt89913zJo1q0BhDl2OmkPfLuHWkj+xuhJNllGH3tOZsJ6daTvvRezr+N93N0ppCCHQZWShiorDoNGidHHCLsAXmdLKNHYbGxtee+21+zxSCw86D4QrJjs7u1orKEGe0bt+/Tp6vZ6mTZui1Wrx9PQ0CVqZq4+2bdvSvXt3sxiltLQ0xo4dy48//li+HYUAfSxKJVhZ3ZmBS3DzViLZObk4OtiAIQGEttJjLI20tDTOnDlTILxVGI1cXbKOlW/O4/eL4agNOvaLdI4k3mbD72sIf+Y99DlqcnJyOHbsWJVID1cWYTASv+swhya+xNa2o/mr7Si2dp/IyRc/Rh2bUKJbSQhhKvyiVqvL1F9+Jab8705aWhqpqanFrrdYeLio8YY9v4JSdReAyM3NZenSpdy8edPkKgkNDTUt5F2/fr3cPt6ifMOVrS50Nw4ODkyfPp3evXtXYG8DIMhfQ9Vq9HRq34AmDQO5diMeMOYtrJJXnPvatWtVUr6tdevWbN68mRYtWphey01I4faSP2mjtcYOGSoMyJDoiTO3hYakQyeJ332YzMxMTp48WS21ScuDEIKkwyc5MuV/xG7dx+HcZHaLNNbFXCLi++UcfepNtGmZJV5P3377LUFBQYwdO5YtW7aUui6TkpJCw4YNmTJlCtevX6d+/fpMmDCBsWPHVsUhWqhh1HjDLkkSzZo1q/bsxJiYGE6cOEFOTg62trYkJiaadLJv3rzJqVOnyt1mbGwsf/75J2lpaVUw4rzH+MmTJ5dZQqAAMgcuXYljz4Fz/P1PBFu2n2D+wg0kJGXQpmVdkOxAynM9RURE8Ndff1VJ0phcLsfOzq5AJEtuYgpppy+Rd+uTEIAEKJAwItBnZpP872m8PD0ZP348derUMfu4KoNBreHq9ytRxyRiRJCBHm+sMAIGo5H43f/y2WNP8/nnnxdrrGfPns3LL7/Mjh07GD9+PCNGjCixT1dXVx577DFUKhUGgwF/f3/69OlT7EK6hYeLGu9jl8vlfPzxx6VvaGb8/f1ZtGgRN2/eRK1WExoairu7OwMGDOC5556jWbNmJe6v1WrR6XTY2dmZZuNGo9FUju1+IIQgNTUVKyurglIDkgQKf8Ia1GXBR7WK2FMCRQBI1gCcP3+ev/76i9GjR+Pu7l5kP/lG3xwhhEIIBIJMDKgxYECgQ3AVNW75l7AQyOTy+ypPUBy67Bzidh7KexQC7JGjQyABBgRGrRb14Qjo0LjYNr755hsyMzNZsWIFWVlZpU4sFApFgSim4cOH07t3b06cOFFgu6+//ppLly7VmMpWFsxDjTfs9wtra2vatWvHhg0b8Pf3R6VSYWVlxdixY8sUFx0eHs6VK1cYN26caQHT39+f0aNHm3WRTwiBXq9HkqRSozo0Gg1PPPEE9evX57PPPiv4pmQH1s1AcxpErunmI4SEZOWHZFUX7syZ+/fvj6+vb7GSBUIIZs+ejUwm4+effy7z8eYX5s7PmszHxssdx8ahHD0djhtWpGOgIbbEoKU1jigc7XFve39ExMqC0BswavPi0vUIUtDTA2dyyCYbAy4o6NezN53mzsVoNHLx4kWCgoKwt7c3ySI3adKETz/9FCEEb7/9Nl9++WXJfd65LoxGIxqNhi+++AK5XF4oH2TmzJmkp6ezZs2aqjp8C/eBGm/YjUYjBw4coGXLlvdFVlSpVDJhwgSsrKx4++23GTZsWJlS4QMCAgpplFdFtSONRsOMGTNo2LAhL7/8MsnJyVy8eJHWrVsXGqdCoWDYsGH4+voWbkiSQO4LNk6guw66a2RmQ0q2N0F1WwIKU6hjUFAQQUFBxY5JkiRTKbnyHK/RaGTr1q24uLgUEBaz9fYgdNIIci5ex6j5/8SdEPKOz71dU3z7dKqxUTEKe1scGwSRGn4WJRJNsOMgmXigwAclklyOa/MGyORytFotx44dw9vbG3t7e9RqNe3atePGjRv8+eefCCHo3LlzqWGmOTk5xMXF4ePjw5IlSxg0aBBRUVGF1l/kcnm1qldaqB4eCMM+d+5cvv7661LrZVYltra2yGSyMi8YBgYGllikuyT0ej0HDhzAw8ODJk2alLitJEnUqlXL9Ni9Y8cOPvroI9auXVtIHVChUDB16tSSGgO5A1AbdNdJy5RIzrAi6C6jXhYkSWLcuHHFvq/RaIiKisLPz6/QzcfBwaGQbLAklxHy5Bh0mdncXLqBnMhohMGI0t0Fz06tiO/ZmFffeYu33367VFfM9evXuXr1Kt26datyrZp8rBzsCZo8koyzVzDmaqmLLXX5/77tAnyoPW6QSaJ6+PDhpkmMlZUV7733HosXL2bAgAEABAcHl9qng4ODWQp2WHgwqfGGXSaT8eGHH1KvXr370n9+2Ni1a9ews7OrFmMghCAtLa3ILEiNRoNGo8HBwQGZTIa1tTUffPCB6f2+ffvi5+dXyQVEGUggk6h0mKnBYECv16NU/n8S0cmTJ5k2bRpffPFFgRmkXC4vVjbYysGexm/MJHDMAFJPnMOg0eEQ7I9Hu+YsX7Oaa9euFZuGf+nSJbZt28akSZPIzMwkPj6+zMeV79IQQmBlZVXsU0F+WK5CoSh0jUhyGXUnj0R9O44bP/+BJiU9b01AaYV9HT9affkajsEBedtKEm5ubqZ9dTodZ86cQa/X8/777wMwYMAABg0aVKbxW/hv8kAY9vspytSqVSs2b96Mg4MDTz/9dLU8tioUCoYOHVqkEfn1119Zu3Ytq1evLpTiL4TAw8Oj3JrqhZDy/NsymcBoLLthzy9hGBgYaPKRX7hwgePHjzNy5EjTbLpu3bq88MILRSp2luROkWQynMNCcGqQN2PNzc3l2q1IRo8ezfjx44uVAzhz5gzLli1j4MCBGAwGDAZDgQVsIQQnT54kJyeHzp07F1pDyZe08PHxoVevXkXqtOfm5jJ58mSaNGnC22+/Xeh45LY2NHtvNgEj+xKz6R/02SocQgIJGNEHW19PpGLWbRQKBW5ubvTo0YOuXbsCUL9+/WLPkQUL8AAY9vvNsGHDqr3PkhZCExISuH37dqHZ6aFDh9BoNPTo0aPCvmaNRkNqaioebnZYIUcmM5QrgufkyZOkpqYybtw4k3F0dXWldu3aBYyup6cnU6ZMqdAY4f+NZWJiIv/88w8DBw4s8QllyJAhdOnSBU9PT2JiYoosFv3jjz8SFxdH+/btC71Xq1YtduzYQXh4OO3bty9S3kIul9OxY0eCg4PRaDRs376dkJAQkytNkiQkpRUebZvi3qZJXoRMGdZcJEkiJCSExMREU6RRdbmQLDy4lGrYJUn6BRgMJAohGt957R3gKSDpzmavCSG23nnvf8BU8jJenhNCVEwdq4ajUqk4cuQI9evXr9bKMw0bNqRjx46V/nILIUy689bWeWGMt27dYteuXQwb2gc/FxmSlJe0ZDQakcvlGAwGVCoVtra2Rd54OnXqhFarLfBU4+/vj7+/f6njMRqNqFQqoqOjWbp0KdOmTSvVneTr68uIESOKDLm8m7ulFWrXrl1kaN9rr72GRqMpNBuXJInmzZszd+5ckpOTi13AVyqVvPjiiwClFl6XJKnMaxY6nY5z585x9OhRzp49C0Dv3r0ZPHhwmfa38N+kLDP2JcA3wNJ7Xv9CCDH/7hckSWoIjAMaAbWAXZIk1RNCVNhRK4Rg27ZtWFlZ0bt37xoT+aDVarl16xbe3t5VYtiNRiNarRalUlnANTB69GhGjRpV6DzcW8yiNHJzc1mzZk2BYrp+fn706tULd3cPMMqQSZCXjZo3a09ISGDjxo306dOHkJCQQm1WJoZcpVKxevVqsrOz2b9/P8OHDy/VsCuVyqIjfMqJJElluomUtS9ra2uzPenZ2toyc+ZMateuza+//orRaOSff/4xS9sWHl5KNexCiP2SJNUpY3vDgNVCCA1wU5Kka0BboMIVHwwGA5999hkBAQEVTJWvGpydnZkwYUKVKQIePnyY9957jwULFtC48f8nrhQVMlmRm52VlRVNmzYtEItub2+f5781qkEtu+NqFyatdScnJ5o1a1bqDLkiKJVKmjZtiq+vL1OmTKmyqlTFca/MQ2Uo7/5CCOLj45HL5Xh6ehbYX61W07ZtW27evGlSfOzWrRsjR44sdx+VGaOFB4vKWKVZkiQ9DhwHXhRCpAF+wJG7tom+81qFkclkPPfcc0VW4bmfSJJkcmFUBS4uLtSuXZuYmBjq1KljdhlXhUJRQvioBNyZsd+lbePg4ECHDh3MOo58lEolbdu2LfZ9o9HIrVu3sLe3x8vLy+z95xd/btq0aYXDVCuK0Whk37592NraMnTo0ALvWVlZMWfOHJYtW2ZKbru3sEpZuHz5Mk8//TQ5OTlMnz6dKVOmlLkAiYUHj4p+st8DIUBzIA74vLwNSJI0TZKk45IkHU9KSip2O5lMxogRIwgKCio0y9BqtaSlpVW78mN10LhxY5577jleeukldu7ceR9G8P+umPJWRyqJ/OLkFy9eLNfCrFqtZurUqYUzZs2EwWAgMzOzTBWdzI1MJqN79+507Nix0Hs6nY6FCxdy5coV5s2bx+uvv8769evL3ccPP/yATCZjwIABvPvuu5YKTQ85FTLsQogEIYRBCGEEFpPnbgGIAQLu2tT/zmtFtbFICNFaCNG6qMo8ZWHTpk0MHDiQmzdvVmj/8pKUlMSVK1fKFQN98+ZNoqOjK6QPExAQwKuvvlriTLYsYyh/tSEJpP93xVRG2yY9PZ2LFy+aDInRaOTq1avlrhBkbW3Nc889V2XqhM7OzjzyyCMFNOCrC0mS8PHxwdPTEyEEly9fRqVSAXk+9vDwcG7cuMGNGzf4/fffuX79eoX6qVu3Ln369Cnw2sqVK/n6668t4mAPGRVyxUiS5CuEyC9ZPwI4d+fvjcBKSZIWkLd4Ggocq/Qoi6Fu3br079+/2qorXbhwgatXrxIQEFCmqBSj0ciRI0ewt7cvcoE13/Dn5OSg0+kKLcS6uLjw6KOPVmisQgg0SamknrqAPisHa3dXXFs2xMrJoXT/qgR5M3ZBZWfs169f59ixY/j4+ODq6opMJmPQoEHl9vEqFAqGDx9e4XGUhiRJNSK1XqfTcfDgQYYOHYqdnR0Gg4E9e/aYniR27dpVYSN869YtDh48WOC1Hj160LhxY/bv31/psVuoOZQl3HEV0B3wkCQpGngb6C5JUnNAAJHA0wBCiPOSJK0BLgB64JnKRMSURrNmzYpVWRRCkJCQgFKpLJDJl09SUhKnTp2iXbt2pUZzCCE4deoUBoOBoUOHltm3LpPJ6NevX4kG4+OPP+bcuXPk5uYybNiwAsktFcWoNxCzZS8XPl5E6ukLpGhUyBztCW3bkhbvz8a9bTN0ej2HDh3C39+f0NDQe1rI87FLZnDFNGzYEH9/f5OapCRJZQ7VFEKgUqlM+/wXFvyUSiVDhgwxXZM6nY6PPvqIlJQUIC9efuHCheVud8qUKTz55JOsWLGCV1991RTW6evri62tbY24qVkwH2WJihlfxMs/l7D9h8CHlRmUOcjNzeWJJ54gLCyMBQsWFHo/Ozub2NhYcnNzyxSml5iYiFarxcPDo8yLTpIk4eLiQm5urikW/F6eeeYZMjIy0Gq11KlTB61Wy9mzZwkICKjQIqEQguQjpwif8TbquCRuk8sF1LhnqlHuOkhuVDzdNv2Ilb8XcXFxhXRZ7owcJBkyM7hiSivPV9qxbN26FWtra4YMGVLoPY1GU+WL2NWNJEkFPncbGxtTsfG7tykvTZo04ciRI6b9/ws3yf8yD0XmqU6nIyoqCk9PzwLiSY8++ig+Pj5F7lO7dm0aNmxIVFQUXl5eRV7odxu03r17I4QosF1ZQuQiIiJ48cUX+fDDDwtFlOQXEbmbjIwMjh8/DlAxw24wcP2nP8iNT0YguIIae2Q4I8caGVlXb3Fr9WYavzGT0aNHlzBTk+fN2IV5F0/LgyRJNG7cuMgx6nQ6ZsyYgZ+fH++//77p/Jf2mZgzrLE8VLTfe41wRW+yFmP+36LGxzsJIUhOTi5RVTErK4sdO3Zw9epV02sKhYKJEyfSq1evYvdbuHAhn376abGGKyUlhWnTprF9+3YUCkUhESi1Ws3zzz/PihUriu3D2dmZZs2aFekOKgpHR0ceeeSRArHr5cGo1ZGw91hemCKQhQE7ZMSi5Sa5YDSS8M8REAKlUlmMYb8r3LGYGbtWqyUxMbFKoyskSSIsLIx69eoVMkoymYyGDRsWWuyMiIhg586dxV4vubm5zJkzh6VL7823q1piY2PZsGFDpeuxGgwG5s+fX/qGFv7T1PgZe/7jeL9+/Yot7ODk5MSQIUNwdXUtc7uSJPHmm29iNBqLda3kFwHOj1C4F6PRSHp6OtnZ2cX2ExwczBdffFHgtezsbL799lu6dOlSKMRNJpOVazFYr9ej1WqxsbHJOw6BqTapDKiFEgfkaDCi507xDGOesS5q/pZ/TJfPXuTihVNcvSUnISmHjh074unpaUrIioqKYseOHQwdOrRMkgHmRqFQ8PLLLxd6PT09nR9++AFHR8ciwwfvLvKc///JkydxcHCgQYMGQF5ZxMWLF9OoUSM6depErVoFq0qp1WpWr15NfHw8zz33XJmSqQwGg8klV17y14u0Wi1CCMLCwsjNzcXGxqbcbVn4b1DjDbskSbRt27ZEP7hCoSi3cZEkyfRFLg4vLy9Wr15drOG3t7cvV4WgfHJycti0aVOxxqc8nDt3juPHjzNmzBicnZ2RKa3waN+c21HxSAKaYc8pcrBGlqcBLpPh2alFITVBIQQGg4HNmzezevUqnOw1hIW6o9PasGvXLlasWEH37t2ZNGkSTk5O+Pj40LVr1yrJQi2JfBlduVxe5OcSGhpKQkIC58+fL/Lc2tnZ8dNPPxUoV3j9+nXc3NxM10NcXBwbN24kKyuL0NDQIg37vn37uHLlClOnTi2TYQ8ICGDs2LGlXitGo9FUqu7udkeOHMnp06dNn9Pzzz/PJ598YnGvWCiSB8Kwl2aAq7LvkiQDylKOrig8PT3ZuHGjWVT6vLy8aNCggWkBUVLI8Rk/kMgte5BycrFFTkf+v76pfaAvdSYWlgTW6XR8//33nDt3jmeeeYa4qGOcPH6Y2DiJNm078/jjj7Nz505ef/113nvvPdzc3CrsLqoMN27cYM6cOTz//POmDEy9Xs/p06fx8vLC39+fTZs2FWts7/3M5HI5w4YNK3CTaN68OTt37sTa2rrIz8jFxYWFCxei1+uLfUpMSUnh8uXLtGzZEhsbmzL7uHU6HUeOHMHT07PAMej1ej755BNiYmJYvnw5J06cKLTmY8FCPjXex/4wIpPJcHNzM4thr1WrFp07dzY9lkuShEvH5rjMegSnsBBkyrywNrmdDW5tmtB20fs4NyhYgUcIwa5duzh9+jQTJkxg+fIVREScp2f3JnTq1Ja0tDS++eYbWrdujbe3Nz/88MN9y1y0srLCw8OjQDRPdnY2zz33HIsXL0Ymk+Hu7l5mN4UkSdjY2BSQ6lUoFHh4eODo6FjkjVsmk+Hs7Iy7u3uxT3Pbt29n2rRpREZGluv4lEolQ4cOLeSO8/Pz49tvv2XNmjWm0oQWo26hOGr8jN1oNHL69GkaNGhQTGhe1REZGUlGRkaxkRk1FXdPTwZ99D/UsxJI2B+OLj0LG28PvLu3RenmXMggpKens3LlSmbOnMmSJUvo0aMHjerZseHPVVy6oWfEyHEMHz6c+fPnM2PGDL7++muuX79eJU9SMTExrF+/ntGjRxepphgQEMBPP/1U4DUHBwc+/vjjQi6T+0nv3r3x9vYut+6MJEl4eHgUej0wMJDdu3czd+5cJk+ejK+vb4mGPScnxyRD7OjoiEqlIjc3F4VCYcopsPDw8kDM2M+fP09WVla193vt2jXOnDljNi0aIQTHjx8nPDy83GFr5ZEGyH/st/P3oc74wYTOGI9dt5bEZqYVuf/t27exsbEhJSUFZ2dnGjZsyLsffI2vryt1QwJYvnw5N27coF+/fhw8eJBu3bpx4MCBSsW3F8e1a9f46aefipUcyD+2u42aQqGga9eu90UOoDi8vb3p3bt3kZORisg8+Pv7U6tWLT7++GNeeumlUmfrL7/8MkFBQUydOpXU1FRGjRpFUFAQXbp0KRA9ZuHhpMYbdkmSGD58eJGzmKqmc+fOjBgxoshSaBVBCMGCBQuYP39+uaMjkpKS+PPPPylJMK0o8o1gREQEe/bsKdKFcvXqVQIDA00Vgnbv3k2/3i3499h1/t51mGHDhrFp0ybCwsK4fv069evXL7fWS1lp3749W7ZsoU2bNlXSfk1g586dTJ48mYSEhDLvExgYSFxcHNnZ2QQEBJS6/RtvvMG0adNQqVQkJCRw6NAhtm3bhlar5dSpU5UZvoUHgBrvipEkqdiqNVVNUX7a3NxcMjIy8PDwKNI9I4QgMzMTg8GAq6trgZmVJEm88847AOWWTM2PBqloslDr1q1p1KhRkTep/CiT3NxcrKys0Gg0eLrYcyMyjRyVBplMhtFoRJIkhBCmakpVgbW19X0Jn6xO9Ho9ubm55ZqxL1myhKFDh5Kenl7kZ/jLL79w5swZ0/8TJkzAy8uLixcvAnnXW7169Qpd01999RWXL1+udqliC1VLjTfsRZGbm8vly5epU6dOpar23IsQAp1Oh0KhKNbwbtiwgc8++4xVq1YVobGSx//+9z9SUlJYvnx5gS+hJEnUq1cPg8GATqcrser9vXh5eZn0uCuCs7NzsecqODiY/fv307lzZyIiIujduzdffv42Ux7rzs0YiUOHDtGxY0fi4+Px8fHhxo0bRZaXKy/5haXLcx6K48KFC2zfvp3x48ebpaoSQHR0NNHR0TRv3tysMeMDBw6kf//+Zbq5a7VaPvvsMzw9PVm3bh3t27enRYsWhbabNGlSgRuFwWBg165dGI1G06Tggw8+MBX0yOeZZ54hPT2dNWvWmOfgLNQIarwrpigyMzM5fPgw0dHRZm339u3bjBw5km3bthW7TePGjXnkkUdKjN/u06cPgwcPLnbB9fvvv2fy5MmkpqZy69atMmmAS5KETCarkkiIoKAgMjMzCQkJMUnGPvnUdNLS1YSHn6Zt27b07t2bdevW0a1bN5Ohr+xY1qxZw9ixY8vtXiqKf//9l3nz5pnVRXT16lUOHDhQbIJaRclXkizq/BmNRqKiokx1U3U6HV999RX79+/npZdeolOnTowaNarQfnK5HIVCYfr5888/uX37Nj4+PgghmD9/PklJSTz55JMMHDiwwH4PUmCAhbLxQM7Y3d3dGTt2bLldNBqNhqioKPz8/IoMNbS2tiYgIKDEp4BGjRrRqFGjYt+XJIkRI0aUOA5JktDr9cTFxbFv3z4GDx5slhlwRXFzc2PgwIGsW7eOxx9/nOXLlzPlicn8seEUl69cY+KjU1iwYAFdu3Y1afKEhYVVul9XV1f8/f3NsoYxcuRIWrduXexTVEVo06YNDRs2rLYokuTkZN566y2srKx44403sLGxwcbGhi1btvDxxx+zYcMGUlJS0Gg0fPLJJyW2NW7cOMaNG2f6v1GjRkyfPr2qD8FCDeGBnLHL5XLc3d0LxB6XhE6nIzw8nI0bNzJixAgOHTpU5Hbe3t589913dOrUCaPRWO4UcIPBgEajKdV3Gh8fT1xcHF5eXgwcOLCQUNmpU6dYuHBhtUUCyeVyk9b59u3bmT59OmfOnkNvENStW5d///2Xp556CiEEhw8f5rnnnjOLa6Jfv358/fXXZtHTd3V1pVmzZmYNiXVwcMDb27vK6trei1arJTIykgYNGpgmLXK5nNatW7N27VoOHz7MvHnz6NevX7WMx8KDywNp2ItD3FEivNew6nQ6zp49i5WVFS+88EKxM+67Q+kSEhJYvnw5Z8+eLbNxv3DhAitXriQzM7PE7YYOHcqsWbNwcXEhKCiokOzssWPHWL58ORkZGWXq1xzY2toyadIk4uLiiIiIYOPGjTRu3JiQkBBOnTrF+fPniYyM5L333qtQvHhRn01RoYv/ZXx9fVm/fj3Tpk0rdOOUJAk7Ozsee+wxevbseZ9GaOFB4aEy7EeOHGHChAmF/Ky2traMHTuWgQMHMmXKlDItrtnZ2WFtbc2pU6dKVJa8G1dXV+rUqVPqk0SbNm145JFHit3uscceY9OmTWZbBCwLKpUKZ2dnvvnmGxwdHXFycuLo0aPs27eP+vXrExERwcsvv4yfn1+FDHFUVBQTJkxg3759Zh+7wWDg7NmzxMbGmr3t6iQ/C9bi87ZQWWq8YTcajRw4cKBMboniZn+SJOHg4FBm1w3kRZEMGzaMQYMGldkH7O/vT48ePSotFWBnZ4ePj0+1fsHPnTvH5s2byc7OZvv27bRv397kgqhbty5Xrlyp9GJ1VS3+ZmVl8fTTT/P999+bvW0LFh5EHojF09jY2AJ+x+Jo164dK1asMJvxcHJyynMdCIFenYtBo0VmZYXcxhpJXnEjlV/9Jz+Coazs27eP1NRUhg4danajX79+fVxdXU2ZsRkZGYwePZqIiAj279/PrVu3WLZsGR988EGpfWdkZHD9+nXCwsJMN7mAgLwM1qow7Pb29rz77rtlStyp6eRfG0qlsty5DhYs5FPjr5z8zNO7wwuLS8k2d0igEAJteiaXv1nO353Hs7l+f7Y2G8qZt78iJzK6win1ycnJjB49usQCHUWxdu1afvrppzK7hu4lOzubmzdvFrm/i4sLoaGhnD9/npSUFOzs7IiIiODcuXNkZmaiUqk4e/ZsmZ6c4uPjOXr0aIE1gqoM17SysqJPnz5m1a7R6/VERkZWu5RFTk4Oo0aN4vr169Xar4WHixo/Yy+qpmVMTAzvvfceU6dOpV27dlXWty4ji5MvfMSNlZvYr0shHT0Nk+zI+TiGpIPH6bDkE+zrlN/nrFQqqVevXrGFQ4rjrbfeQqvVlsuldDdXrlzh6NGjjBs3rli52ZkzZzJw4EDOnTuHQqFg+vTpuLq6mhQNyxL6FxQURKdOnUwRQjVtcTR/VpyWloZGo8HOzg43N7cCT0/Z2dls27aNNm3a0Lp162obm0wmo27duhX+jPO5evWqKT8gLCyM9PR04uLicHJyui9yyxaql1INuyRJAcBSwJu8+jyLhBALJUlyA34H6gCRwFghRJqU9y1eCAwEVMBkIcRJcw5ao9EQFxdX5bOp+L8Pc2vVZlJ1arIw0B1nDpBJoNGapIMnuf7LHzR5+1kkRfncIs7OzkUW2C6NkmqgGgwGMjIySlxLqF+/vkmOtijylQUzMzM5evQoXbp0MemzazSaYotb3ItCoeCjjz5CJpOxbNmyUg17dnY2p0+fJiwsrMyFO4QQZGVlIZfLy1ToIn+fnJwc9uzZw9atW8nMzESpVJKbm0tAQAAjR46kZcuWKJVKHBwcGDRoUJlLGpoLOzs7Fi5cWOl2vv32W+Lj49m7dy/z5s1j/vz5pKamIpfLTRmsFh5eyjJj1wMvCiFOSpLkCJyQJOlvYDKwWwjxsSRJc4G5wKvAACD0zk874Ps7v81GcHAw69atq9L4YiEEkas2Y9TqcECOEcERskhAhwEBRiO3Vm+h0dynkSkqr6teEXQ6HdeuXcPb25vExESeeOIJ5s6dy7Bhw4rc3t7evsxGMCsri1u3buHu7o5Go2Ht2rUEBQXRuXPnUveVJIknn3yyzKGMOTk5XLhwAS8vrzIbdp1Ox1NPPYW/vz/z588vtR8hBElJScybNw+DwcDo0aNxcnJCpVLh6OhIZGQk3333HU2bNuWZZ57B1tb2gdJP0ev1BVyDn3zyCQaDgaZNm5KZmUlMTAwXLlxg4MCB3L5922TY9Xo9er3eJHNhofqoqEu1LJRqGYUQcUDcnb+zJEm6CPgBw4Dudzb7DdhLnmEfBiwVeVfZEUmSXCRJ8r3TjlmQJKnSj6qlIQxGtBl5TwRKJHriQgo6dAgUd6qF6jJzMOp0wP0x7CqVin379tGyZUvq1KlD//79CQkJMUvb9evXZ8SIEaxatQpJkggJCSmz60iSpBKLiN+Lp6cnEydOLORyKw6DwUBCQgJt2rShTp06ZdonOzubDz74AH9/fxo3bswff/yBVqvF2dmZpKQkfH19efLJJ1m1ahWLFi1i5syZZlP1rA6WLl1aQARs1KhRrF+/Ho1Gg7e3t8mlee8T1/fff8+FCxeIiIjg2rVrlR7HtWvXqFWrllkSxWJjY7G2tjZL+cWzZ8/SpEmTSrcDeTLiYWFhlV7czsrKIjg4uPQNK0C5prySJNUBWgBHAe+7jHU8ea4ayDP6UXftFn3ntQoZdiEEUVFR+Pj4VLkxvxtJLsM+0BckQMBxsslATzPssbpj2O38fZBZV35MKpWKTz75hLZt2zJo0KAy7+fg4MDIkSNxcHDAzs6Ot99+u1z96nQ6VCoVDg4OhSJddDqdaTanUCgqXZu1JGQyWZmfJCCv5ui2bdvo0KEDnTp1KnV7IQSbN2/Gzs6Oxo0bs3z5cp588kl8fHxMYmxnz57l559/ZsaMGXz//fecPXuWFi1aVPv6gFqt5p133uGZZ54p1xPDlClTCvz/wQcf8M033zB//nwaNWqEjY0NI0eOJC4uroBkxrPPPktycjILFy7k/fffr/T4P//8c4YPH26WCcYff/yBp6cn3bp1q3Rbzz33HF988YVZPs+XXnqJ999/v9Jhzbdv32blypWVHk9RlNmwS5LkAKwDnhdCZN59goQQQpKkcoWISJI0DZgGlHgBGwwGJkyYwMKFC2nVqlV5uqgUkiQRPGkEUWt3oM9R0e2uuqESEpKVguDJI5CbwbBrtVpOnjxZbn+uXC4v0e9eGlevXmX//v2MGjUKT09P0+vu7u7UrVuX8+fPM3ny5HLNTIQQ5ObmIpfLq+xGbGtrS+/evYtdAL4XnU7H33//zcSJE1myZAkvvfQSJ0+e5Mcff8TBwYHs7GymTp1Kv379+Ouvv+jRowdbt24tUkWxqtHr9Zw8edIkAlZROnTowJdffplXKtHFhbVr13LmzBlq1apF3759C2xbVIBCRTFnmKaVlZXZwnrNUYYyn/watpVFJpOZ7bwX4u7QweJ+ACtgBzDnrtcuA753/vYFLt/5+0dgfFHbFffTqlUrURx6vV588803Ij4+vthtqgq9Olecm/e9+N2+mVhBPdPPSquG4vCkV4QmNV0YjcZK92M0GkV2drbQaDRCp9OJ69evi4yMjEq3mf9THCkpKeLo0aNCpVJVqq+7yczMFCNGjBDz5883W5tqtVr8/fffIioqqkL7x8TEiHHjxomtW7eKGTNmiKNHj4rHH39crF+/Xvzvf/8Tf/31l5g4caK4dOmSGD9+vNizZ4948sknhcFgEELcdS4NBmHQ6sp0biuK0WgUWVlZQq/Xm73t4vozGAxCpVJV+HjuPh9qtVro9fpKnaP8/TQajdBqtZVux2g0ipycHLOMKb8tg8FQ7rbubsNoNAq9Xm867xUZG3BcFGNTyxIVIwE/AxeFEHeHcmwEJgEf3/m94a7XZ0mStJq8RdMMUQn/ulwu55lnnqno7pVCbmNNgxeewL19c24sWU9OZCxKF0fqTByCb9/OWDk7muXOLUmSyRWRnp7Ojh07aN26daWqCP34448mbZfiZs5ubm60bdu2wn0UhUKhoEmTJmb1HSYkJDBnzhymT5/OzJkzy71/amoqtra2ZGZm4unpSWRkJA0bNmTvvr1cungJW1tb0tPTiY6ORqlUYm1tTU5Ojqm4SG58MrfX7+DWys3os1U4hAQQ/MQofHp3RGFrPp12+P8s6epi8+bNvPHGG0iSxPz58+ndu3e528hXn7Szs6Nhw4acOXMGFxcX4uPj2bZtW7lrJrz66qvs3r2b2bNn88MPP+Dk5ET37t2ZO3duudqJjY1l+PDh+Pv7c/36dZRKJbVq1SIwMJBvvvmmzO2o1Wpef/11zp07R05ODllZWQQGBpKUlMTXX39d5u/QjBkzOHPmDFu3buWpp57CYDAQHR3NrFmzmD9/PpAnJ/Lyyy+X6ziLoizPTJ2Ax4CekiSdvvMzkDyD3keSpKtA7zv/A2wFbgDXgMVA+b+JNQi5jTU+PdrTYckn9Nr9G13/+o7aYweidHGqEv+ro6MjQ4YMwdramg8++KDC+idZWVlkZGRUSV3SkrC1teXdd98tVbq4PPj6+rJ48WJGjx5d7n11Oh1XrlwhLS0NJycnUlJSCAgI4PKl87RubE3dQCt8anmi0Wiwt7dHr9ej1WqxtbVFJpORG5fE0ade5+Tz80g6fJLwM6dZ/ud6Do2fw8XPfsKQW7qWfnkwGo3Ex8dXW4TKZ599RseOHWnRooXJuJSXJk2a8O677/LUU0/x3Xff8e+//9KvXz8uXLhQ7kpbFy9eZNWqVZw6dYrPP/8cpVJJYGBghb4Hixcv5ty5czz22GP07t2bzMxMWrduzc2bN8vVTnx8PL/++itXrlzh4sWLnD9/nvbt25OYmEh2dnaZ2+nWrZvpnNy+fZs+ffpw8eJFkpKSyM7O5vnnn2fFihXExMSU91ALUaphF0IcFEJIQoimQojmd362CiFShBC9hBChQojeQojUO9sLIcQzQogQIUQTIcTxygxQ3KlqVN0G6l4kSUKSyzAYDMTHx3PgwAF27NjByZMnyc7OLlJVsiLI5XL8/f1JSkpi48aNxMfHV6idOXPm8M0331SZnzs7O5uzZ8+iVqurpP27USqVtGvXrkLrCTqdjrS0NHQ6HdbWStJSk3EglqYBGmRpN3GxV7M/YiXDJrUnKu46Xl5eREVF5S3+GY1cXfQ7cdsPIPQG1Bg5h4oUtBhy1Fz49CdSws8W2W/+I7FRq0OXrcKg0SAMpV8jmZmZdOnSpVozT1u1akWzZs0qvH/t2rUJCQnho48+on379owaNYqhQ4eWO7FLCMGyZcuoX78+MpmMM2fO8Nprr1V4XGfOnEEIwa+//srXX3/Niy++SPfu3cu9luXo6EhYWBixsbGkp6fj5eXFjBkzyj2e5s2bm/5u06YNY8aMMQUluLi4MGTIEG7evGmW/JwaLylgMBiYOHGiqXbj/UIIwZUrV3j99deZPXs2GzZs4ODBg7z88suMHj2an3/+2awJU127dmXbtm00bdq0QvvnV8apqqiOhIQEDhw4QEpKilnbzcrK4tChQ+WaCZWEra0t48ePZ/So4eze9gdje3jx8Ucf0raRN1HJgm9+PYTCQU50ZgTfL/mM1v1qsX3nZnr36Y0+R03kyk0IgxEDgghyqI8t1sgQCAyqXG4u31CksdbnqLm5bAP/9H2CzWED2NpsGGff+5rsmyVLUSiVSoYMGVKt7pjjx49z+vTpCu9/69YtxowZg6urK2vWrOH06dNs3bqVS5culbstg8GAj48PMpmMZs2aMW/evAqPq1mzZvTr14+hQ4ei1+uZP38+x48fL1VW+17S0tI4ffo0b7zxBv7+/iQmJvLdd99VeFwA4eHhrF+/nrNn8yYG6enpbNiwgaCgILPUeK7xhl2SJFxdXas11PFehBAcPHiQt99+m0aNGjFz5kwaN26Mh4cHTZo0YfTo0URFRfH6668XW+ZNo9GQnp5eZm13Kysr3N3dCyVh5ZdOS0tLK/PYExISSEhIqPATxfbt23n11VcLfCECAgIYM2ZMoSIh+WP8+uuv+fbbb8vd5/Hjx5k2bRqnTp2q0FjvRggBRi22hhhGtIWs+DMkxkfTtXN7Nh0XrP37Gvb2rsRGgDrKne7DGrN+wxpsa6eA3xWSMi6Tm5h349IiiEbLCbK5hJpEdCAEuXFJiHs+U71Kzdl3v+bY9LfYuW8vK6LPcfTyec598AP/TnqF7BtRxZ4XOzs7FixYUG0FvV999VWOHDlikmWuCOfOnUOn0+Hh4cGHH36Io6MjW7dupU6dOuWKapEkiU8++YTPP/+cNm3a8OKLL6LX601Vz8rLU089RVxcHNu2baNZs2a4uLiwb9++ciee+fv7M3PmTNavX4+bmxtNmjTh6NGjeHt7l+sGvGXLFurWrcsbb7yBlZUV27dvJyAgAC8vL5ycnPjmm2949NFHK3Ss9yLdbxcHQOvWrcXx40V7bO4e3/3QHBFCcPPmTebOncvjjz/OxYsXOXPmDK1bt8bDw4Pz589z+/ZtJkyYwMmTJ1Gr1bzxxhuFwquOHTvGqVOnmDBhQqXuyOnp6QwePJjevXvzzjvvlLq90WjkscceQ5Ikli5dWiAUzWg0otPpOHjwILGxsYwfPx6FQoEwGjGoctGp1Egyid9WrWLT9q0s+e03PDw8CvVhMBhYu3YtLi4u9OvXDyEEzz//PDKZjAULFiCTyUhLS2P58uX079+/xPJ1GRkZHDt2jLZt21a4ULkQAoQeVNGQFo7IuYHBIIi4JfHed3vx9auNjY0NCQkJGI1G1Go1jz76KH9tXIe9sxV9pwShdFEjyzGSM2QvIkqFIO86zMTAEbLoiwsSEsFTRtHupw9NfUuSRPzuf9k3dDrZqhx2kE4vnNlLJr1xxlamoMHzk2n+0YvIlPc/Aere739FvmOl2ZDytllce+Zq5361VVZbW9b2JEk6IYQo0t/1QIiA3U/0ej1Llixh4MCBXL16leTkZN577z2OHz9OfHw8AwYMwNbWli+++ILp06fz888/c/r0aTp06FCgndq1a2NlZVXpknJ2dnZMnz6devXqFfn+vTdCSZKYMGFCken9165d4/Dhw+zfv5/Y2Ni8Isl6A9F/7eL64jWknjwPMhmhXVrx9fQ5uDm7FNmnwWDg999/x9fXl379+iFJEp999plpDJDnuvnll1/w9vYmKSkJW1tbmjdvXmhMzs7O9OnTp0LnJu/YjaCOhbTjiOyrGA16ktXO3Ex3J9Vox8xZzdBq8wpEq9VqsrKysLa2Zu/evbw2901q1w4kPimGmIxzZIjLyAf6oV98FcmYN04n5PS6Y9Tldja4j21FTM5RhDBgZ+WJi7IOkas2Y1DlosKILTKskaEgb9ZvaxTcWrONRq9Nx9q98PnMj7nv0KFDmeP0K4O5orrMibnaM+e4auJ5Kokab9jvNzk5OVy5coWePXuye/du3nvvPT7//HOcnJzw9vbm559/plevXowfP57169fTp08f/vnnn0KG3dvbu9xqjkWhVCp59NFHi31///79/PTTT3z00Uf4+/sTHh7OqlWreO+99wpdWI6Ojvj5+fHee++hUCiwtrbms+nPc3DVesbm2HOQDNLQE7QxlraHTyP/4R0CRvQt1I6VlRU//vijyW1UVMJL3bp12bRpE05OTjz22GO4u7vz888/V/p85COEAXITIO0kIvsyRr2W1FwHItPdSVHbY2vnRIOwQDIzM1m6dCnNmjUjMzOT5cuX8+abb3LixAn0ej2Ojk44OjoRkBtEYnIrbg2tR8LxpehOXQejQEJCAcjcrHF6tSkJYRe5emwnqmwNXt4e1AtqTFZmXkESG2TkYkSHwAD/L0WRkYVBqy3yOFQqFW+88QYrVqyoFsNu4eGkxht2g8HA+++/z9SpU+9LIYWoqCjs7OyIjo4mNDSUc+fOYW9vj6+vL/v372fSpEmsWrWKV155hYSEBOrUqcO+ffswGo33pVCCXq9Ho9GYfPn5BbaL8u37+voWKL+XeTUS/3/OYpWTi4Q97XFCjZEDIoPGSSlc+mIJXp1bY+NVULtDkqQCESv5krgeHh4mY69QKPD390cIwSeffFJIh2Xbtm2cO3eOZ599tlxPNUIYQJsKaScg6xIGnYpMrT0307xJUjtiZe1ASGgAfn5+2NnZER4ezv79+6lfvz4Gg4GWLVvy119/AbBy5UqaNGmCQqHIEwHzD8bXO4BE/yZcX/onyZv2oM3OQgpS4DA1FJtetdDL1WxfE0HrrkFo3HJI013FOFKJtEWBnUrgiRXbSCMIa2zvLGnZ+Hggtyk649DBwYFFixY9FEVDLNw/avziaf7CpbmjL8qKSqXCxsbGpKmSnJxMQEAAZ8+eJS0tjYyMDJRKJRqNxjRT1Wq1GI1GDAZDlYdpXr16leXLl5OTkwNAz549WbVqlckwtG/fntWrVxMSEmIKHS1KVU4IQeLeYxhuxZM/H7dG4ia5eGGFDEgNP0fGxesYDAa0Wm2xx7Zr1y769OnDX3/9VaQPt0GDBoSEhBSY+UdERPDPP/+gLWYme+9YhTAitGmQtA9xawXG1BNkqOB8cgBHY+qQovOidlA92rdvT2hoKHZ2dkiSREpKCtevXycrK4uBAwfi7u7OmDFjUKvV/PnnnwXK/0mShJWVFX5Nwuj8yVy67l9B/U3zcf5yPLa9apm+PcIoiL2VhtGYF+KobOGGdRdvJCQ64MhI3GmJAzIkJLmcOhOGYOVYtDaOXC6ndevW1RoVY+Hho8Ybdrlczh9//HHfigP4+vqSkZGBn58fkZGRtGnThoMHDzJ8+HDGjRuHXq/H2dnZVIQ4JSUFDw8PIiIiWLduHRqNeRNY7mXPnj3Mnz+f5ORkIM8Y3R3mmP9/REQEmzdvZurUqSb/973oslUIw//P7C+hJgUdLXFAAowaLQZ1LuvXr+fRRx/l2LFjqFSqQu3kl9n7888/y3xjmz17Nr///nupC8tCCNBlQPJhuL0ckXKELLWRi6n+HI2pTUKuF/6BwXTo0IH69etjb29fYH0hJCSEadOm0ahRI65cuUL9+vXJyMigf//+TJo0CRcXlwL95e8rl8vx8PKiWbOOtAztg1z2/22Om9mBNt2C2b/lEqpsLZK9HNchTVA42iMh5Rl0JCSFnFqDuhI6fRySpWC1hSqkxrti8kWM7heenp7Y29tjbW2NSqUiPj6ecePGkZ6eztGjR+nRowdTp05l48aNtGrVikOHDtGiRQvs7OxwdnaucnfMI488Qo8ePUoNkVq4cCG7du1ixIgRxfr67fx9OGmvJz5HyyEyuYkGGyTCyaY9jti6u2Dt4YqblQ4nJyd2796Nh4dHISW/kJAQVq1aBZR9wcjW1rZYoaZly5Zx5cpl3nj1eZSaa5B+GqFNRaWzJirTl5gsZwwye7xreVOnTh2cnJyKPe/16tXjk08+ISMjg7Vr15KUlISnpydjx44tNQonv7yfjY0dZMsAI3q9gawMNTlZGuRyCZlMAkmi9thBNKg/lhu/rifr6i0UDnbUHjeIgGG9sfZyK/a8GAwGbt68iZ+fn1mFqyz8t6jxhl0IQWZmJvb29lVaWKM4rK2tGTx4MJs3b+bRRx/lu+++45lnnmH37t388ccfjBkzhu3bt5tS1VeuXMmkSZMIDg6mQYMGVb4S7uzsXKawwOeee45HHnmEXr16FXkeJUnCp1cHBjZsSfvwswgEXe5+HwnvHu1xbliXnraNadu2LTExMQV89He3ZY5YXMj7/ONiIok8vxfDbR+ElEmu3oqoLB+iM13QClu8vLyoU6eOqYRfWdi7dy8+Pj48+eSTJCUllTlPQpIkrOVOOFj5kK2LRUIiI1VNdkYuPYc3wtZeiUKyw9UxBJdewfj06ogwGEwz9NKuh5ycHB555BFWrFhh1hquFv5b1Pg4dr1ez4ABA5g/f36l0p4rg06nY968eej1eoYPH86NGzf48ssvUavVNG3alIkTJ6JQKPj2228ZNWoUI0eONLscZ2pqKvv376dr165VUq4tLS2NxMREHKKSOTHjXXJuRiPu6HxIVgpcm4fRYcnHOIWFVOhmlZ9iX9aqSvnJRWRfQZ90BIMqFklmQ3S2G7fS3cg1WOPh6UVQUBBubm7IZDI0Gg2RkZEEBASUqO9uNBr56aef6NSpE1ZWVoSHh9O/f/9yleWLyfmX6xnb78S7FMTTtilhrqOQSeWfiGi1WlauXMnAgQMrJcmsUqlYtGgRgYGBdOvWjV9//RWZTIa/vz9jx46tcLsWag4PfBx7hw4dqr325N0oFApeeOEFfvrpJw4cOMCNGzdo2LAh2dnZxMTEkJiYyNGjR5k1axZdunQxm4b03Zw7d47XX3+db7/9lu7du5u9/StXrnDq1CnGPzKOHtt/4tbqLSQfiUBSyPHt05GAEX2w8fGs8BNIVFQUr7zyCk8//TQ9evQodjuTQc+5AWnhoI7DoJeIz/UlMs0VtcEWVzcPGt0x6AqFwjSm1NRU/vnnH3r37l1snD/kXVNubm5ERUXRsWNHkpKSiI2NLbNhlyQJX7vWCCGIV50gR58EGLGWOeNuU5/aTj2QqNg1oFQqmTx5coX2vRutVstvv/2Gn58fYWFhvPfee/zzzz9FZgpbePio8TP2moROp+PEiRO89dZbZGVlERUVRYsWLfD19eXdd9/Fx8enXIZPCGES0mrTpk2JboT8ePp69eqVq9qQEILU1FQgT6a3uPGpVCqTrK1cLr+TvZl3bUhmWCeIiopi7ty5TJs2rciKOCaDro6G1HCE6hY6vSBJ7UJkuhtZOjscnVwICgrCy8sLhUJBZGQkixYt4qmnniI4OBidTkdycjJubm4lPjEJIUhOTmbVqlXk5OSYcgPuXXu4ffs2165do2PHjkWGYAohyDWkodanIIQBa7kzdlaeFZqpVwWfffYZ+/bt47PPPqNZs2Z06dKFli1bFlg8v337Nmq1GoVCUSUTEgsl4+joWOHSfw/0jL2msXXrVnr37s3hw4e5ePEivXr14ujRo9y4caNCs6H4+Hiys7NLVcKzt7evUEUfIQQvvPACAEuWLCnWsNvZ2RWoUylJeYuA5sLf359ly5YV6j/vBmIAdUxe+n/2NQxGQZLKicgMD9Jy7XB0dKZR/Tr4+voWmKEnJSVx4MABhg0bRnBwMFZWVkX6/O9FkiQ8PDyYNWtWweO9h9TUVG7fvk2bNm2KNOySJGGrcMNG7lrgtYqi0+kwGo0olUqzrs14enry4Ycfcvz4cY4ePVrgvaNHj3L9+nWuXr1aIS12CxUnJSWFrKws/ve//5m97Rpv2I1GI3v27KFNmzY4OTmVvkMVcuvWLXbs2IGbmxstW7bEysqKixcvEhsby9KlS2nXrl25F3h79OiBEKLQbN1cGjmSJDF8+PBKt1MRCj4NGpEk2T2vC1DHmdL/hUFLstqJmxnupKrtsbN3IiwsED8/vyKNXatWrdi6dWu5nmDyKYuvv0mTJoSFhZW6sGqu8zp//nwuXrzIokWLsLa2rlS7arWaU6dOERUVxc8//8yVK1ewtbUt1OaYMWNIT09n1apVjB8/vrKHYKEc1Iiap/cLo9HIG2+8wddff11ufWdzc/ToUS5fvkzv3r0RQpCSkkLjxo05cuQIhw8fJj4+vlyqfPkJMEVx5swZvv32W1599dVKFQaWJImRI0dWeP/i0Ol0ZGRk4OLiUvzNzKiBrKuQfgYMOaBwAJfmYF8HdJmQfhKRdRmjPpd0jT03031JUTtgbetEaD1//Pz8yM3NZefOnbRt27aQq0Qul1fpzT5f+ri68Pf3R6VS8ffff9O5c+dKSQooFApefvllU6GLXr16VXt1Jgv3jxpv2GUyGZ9++in169e/30Ohd+/erFixguzsbGJjY/Hw8ECpVPL+++/j7OxsFi2YfFQqFbGxsaYEJ6PRiEajwdra+r5IFdzL7du32bFjB0OHDi10M8vzl+dC/C5EegTHz0axN/wmgb4ujOx7HSvHALb9E05UdAyjBnfldlYAiSpH5FZ21AkOICAgwJQpmp2dTVZWVrVVFKouhMjTc9emZ2LUG7BysOPRCRPQG41s2LChzPLOxWFlZXVfinFbqBk8EIa9S5cupW9YDXh7ezNo0CDWrl1LYmIinTp1onfv3jRq1MjsfbVr147169ebZvQxMTHs2LGDfv36lUlHJL+ik0wmq9QjvVar5dq1a/j5+RWIl/f29qZz587FRytlXoL0CBAGggNcaRDswferjxGXkEaAXHDk1BV2hqfh3TQAWzsnAmr7U7t2bZNbJX/Mnp6ePPLII6XezIQQHDhwALlcTseOHe+7KmhJGA0Gkg6d5Oq3K4jZcQCDVodTvTqETBpJ3SfHMGLEiBpx87bw4FLjDXtNxGg04u/vT+PGjalVq1aV9CGTyQr4dh0dHalbt26Ztdy/+OILbty4wYIFCyoVU5+VlcW+ffvo2LFjgTwCBweHEqo7iTyjTl4xaDdnW5JSc8jVGnB1zsumfPKRnjTrUY+g4LrUqVMHR0fHIv3e+en8pWE0Gk2lADt06FBjDbsQgtOrNnD1xU/RJKVxQmSRgI7EiNtM+N81ciKjafHpK0jWlggVCxXHYtgriK2tLfb29mbzwarVapKSkvD19S3S7+7i4lKu+HUrKytu3LhBTEwMwcHBFR6Xi4sLo0aNwsHBgczMTNRqNTKZDEdHx+IX+IQR9Hml7YQQZKu0rN52joFdQ3GwUyIB3h4O9GjUDRcXtwKz0/y6l8nJyTz33HNlXoyWyWR88sknlX5CKQ6j0Uh2djbW1taVulHqs3JIW70dfWIaCqAdjsSj5TjZ2Gj0RC7fSOCYAXh1bmW+wVv4z1Hqt0aSpABgKeANCGCREGKhJEnvAE8B+bXgXhNCbL2zz/+AqYABeE4IsaOiAzQYDCxatIiRI0ea1YddWYKCgmjQoIHZMky3bdvGe++9x4oVK8zi2pk0aRLOzs7lKjadr0h5d0ihJElkZWWxdOlSTp8+jSRJGAwGnJ2d6d+/Pz179sTGxgaZTPb/NzlJBvK8mbkQsO7vCxw/H4uttYJAXxe83OxR2jqjdHUv0gjnRxqVJ8dCkiSCgoLKvH15UalU/P777zRu3LiQ1n55UMcnkbznmOl/geAiKhpgiwwJbVomp175lK7rv8HWx9McQ7fwH6Qs0yE98KIQ4qQkSY7ACUmS/r7z3hdCiPl3byxJUkNgHNAIqAXskiSpnhCicO51GRBCsHz58iKjIu4XwcHB/PPPP1y4cIGePXsSFhZW6TabN2/OtGnTyhSHXRacnJxMpe7Kyu3bt9m3bx/9+/fHy8sLIQR//fUXq1evpkePHsyePRutVotCoUClUrFx40Y2bdpE8+bNad++/V1RSxI4NwF1HJJk4JEBjRnZuyEyCWxtrAAZOBdfpPudd97BaDQWOfayhIFWRTlFa2trWrZsWSbXm1ar5eDBgwQFBRW62Ri0OowarUkaORdBGgZao8wfPKnHzqJNyzQZdiEE69evJyQkhPDwcAICAujfv79ZjsvCw0mp33ohRBwQd+fvLEmSLgIlKTwNA1YLITTATUmSrgFtgX8rMkC5XM7KlStrVCp0mzZtaNOmjVnbDA4OZubMmRXaNycnh5s3bxISEmJSBJQkqdwFwO3s7PDx8cHGxgYhBFu3buW3337jiSee4Nq1a3z11Vc4ODiQm5uLtbU1AwYMIDo6mt9//73w+XBuBDm3kLIuYWttha3pwUaW955j0Sn/RVVfupuoqCjefvttnn76adq3b1/kNlqtljfeeIPQ0FCmTZtWrnNQHFZWVrRqVTb3SHp6Oq+88gqjRo0qlHyidHLALsCHnMiYvLFixAsF9ncUtGVKK8JefQqHoIKRRtu2bQPg77//pmPHjhbDbqFEyrX0LklSHaAFkJ++NkuSpDOSJP0iSVJ+0K0fEHXXbtGUfCMorU9q165tdlGtiiCEQKVS1bjQu0OHDjFu3DgiIiIq1Y6Xlxf9+vXD2dmZ5ORkVqxYQZMmTfjxxx8xGAy8+uqrvPzyy7z11ltMmDCBP//8E09PTwYMGMCxY8dMIXqSJCEp7KHWAPDuAzY+oHACm1rg0w98+iEp7Co0m9br9aSnp5Obm1vk+0ajkeTkZBITE03FR6qCgwcP8sknn5CdnV3oPTc3N3744QcmTZpU6D1bH08CRvc3qT06o6AzzsjuzOGt/bxJbORPjragjn90dDS7d+/Gy8vLEjFjoVTKfIVIkuQArAOeF0JkAt8DIUBz8mb0n5enY0mSpkmSdFySpONJSUml73AX+ZWAKhvrW17UajW///47J06cMFub5jiW5s2b89Zbb5kt1l8Iwa5du2jYsCHu7u40b96cwYMHs2DBAt544w3mzp3L4cOH6devHytXrqR///6mBK0CyO3BvR0ET4XQmRD8BLi1NvnfK0JQUBBr164tUm8G8iQaRo8eTfv27Zk9e3aRx6bT6UyJO0WRmprKypUrSUhIKHab06dPs2XLliLXMBQKBa1bty7SbSNZKWj40hT8hvVE/v+PMUgKOXa1a9FiwVzicrNNlaS0Wi07d+6kffv2dOvWjXnz5pX7SczCf48yGXZJkqzIM+orhBDrAYQQCUIIgxDCCCwmz90CEAPcHWjtf+e1AgghFgkhWgshWnt6lm+R6ObNm4waNYo9e/aUa7/KolAoqF+/vllDHG/fvs3o0aPZuXNnhdvw8vJi7NixZit+rNfrOX78OG3atOHcuXMMHz6c7777jq5du9K6dWtGjRrF5cuXOXToEH5+fly6dAkPDw8SEhIK+bfzfuRIMmXe7zLK9haHJEkFFnfvxdHRkREjRtCqVasiZ7YqlYqpU6fy/fffF9vHlStXmDdvXolPQFOnTuWvv/4qt4CTJElYe7nT/peP6LDsM+o8PpzAMf1p/NYz9Pz7VwKH9GTMXZ+lTqdj9+7d9OvXjz59+tC9e/f7WnjGwoNBWaJiJOBn4KIQYsFdr/ve8b8DjADO3fl7I7BSkqQF5C2ehgLHMCNWVlZ4eXkVEK2qDpRKJR07djRrm/nHUla9k4yMDNLT0/H396+ydHetVktycjJOTk5kZ2fj6upKRkYGtra2bN++HU9PT3r06EF8fDwBAQEkJiZib29PVlZWlYynPDg6OvLKK68U+75MJsPT07PE4iTNmzdn7dq11K5du9htSqr4VBqSJKF0diRwVD8CRvYFo4C7Su3d225qaipXrlwhOjqa06dPl6kurIX/NmUJmegEPAaclSTp9J3XXgPGS5LUnLwQyEjgaQAhxHlJktYAF8iLqHmmohExkOczPXXqFGFhYSZD7u/vz+LFi4vc3mAwoFKpsLW1vS8Vl8qLr68vixYtKvP2P//8M6tWrWLz5s1VFiWkUCj+f5HUxprsnEwUCjBISYQ2dKZ5425cvnyZ9u3bEx0djYuLCzdv3qz2G21JCCHIyclBCIGDg4PJaNrY2DB//vwS97WxsSky0kmv16PX61EqlWbzc0uSBPLin2AkSeLgwYP8+eefACxYsIBBgwaV2m5mZiZz586lQYMGjBkzhrfffhsbGxv8/f1LvPFZeDgoS1TMQaCoK29rCft8CHxYiXEV4MKFC/j7+5sMR0mP8hcvXmTmzJm8/fbb9OrVy1xDqDLK65bo1asXbm5uVSJ+le9GUSqVtG7TgtNnDhIa5sKu/UsYOj6AQ/v/JiDIDUHewmWTJk3YsGEDzz77LH///Tfe3t41KuNz6tSppKamsnXrVlPSV2XGd+rUKf7991/Gjx9Ped2HFUWhUPD0008XWAguy1qKJElcvnyZ27dv06dPH9asWcOcOXPYvHmzxbD/B6jxU9p82dmyzgZdXFzo0KGD2eLBqxudTsfFixfx8/Mr0n/brFkzs5cIFEJgFBr0IhOdIQWNIYZGbVV8/O5WJkzuzMrfDmPv2JwWzdvw/puLadlCzQsvzGHRokW0atWK06dPU79+/SJDUtVqNZmZmXh4eFR7IYfmzZuTlZVlttm1i4sLAQEB1bp4aTQa+ffff0lPTwfyPqsLFy4wZsyYEvdzdHSkf//+7Nu3D8ireduiRQuOHSvoFQ0PDyc6OtokNmfh4eCBMOxl1UeBPDfNJ598UuR7Op2OlJQU3NzcalRkgVqtZuHChbRq1Yp69erx2GOPMX36dGbMmGH2vvJn5QIdRqFBa0hCY4hFa0wmNSWFKxfSCW1oj6ubI82aNWPD75fp2nEIEUduotFcJS1VzZkzZ7l06ZLJmK9atYqPP/64SCmECxcucOzYMcaNG2e2xd2yIEmS2QsYhIaGEhoaatY2S0OpVPLpp5+aongMBgNff/11udvZsWMHTZs2LfTEotVqLUb9IaTGG3ZzcurUKaZPn87nn39eYt3N4hBCYDTmCVtVZhao1+sxGo1YWVkhSRIajYZ//vkHKysrOnfuzLvvvkvz5s0r3P69Y77zF0Lo0BqT0Bji0RoT0RmSMQgNQgj0WgUxkQaunTNi1DhhrXCkVdNB5KQfYM/uf5kxYwY//fQTPXr0wMrKitjYWGrXrs2KFSt44403ilWcDAkJwd7e3qIDXgylXVNqtZqePXtyd0jwwIEDS203JyeHgwcPcuvWLb7//nsuX77Mzz//jF6vL7Bdp06dTIU2LDw8/KcMu7+/P+PHjy9WFEutVhMeHk5YWFiRPtTc3Fw2btxIvXr1KqV1/f3333P48GEWL16Mg4MDzs7OrF27Fmtra2xsbEwVj+4lz0gLoPSQQSEEAh06Yzo6QzIaYxxaQwJGkYsQBnRaGepsOepsBzJTrcjNUYBQ0LJlKK6urri7u+Pm5kavXr05c+YM+/fvJyoqCiEEtra2puLUn3/+ObVq1Sp2PC4uLpbwvBLQaDRs3LiRkJCQIjNb7ezsOHLkSIE8h6LK9N2Lra0tixYtMt3YX3vtNYAa9aRqoer4Txn2WrVq8fLLLxf7/u3bt5k5cyavvPIKjz/+eKH384WuYmNjady4cbGuh2+++YY5c+ZQt27dIvvx9PQkMDDQNEOTJKnE8DshDGgNCagNVzEYs5AkK2zkQVjLA5FJeQqL+YbcIFToDGloDDFojYkYRA4GYy56nYROI0eVZX3HkFsh9EqsrGxwcHCktp8Hrq6u2NramsrQ5RvrOnXq8OWXXzJx4kQ+/PBDOnbsSJMmTbh27RoDBgyoUQumkOde+OCDDwgJCSky+7MmIZPJcHZ2LnYNSZIkvLy8KtRuTdFWslD9/KcMe2kEBgby7bffFivqZW1tjUaj4b333qNu3bpFRidkZmZy48aNEtPZH3nkER555JEyGcSsrEx+XPwFLl6pDBnZgly1lmW/7GPQsNbUCWyGo1Ur9CIbjSEuz71iTMVgVGE0Shh0MnIyrMhOd0SdrUSjtkLCCkdHRwL93HF1dcXR0bFA3PS9YzIajWzZsoXc3FwOHjxI3bp1UavVxMfHc/z4cUaOHIm/v3+ljbtWq+XUqVP4++eVxKsMRqORW7du1ajwy+JQKpX07du3zNsbDAZ+//13JkyYUIWjsvCgYzHsd2Fra1tsqno+HTp04MUXXyw26qZt27Zs2LCh2FqmUHTIXVxcHEePHqVXr14FFovlVtm0aC/nn12xCNGcUyducur4Dbr2bIjWcJskQzQGoUMIIwa9hCpTgSrLkex0K1RZViDk2Ds44u7igluwG25ubiaZ3eLGcjcpKSksXbqUmJgYXnnlFY4cOULr1q05cuQI4eHhbN++nSeffLLENsqCVqvl7NmzAJU27NbW1ixevNismirlUYxMTk4mLS2N4ODgMkUCleWmmO+LNxgMFSrebeG/hcWwl5Pg4OASC1fIZLIKCZYdPHiQd999l5CQEJo0aQLkfZn10i28fPOqCyUlZnLzWgIt2+b1LxAgDKTEWZOZokSVZYXRoMDG2h57ewcC6rnj7u6Ora0tMpmM5ORkgHKFHdrY2NC/f39u3LjB5cuXsba25vLly7i6ujJs2DCzRYnY2dkxbty4cvmA8zNk82vP5lMRZcvSyM3NZfv27YSGhtK4ceMStz1//jxXr17Fz8/PLE8NQghWrFjBO++8A0CfPn0YNmxYpdu18PBS4w270Wjk4MGDtGjRolxhjw8affv2LSKcTmAQKtN/tyOTOHXiJvFxaeh1RkJCfUAoSI32QKl0pHZA3oKnvb09tra2BfzkmZmZbNiwwaSdXlYcHR353//+hxCCQ4cOceDAARwcHBgzZoxZpZRlMlm5I2fi4uLYsmULAwcOpE6dOuXaVwiRVwWkHNo1MpmsTE8BrVq1IiwsrEyLnGXlq6++YtSoUfTs2bNCPncL/y1qvGGHvC9wWFjYQ23YnZ2diwhxlMhVCf5cc5QTx67RsWt9Pln4GL8vP0SrtiHI5XJk2NKmTTvs7JwLVD26Fzs7O3r16oWHh0eFxpeUlMSuXbuYM2cOQLUraxaFl5cXvXr1wtvbGyEE27ZtQy6X07dv32KNdWJ8AlGnzqI8fQ1NXBJKV2cCRvTBuVEoMquiC3tcv34do9HIkCFDynQTcHBwqJLwzoiICDQaDU2aNKlUVJaFh58ab9glSWLEiBE1TvdFp9MRHR2Np6dnlcVoS5KEi2MDxj/ejbETO2Jrl6dRMmJse6wUciQJbBVB2Fs5I0klzyQVCkWl3CZRUVHUrl27SqQMKoqtra1pAdtgMLB06VKUSiV9+vQp0gAbdXou/bKWyPm/cjkrGY1eT12ZHZ7fr6TBC0/Q4IXJyKyVhfY9d+4cOp2OunXr3rcIoCFDhhAZGUlWVla5yh1a+G9Ss6xlEVSFv9QcZGVlsW3bNjp06FAls6ecnBxu375NnaAAvN2aozZcIa+ELNjY5C3MWkleyPR1SMlMxdXVtUpT9vNlefOzFI1GY4XVDasCmUzGF198UaIscOrJ8yR9uRxZWhZuyEhF4qgxg55JEuc/+hHH0NoEjOpXaL/evXsjhLivYZ1Xrlzh/Pnz/9feuUdHVV4L/LdnJk/ygkhISIJJIKAgJJDwELilgAjGJ5YUo1iLbW1RvFZv761RLleXWG3Lsq1eHra1UlsLesUKRrt6w2uJFRECISQ8Ihi4GDGBhDAhIck8vvvHnJkGSMgMeU2G77fWWXPOd875zt6Tkz3f2Wd/ewPoeQGaDtGlWK6QqKgobrvtNoYPb7vEW2fZvn07ubm57C85QETwOCKDJhFkisNEOGaJItySQVTIv3Do4P/xzjvvdHvK3OTkZFJTU/nlL3/JypUr8bU4SncjIiQkJBAfH9+mAVZKUfHGezSfrkMAM1BCI5GYEcBe38DR195B2eyX9BsREUFkZGSvGvampiYWLFjAggUL+MY3vtFrcmj6Bn4/YvcWpZRnVNUT/4AWi4UhQ4Z0W/+ZmZnk5+e7Hv+xEGYZRpglDYUT8ZgmV0Wh4ODgbo/ZFhFyc3M9U9IvF87ZWbwtWO1LegfldFJXctj1whS4hiBmEE0xDThwjXDqSstxtLRgCu4+3a6Ur776iqVLlwIut4yOitFcjoAx7GVlZRw6dIicnJw+MTGlI+Lj47nvvvsuajUjXOhuGTDAFZve3bh/MLvTLaaUwn6ukdqiUpqqarD0C6P/uJGEJcRdYuDtdrtnPsGTTz7Z4Y+5iGAKdcnehJMvaKIeB0HGzySAOTjYU4vUXVfVX1IRL1682FN6sL0ZzRqNm4Ax7BaLxTMVvi9TVlZGbW0tkydPvsRnXlNTQ0tLS7vuhr6Mcjqp3FfGR08vhx2lNNXVUxcMQ0ePIuOJBxmSe8slUSuRkZFeT9YRk4mk22dQve0zguyKCMyEY2YQQVgQMJkYfPt0TMaTyM6dO/n666+59957u/XpxFteffVVampqUEoxatQobr/9dp/Ob2xs5N577yUuLo5FixbpqJoAJ2AM+4gRI3wq5nz27FnKy8sZOXKkX83kq6mpoaqqCqfTeYlhf+aZZ6ioqGD9+vVXNAnKn2msrObdB57gr/s/Yw79EeBvLWf4ZlETtn/9iuABMSTMnur5QbNYLCxbtgzwvnhG8rdmc/ztv3H6k71cqy6MMY9Mv5ahD85DzC63zrhx42hsbOzxHPLtERkZyTPPPENtbS3vvfeez+fb7XY+/vhj3n33XUaNGtX1Amr8ioAx7L6OYKurq9m9ezeJiYleG/ba2lqsVivJyclt/sNf7BtWSlFZWYnFYvH6kX7y5MkopdoM77zzzjupqanxu9DPzqKU4sT6vxN7sJJ0XJE2gwlmJOGYgJbasxz53VsMmjYec5jLIF/JE0vY4DhuXPMiZS+8SmXBVppPncESEU78zVMY+e/fJ+aG4Z5+e6pCUke476lhw4Z5SuK5Z6D6yvXXX88DDzxAbm4uL7zwAmazmQ0bNlBRUeHJ964JDK7aqJjU1FTy8vJ8yoBXUlLCpk2b2i0m/MUXX7Bx40YaG12zRZ1OJ9u3b2fnzp1eX8NisXjytF/MTTfdxPz58/1mFNllKEXVtp1gt3v83aZWvm+UovqjXdgbmzp1GREhYugQxv/3fzHr47XM3rWe2Tv/hxtff5HYCWMQk/+5t5qampg+fTp5eXns2LGDH/zgB1RUVPjcz8mTJ3nuued48MEHWbt2recl+A033MCECRP8MqRYc+UE1tDPBywWi8/xwOPGjWPEiBHtukHsdjvNzc2eWZkmk4mZM2d2+wjbZrNRV1dH//79++5oXimaUTTg4BwOmnDSgIN6TNhRBDmVKzdOJxERzGEhRKWndF7mLsCV2gCQtp9CnE4nERERDBkyhPj4eO655552C7lfjnXr1rFlyxbq6up4+OGHPffJ0KFDiY2NZd++fZ1VReNHdDhiF5FQEflMRPaJSJmIPGu0p4rIThE5IiJviUiw0R5ibB8x9qd0sw49RlRUFAkJCe2G1w0fPpx58+Z5ZqK6c2kPGDCgW192fvzxx9xyyy3s3bu3267RrYhwzeSx1FicmBCqsfE1LTiA09hpECcDJ2Vi8aMJUZ1BKYXTZufUjr2ULlvFvv/8Ncf+vJHmmroL3HluiouLWb16NTU1NTz77LNYrVafr7lkyRIKCwvZtWsXP/3pTwPvqU9zAd4M75qBGUqpcyISBHwsIn8DngB+pZRaJyKrge8Bq4zPM0qpYSJyD/BzYH43ye9X9FQM/cWkpKRw9913M3jw4B6/dlcgIpgnjyEiJIzZdovHBZOCy58eFB3J0B98G3NY178wVkpRVVVFcHBwj4SNAtis59i35NeUv1VA6emvGKgsJIZHET1yGNn/vdTlFjLuo7CwMFauXMnq1avJzs5m4sSJPPfccz5f02w2a2N+FdHhiF25OGdsBhmLAmYA7xjtfwTuMtbvNLYx9s+UQIvN8zNSU1N56qmn2sxjrpTi5Zdf5pVXXmlzNOgvvPn3AtbH2ug39josEa55CGKxEHVdGpkv/oTEnGnd8qPpcDgoLCzk008/7fK+28Jpd1C+4k2O/PYtGk7VUKNslNGIo7GJ2t2l7Fr0DM2naj3HK6VISUkhIyODlStXerKAajSXwyuHrIiYgSJgGLACOArUKaXc86+/BNxWJRE4AaCUsovIWSAWON2Fcmu8xJ2d0B2l05ZxbGpqwuFwEB4e3i3GUynFhx9+yLlz58jNzW3TlfXdhQuZPWcOWdePouaTvZyvrMIS0Y+BU7Pod237NVU7i9lsZvr06T0WPtpUfZpjb25Etdjoh4l0QjnMP5N6WQ8e5fjbfyP9R3mYLGaampqYNWsWAMePH+fzzz9n5syZXHvttT0ir6Zv4pVhV0o5gEwRiQH+ClzX2QuLyEPAQ0C3Ts33F9zl2iIiIno0lE5E+MUvfuFZb4sXX3yRsrIy3njjjTYTe3WUrsGbFADvv/8+p06d4u67727TsA8dOpShQ4cCkHjb9A776ypEhKSkpG69RmtazlipLz/mujaX6uZoaqb4P35JwqzJRI1I88hYVVXF2rVrycrK4qOPPuL+++/vMZk1fQ+fwh2VUnXAVuBGIEZE3D8MSUClsV4JJAMY+6OBmjb6+q1SKlsple0vMcPdic1mY9u2bT0efSAihISEEBIS0q6RdIe8teeDfeedd/j+979PXV1dm/sdDgf5+fm89NJL7bp7fvazn7F69WqvonbcPyCB6METsxmxuL7nJpwcp5mT2DjLP5OPhSfHe+L1w8LC2L59O1OnTmXatGmYTCZKSkp6RXZN36HD/zIRGQjYlFJ1IhIGzML1QnQrMA9YBzwAbDBO2Whs7zD2b1H+7NztIYKCgsjJyfGrVLdu5s2bd9n9DocDm8122WPsdnu7k1xEpMdeTPo7oXEDGDh5HFVbXD79/liIwOwJ5AyKjiTr10/Tb4jrRbjJZGLYsGGsWrWKuLg41q1bx8yZM3tJek1fwRtXTALwR8PPbgLeVkoViMgBYJ2ILAP2Aq8Zx78G/ElEjgC1wD3dIHefw2Qy+TQZyp+YP38+3/72t9sdQZvN5g7dPb3F119/zenTp7nuuuv8IsY/uH806Q/fS+2eA1Bn5XpaJawzmUiYM5W4aeMvOEdEuOGGG1BKsXjx4i4t0q0JTDq805VSJcAlGYOUUl8AE9pobwJyu0Q6jV/QkVvEn90m5eXllJeXk5aW5heGXURIumMGzlXP8PnKNzn96T6U3UF4cgJJc29iVP4PPW6Yts711+9Z41/0/p2u0XQj48ePZ/To0V1aWLqzmIKCuHZ+DvEzJnHueCXOFhuhAwfQLyUJU5BFG29Np9GG3c84evQoZ8+eJSMjQ08o6QLCwsL88r2GiBAaF0vIwAGebY2mq9DOOj/j2LFjHDx40JNvRnMp7vDLQHgn31PuFaUUdrsdu92u762rAG3Y/YypU6cyd+5cv/AH+yvFxcVs3Lix3SybmkvZs2ePJ4ndSy+9pI17gKMNu58REhLi9QxQp9NJfX29TwZOKcX58+dpaGjwasTrdDqxWq0dhju6aWlpob6+3mvDYbfbsVqtPuUDN5lMmEwm7b7wgT/96U8kJiayaNEifvOb33j999T0TfSwsA9TX1/P22+/TUZGBhMmXBKg1C6bNm2isbGRefPmdejHr62t5d1332XChAlkZmZ22HdRURFlZWXMnz+fyMjIDo8/evQo27Zt4/bbb/c6idmYMWMYM2aMV8dq/klSUtIl98nvf/97Dh8+zP79+6msrGznTO+x2WxYLF3zAthut7sSxHXBu6aWlpYuyznfVX1ZrdZuC4HWhr0PExkZyX333eez22bWrFkopbyKh+7fvz8LFizwuu5nVlYWGRkZXkehpKWlkZyc7FOuFj1SvzIOHz5MQUHBBd9fbm4u1dXVvPrqqzz++OOdvsaKFSu49dZbSUlJ6XRf77//PrGxsUyePLnTfeXn5/PCCy90uh+ApUuX8thjj3U60urLL7/kww8/7BKZLkYb9j6MyWQiPDy84wNbISI+3ZBms9mnawQHB/s0mgkKCvKLYtGBzuLFi3nkkUf45JNPeP755z3feXR0NDabjbCwMGJjYzt9nX79+hETE9MlfUVERBAVFdUlfYWGhnZZXYSwsDAGDBjQ6WirhoaGbhukiD9EFmRnZ6vdu3f3thgazVVJc3Mz5eXljB49utN9HTp0iKSkJE+xmc5w4sQJQkJCiIuL63Rfe/bsYezYsV1iSIuLixk9enSnXUQNDQ0cP36ckSNHXtH5IlKklMpua58esWs0VzGVlZX84Q9/8FT7uhKf7/79+9mwYQMiQnZ2NoWFhdTX12OxWHj00Ud9HtmuX7+ew4cPM2PGDDZt2oSIkJWVxZw5c3zqp66ujhUrViAiOBwOPvjgA0wmE2lpaeTl5Xndj1LKU33KHWb7wQcf4HQ6WbBgAampqV718+abb1JZWcnixYv585//zOnTrkzmVquVwsJCACZOnMjNN9/sk57tCt3bS1ZWltJoND3PHXfcocaOHavGjBmj8vLyrqiPbdu2qR//+Mdq6dKlKiYmRlksFrV06VIVHR2tampqfOrr1KlTasqUKcpsNqvvfve76vrrr1d5eXnq0Ucf9VmuDRs2qPDwcLVq1So1ffp0FRsbqx5//HGVk5PjUz8VFRUqJiZGjR8/XiUnJytA/fCHP1RJSUlq8+bNXvfzk5/8REVFRanTp0+rjIwMtWTJEhUREaGWL1+uoqOj1a233qrmzp2rrFarV/0Bu1U7NlWHO2o0VzFnzpzh4YcfZuHChdTW1nZ8QhtMmzaN559/HqvVSnBwMFOmTOG2227z2TeulGLLli0cPXoUp9PJmjVreOKJJ67YrbNmzRqam5spLCzEarWyaNEiRo0a5XNRlZiYGLKysti1axcnTpwgNjaWRx55xOeghQcffNDjCsrIyCA3N9dTmyEtLY3XX3+dzZs3d0l0kjbsGs1VTmNjI+fPn+/4wHZoaWnhqaee4i9/+Qtr167FbDZTX19PQkKCz32tWLGCjIwMTCYToaGhnSrQHhYWxs0338zs2bMpKiqipKSEuLg4n6NZTp06xSeffMLTTz9NYmIi58+f58CBA1csl7vP6upqT5EXh8Ph+WHsivBO7WPXaK5ipk6dSn5+PoDn01fee+89VqxYwbe+9S0OHDjA/v37Wb16NaWlpT73VVBQwIkTJ5g4cSL5+fmsWrWKlJQUxo69JMFshyxZsoQpU6bQr18/ALZu3YrT6fQ5FUV0dDQjR47klVdeoaGhAYfDwWuvvUZ9fb1P/fzjH//AZrPx+uuve76jPXv2cNddd1FWVsa4ceO4//77SUtL86nfttBRMRrNVcz58+c5cuQIIkJ6evoV1X6tq6vjxIkTl7SbzWaGDx/us8vCZrNRXl7OoEGDOHnyJACxsbFeT2Bz09LSQnl5+SWGPDIy0uc4+5MnT3pedrYmNTXVa1dRRUUF586du6BNRIiPj/foec0113j9pHO5qBht2DUajaYPcjnDrn3sGo1GE2BoH7tGo/FrmpubKSgouCBxWUpKCpMmTepFqfwbbdg1Go1fYzKZSExMZMeOHSxfvpzly5djtVo5dOgQDQ0NFxw7ePBgqqursdvthISEMGrUqKsyt5A27BqNxq8JCgpi0qRJWK1WQkNDKSoqoqqqivLyckpLS2lsbCQzM5Pm5mbCw8MpLS1l9uzZ7Nu3j/Ly8i7L6tiX6NDHLiKhIvKZiOwTkTIRedZoXyMiFSJSbCyZRruIyMsickRESkRkXDfroNForiJaB3wsW7aM6dOns2DBAgYPHkxRURHp6emMGDGChQsXXrXlJb15edoMzFBKZQCZwBwRcTu3/l0plWksxUbbLUC6sTwErOpakTUajcbFwIEDycnJYe7cuQAMGzaMqqoqLBYLH3300VVbKapDV4yRk8AdfBlkLJeLkbwTeMM471MRiRGRBKXUyU5Lq9ForlrGjh3L7373O+Lj42lpaeHcuXOkpKTQ3NzMoEGDWLZsGbGxsXzxxRfYbDZuuummq3bE7pWPXUTMQBEwDFihlNopIouA50VkKbAZeFIp1QwkAq1nK3xptGnDrtForpiBAwcyY8aMdve7o2TS09N7SiS/xas4dqWUQymVCSQBE0TkBiAfuA4YDwwAfurLhUXkIRHZLSK7T5065ZvUGo1Go2kXnyYoKaXqgK3AHKXUSSN7ZDPwOuAuplgJJLc6Lclou7iv3yqlspVS2e4MZxqNRqPpPN5ExQwUkRhjPQyYBRwSkQSjTYC7AHfGn43Ad4zomEnAWe1f12g0mp7DGx97AvBHw89uAt5WShWIyBYRGQgIUAz8yDj+QyAHOAI0Agu7XGqNRqPRtIs3UTElwCU5M5VSbb7FMKJhHum8aBqNRqO5EnQSMI1GowkwtGHXaDSaAEMbdo1GowkwtGHXaDSaAEMbdo1GowkwtGHXaDSaAEMbdo1GowkwtGHXaDSaAEMbdo1GowkwpHU1kl4TQqQeONzbcvQA1wCne1uIHkDrGVhoPf2Ta5VSbWZQ9Jeap4eVUtm9LUR3IyK7tZ6Bg9YzsAgkPbUrRqPRaAIMbdg1Go0mwPAXw/7b3hagh9B6BhZaz8AiYPT0i5enGo1Go+k6/GXErtFoNJouotcNu4jMEZHDInJERJ7sbXk6g4j8QUSqRaS0VdsAESkUkc+Nz/5Gu4jIy4beJSIyrvck9w0RSRaRrSJyQETKROQxoz2gdBWRUBH5TET2GXo+a7SnishOQ5+3RCTYaA8xto8Y+1N6VQEfEBGziOwVkQJjO+B0BBCRYyKyX0SKRWS30RZQ9y30smE3yu2tAG4BRgJ5IjKyN2XqJGuAORe1PQlsVkqlA5uNbXDpnG4sDwGrekjGrsAO/JtSaiQwCXjE+LsFmq7NwAylVAaQCcwx6vj+HPiVUmoYcAb4nnH894AzRvuvjOP6Co8BB1ttB6KObqYrpTJbhTYG2n0LSqleW4Abgb+32s4H8ntTpi7QKQUobbV9GEgw1hNwxewDvArktXVcX1uADbiKnAesrkA4sAeYiGsSi8Vo99zDwN+BG411i3Gc9LbsXuiWhMugzQAKcNUxDigdW+l6DLjmoraAu2972xWTCJxotf2l0RZIDFJKnTTWvwYGGesBobvxKD4W2EkA6mq4KIqBaqAQOArUKaXsxiGtdfHoaew/C8T2qMBXxq+B/wCcxnYsgaejGwX8r4gUichDRlvA3bf+MvP0qkAppUQkYMKQRCQCWA/8WCllFRHPvkDRVSnlADJFJAb4K3Bd70rUtYjIbUC1UqpIRL7Zy+L0BFOVUpUiEgcUisih1jsD5b7t7RF7JZDcajvJaAskqkQkAcD4rDba+7TuIhKEy6i/qZR612gOSF0BlFJ1wFZcbokYEXEPilrr4tHT2B8N1PSspD4zBbhDRI4B63C5Y35DYOnoQSlVaXxW4/qhnkAA3re9bdh3AenGG/hg4B5gYy/L1NVsBB4w1h/A5Y92t3/HePM+CTjb6nHQrxHX0Pw14KBS6qVWuwJKVxEZaIzUEZEwXO8RDuIy8POMwy7W063/PGCLMpyz/opSKl8plaSUSsH1/7dFKXUfAaSjGxHpJyKR7nXgZqCUALtvgd59eWrcDzlAOS7f5dO9LU8ndVkLnARsuPxx38Plf9wMfA5sAgYYxwquiKCjwH4gu7fl90HPqbh8lSVAsbHkBJquwBhgr6FnKbDUaE8DPgOOAP8DhBjtocb2EWN/Wm/r4KO+3wQKAlVHQ6d9xlLmtjeBdt8qpfTMU41Gowk0etsVo9FoNJouRht2jUajCTC0YddoNJoAQxt2jUajCTC0YddoNJoAQxt2jUajCTC0YddoNJoAQxt2jUajCTD+H7lrd/EAj5fGAAAAAElFTkSuQmCC\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -300,7 +320,8 @@ "env.reset()\n", "# random first step\n", "env.step(env.action_space.sample())\n", - "plt.imshow(env.render(mode='rgb_array'))" + "plt.imshow(env.render(mode='rgb_array'))\n", + "plt.title(\"mobile-medium-central-v0\")" ], "metadata": { "collapsed": false, @@ -311,20 +332,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "outputs": [ { "data": { - "text/plain": "" + "text/plain": "Text(0.5, 1.0, 'mobile-large-central-v0')" }, - "execution_count": 12, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -338,7 +359,8 @@ "env.reset()\n", "# random first step\n", "env.step(env.action_space.sample())\n", - "plt.imshow(env.render(mode='rgb_array'))" + "plt.imshow(env.render(mode='rgb_array'))\n", + "plt.title(\"mobile-large-central-v0\")" ], "metadata": { "collapsed": false, @@ -350,7 +372,7 @@ { "cell_type": "markdown", "source": [ - "### Custom Scenario\n", + "#### Custom Scenario\n", "\n", "It is also easy to define a custom scenario by subclassing `mobile-env`'s `MComCore` base class.\n", "\n", diff --git a/setup.py b/setup.py index 113de1b..f7049e7 100644 --- a/setup.py +++ b/setup.py @@ -20,9 +20,9 @@ setup( name="mobile-env", # always use higher version in dev than what's on PyPI - version="2.0.0dev", + version="0.3.0", author="Stefan Schneider, Stefan Werner", - description="mobile-env: An Open Environment for Autonomous Coordination in Mobile Networks", + description="mobile-env: An Open Environment for Autonomous Coordination in Wireless Mobile Networks", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/stefanbschneider/mobile-env", From f643abc82cd7624297d9e50a463af5cd844fc4fd Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Fri, 3 Dec 2021 15:35:09 +0100 Subject: [PATCH 10/14] rename features --- examples/demo.ipynb | 166 ++++++++++++++++++++++++----- mobile_env/handlers/central.py | 6 +- mobile_env/handlers/multi_agent.py | 6 +- 3 files changed, 144 insertions(+), 34 deletions(-) diff --git a/examples/demo.ipynb b/examples/demo.ipynb index 2cf9d03..8fa1593 100644 --- a/examples/demo.ipynb +++ b/examples/demo.ipynb @@ -31,8 +31,8 @@ "\n", "1. Installation and usage of `mobile-env` with a dummy actions\n", "2. Configuration of `mobile-env` and adjustment of the observation space\n", - "3. Training a reinforcement learning agent with [`stable-baselines3`](https://github.com/DLR-RM/stable-baselines3)\n", - "4. Training multi-agent PPO with [Ray RLlib](https://docs.ray.io/en/latest/rllib.html)\n", + "3. Training a single-agent reinforcement learning approach with [`stable-baselines3`](https://github.com/DLR-RM/stable-baselines3)\n", + "4. Training a multi-agent reinforcement learning approach with [Ray RLlib](https://docs.ray.io/en/latest/rllib.html)\n", "\n", "\n", "### Step 1: Install and Test `mobile-env` With Dummy Actions\n", @@ -55,18 +55,18 @@ "Requirement already satisfied: pygame==2.1.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (2.1.0)\n", "Requirement already satisfied: shapely==1.8.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (1.8.0)\n", "Requirement already satisfied: svgpath2mpl==1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (1.0.0)\n", - "Requirement already satisfied: setuptools-scm>=4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (6.3.2)\n", - "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.4.7)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (1.3.1)\n", "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (8.4.0)\n", - "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (4.28.1)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.4.7)\n", + "Requirement already satisfied: setuptools-scm>=4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (6.3.2)\n", "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.8.1)\n", "Requirement already satisfied: packaging>=20.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (21.3)\n", + "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (4.28.1)\n", "Requirement already satisfied: cycler>=0.10 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (0.10.0)\n", "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from gym>=0.19.0->mobile-env) (1.6.0)\n", "Requirement already satisfied: six in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from cycler>=0.10->matplotlib==3.5.0->mobile-env) (1.15.0)\n", - "Requirement already satisfied: tomli>=1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (1.2.2)\n", - "Requirement already satisfied: setuptools in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (46.1.3)\n" + "Requirement already satisfied: setuptools in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (46.1.3)\n", + "Requirement already satisfied: tomli>=1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (1.2.2)\n" ] } ], @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "outputs": [ { "name": "stdout", @@ -156,12 +156,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "outputs": [ { "data": { "text/plain": "
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\n" + "image/png": 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2CcjJyWHDhg2cPn3aYfu3WCwUFRXx7rvvMmLECDp16uSIBhkVFcXdd9/N2LFjiY+PRwhBSEgIY8aM4fTp0xQWFvLxxx/TtWtX+vXr12wmM/W+ZQshvAGVlLK08u/xwDPAGuBGYFHl/987Q9DWiF6vZ/v27QwaNIjAwMCmFsfB4cOHeeGFF2jTpg3Dhw+vtk63bt3o1q3bBdvQaDR8+OGHNZps0tPTueeee7jtttuYNGlSg+VujWg0GubNm0dZWRlffvklpaWljB49GqvVSkxMDAkJCcTExBAbG4u/vz8AYWFh3Hbbbfz666+89dZbWK1WRo0ahY+PD/7+/kyfPp2uXbuSmJjIiy++yIIFCxpkWlOr1cyZM4dVq1YRFhZGXFwcu3fvZtCgQZSXlzNq1CiH2SIrK8uxKBkfH096erpDuV555ZWNYuJr164dN910Ezk5OSxdupT8/Hw2bdpE27ZtycvLY8+ePVRUVJCQkMDRo0fp0KEDK1euBODGG2+koKCA1atXY7PZmDVrFqWlpZw+fRqz2YzFYmHKlCnNwlRZb5u7EKID9tk62G8Sy6SUzwshgoEVQFsgDbsrZI1R8C9Vm7vVaqWwsBBfX996JZ2uL3q9nry8PKKjo6vdsm8wGEhJSaFDhw4uTZSRlZXFf/7zH2644QZGjhzpsn4UFForSuAwhXP44osveOmll/jmm2+Ij49vMjmUpNkKCg3DlQuqCi2QhIQE5s+fT1hYWJPKIYRwvKrDbDazZcsWR6zvxqK8vJwTJ04ouVsVWjSKcr8E6dSpE//6178cNtrmSklJCY899hjLli1r1H4zMzPZuHEjiheXQktGCRzWCrHZbBQWFuLl5dUiNupcCH9/f959910iIiIatd82bdowderUJnuySUtL46OPPmrUpMo+Pj5YLJYW+bTi4eGBRqNpkiTUzmDKlCmOPQDORLG5t0Ly8/OZMWMG06ZN49///ndTi6NQR2w2G1artVFd6qo2HLVUWrL8arW63t41SrKOSwwvLy+mTp3KgAHVfucKzRyVSqUENFNoMMoV5GIMBgMlJSWNOqvw8vLigQceYMSIERev3EIpKyvjscce48cff2xqURQUmiXKzN3F/Pbbb2RnZ3PdddcpaeCciMlk4tChQ8TGxja1KC7FbDbzn//8h1tvvZWIiAgWLFiAu7s7N998M127dnXU+/TTT/H392fGjBnVtlNRUYGbm5tLNtdUVFSwdu1aMjIymDZtGu3bt8dms7F371527NhBfHw8V1xxBdu2bePgwYNER0czZcoUfvrpJ44dO0ZUVBQzZ85s1L0eALm5uSxbtoyYmBimT59OVlYW3377LWq1mmnTpjmSbp84cYLVq1fj7e3NFVdcQVxcHGVlZSxdupQJEyYQFRXFmjVryMrKYvr06c3mmlSUu4vp1KkTkZGRzWLHWmsiMDCQFStWtPobptVq5YsvvuCqq67C39+fJUuWMG/ePAwGA++88w5g3y/w2Wef4e/vT3Z29jlPicOHD2f79u1s27aNXr168Y9//IPk5GSOHDnC7NmzCQ4ObpB8VYHBduzYweTJk3nooYd47bXXCAoK4vnnn+eZZ57h/fffx8vLi4SEBNq1a8ddd92FWq3mp59+YubMmQwdOrRJchZ4e3szYMAAXnnlFSZMmICPjw/XXHMNmzZt4u233+aFF14A4OjRo5w5c4Znn30WDw8Px+f95ptv4u3tTVFREfv27WPcuHGO8VfdGJoSRbm7mHbt2rmsbSklKSkpaDQa2rRpc0ltBBJCtGhPoLryxhtvEBgYSEVFBa+//jrt2rXj0Ucf5YorrkCtVhMeHk5ISAg2m41HHnmE/v37s2vXLl5++WUeeughnnjiCTZu3MgPP/xAaGgoHh4e3HzzzU6RbcmSJVx33XWMGjWKRx55hGPHjpGQkMD27duZOXMmubm5hIaGEhgYyP/+9z9++eUX7r77bn7//XdOnTqFTqfj7bffZvDgwU6Rp7Z4e3sTGRnJ5s2bKSsrIyIiAqvVypNPPkn37t0d9fLy8vj66685fPgwvXv3pm3btnzyySf07duXHTt2sHv3bm655RZGjRrFww8/zMmTJxXlrtAwrFYrDz74IH5+fnz66adNLY6CC7nvvvvo3LkzX331FWazmeDgYCZPnswvv/zCjBkzqKioIDQ0lNjYWCIiInj33XcdcYFCQkJ48MEHGTBgANOmTUOtVrNhwwanmUGCgoLYsGEDkZGRVFRU4OHhgbu7O88//zxeXl689tprALRt25Y33niD1NRUfvjhB5588kl69erFvffey+LFixtNuS9fvpzdu3fTuXNnxo0b5yg3Go0sXryYkpISbrvtNkf5oEGDWLp0KTqdjrlz5xIWFkZQUBBHjx7FarViMpn46aefCAgIwGKxNLp56UIoyr0Fo1arueuuu5Q0fI1IWVkZKSkpxMXFuTTuztlU5c4VQqDVatFqtRQVFWE0Grn88stJTEykU6dO/PHHH2RnZ5OZmcmgQYOwWCyo1WpHWNvx48czePBgDh48SI8ePZwimxCCJ598kilTpiClZMSIEaSmpmIymXjhhRcYMGAAZ86cYfDgwdx8883ExsZy9OhRHnroIZYtW8bevXvZtWsXixcvdoo8tWHmzJnMmDGD1NRU3n33XQB+/vlnUlNTefHFFxk4cCAGg4GCggI++eQT/Pz8OHz4MLt37+byyy/nq6++wt/fnwceeACLxcKsWbOYNWsWFRUVjB07loEDBzbaWGpC8XNXaFUYDAb2799P165dXRJp88SJE2zatImpU6c2yqO3lJLs7GyCgoLQaDQOm7q/v78jTaBWq0Wj0WAwGM47vyphR1hYGOvXr+fRRx9lzpw5PProo05ztzQajY7dvFU2aXd3d8rKyrDZbA6zUU5ODlarFZVKRUREBLm5uVgsFoQQRERENPq6VEVFhSOstZeXFxaLxZGM3sPDA39/f3Jzc/Hz83N81r6+vo6d3VWhur28vBzj9/T0bPA6Rl1QAocpXDLs37+f66+/nmefffaCniMNwWQyUVRU5FC2LQWbzeYIZRsXF9csbMIKDUdR7gqXDGVlZezYsYN+/foREhLS1OIoKLgUZYdqK0AJj1s7fHx8GD9+fFOLoaDQ5CjKvYWg0+l4+OGHmTp1KldddVVTi6PgQkpKSjh48KDD/qvQuomLi6Nt27ZOb1dR7k7EarU6XMGcvThktVopKSlpkVH7FOqGxWKhuLgYo9HY1KIoNAKu+k0rNncnkpqaysaNG5k8ebLTF6yqkvUqQaXqhpTS4RKofG4KrQ0lE1Mj4e/vT5cuXfD19a3zuWazmfT0dMrLy6s9LoRAo9Gco6CklOTm5pKfn99iw526mszMTK655holwJjCJYei3J1IYGAgI0aMwMfHh+Li4jo9buXn57N27VpSU1NrfY6Ukp9//pnt27fXQ9pLA41GQ3BwMN7e3k0tioJCo3JRs4wQ4hPgSiBXStmjsiwI+BpoB6QCs6SUOmF343gDmAQYgJuklH9eTIjWYpapwmAwsGzZMjp37uzYAn4xTCYT6enpREZG1jpmipSSM2fOODaJKF4056N4GSm0ZhpqllkCTPhb2aPAFillPLCl8j3ARCC+8nUb8G59BG7paLVa+vfvT4cOHep0TseOHc9R7FJKx6s6hBBER0cTERFRa8VltVodwZouBS6WhLshXOz7UVBoSi6q3KWU24DCvxVPBZZW/r0UmHZW+WfSzi4gQAgR6SRZWwwajYa+ffs2eFHVYDBw99138+WXXzpJMigvL2fz5s0kJiY6rc1LlSqz2B9//KEoeIVmR31dIcOllFmVf2cD4ZV/RwPpZ9XLqCzLogVjs9koLy93iYtjTUgpKS0trTZmSH3x9PTkyiuvxM/Pz2ltXqpIKTGZTIo/ukKzpMF+7lJKKYSo87RFCHEbdtONSxz4nUleXh7ff/89o0ePJi4urtH69fb25uOPP3aqC59arVbiijgJlUrFFVdcATRPe77VasVisQD2791mszmeMKoiTSq0Xuqr3HOEEJFSyqxKs0tuZXkm0OasejGVZechpfwA+ADsC6r1lKNR8Pb2dlmUwZqocn9srlgsFrKystDpdGi1WiIjI/Hz82u2SkOv13PgwAG6detGQEBAg9tzlS3fWaxcuZJFixbh5eVFXFwc+/fvJzY2llOnTrFz505HdEOF1kl9Ncca4EZgUeX/359VfpcQYjkwCCg+y3zTYvHx8am110trw2w2o9fr8fX1Ra1WO0wRO3fu5Ntvv6WgoAB/f3+MRiMVFRUMGjSIGTNmEB0d3ewUX1JSErfeeiv//e9/mTJlSlOL43Iuv/xyunTpwrFjx5g7dy5eXl7cd999/Pvf/1bWCC4BLqrchRBfASOBECFEBvAkdqW+QggxD0gDZlVW/xG7G+RJ7K6Q/3SBzM0Km82GxWI5b4NRa+HkyZOObD9hYWEYjUbeeecdDh48yIwZM4iIiCAvLw93d3f8/Pz49ddfefjhh3n00Ufp2bNns1LwnTt35rXXXqN///5NLUqjUPXdPPHEE0yePBl/f38mTpzIihUrHHWKiop46aWXMJlMhIWFNZssQpcKJpOJnj17uiTYnRJ+oIHk5OSwfv16Ro4c6dJ8qU2FTqfj5MmT9OjRA61Wy4cffsiBAweYO3cuX331FaWlpbRv357S0lIyMzMZN24cAQEBfPHFF7zxxhuXXG7X5kRiYiLTpk2jV69efPDBByQkJPDggw+ycOFCjh8/TkBAADabjdLSUrZu3UpoaCi9e/duarEvKU6dOsWWLVv497//Xa/zlZC/LsTd3Z3IyEin7oA0GAycOnXqPL/3piAwMNCRNqxqFj9//nw+/PBDrrzySuLj48nPz8fLywsfHx8+/vhjOnXqxMSJE1myZAkLFixo1usGrZnU1FRiYmLw9vbmySefpGPHjmzfvp1BgwY5vhOVSoW/vz/e3t54enri4+PTxFJfWrjy931J/+qMRiM2mw0PD496zy4DAgIcHhPOYs+ePdxxxx288847XH755U5tu75IKdmwYQPDhw9nz549JCQkEB4ezv/+9z/CwsLQ6/WEhIRw66238txzz/HQQw/x8ssvc+bMmWbvDdVamTRpEpMmTWpqMRSaiNZnJK4D27dvZ/Xq1Q53seZC9+7dWbhwId27d29qURwYjUYOHDhAz549+euvvxgyZAgffvghM2fORKPRMGTIEEpLS0lOTiYhIYFDhw4RGhpKbm6usninoNAEXNIz97Zt2+Lv79/sFkJDQkKYPXt2U4txDmazmbKyMjw9PTGbzXh4eDji1x84cIAzZ84watQokpOTCQsLo6CgAA8PD6duwFJQUKg9zUurNTKdOnVi4MCBjZ51vSWi1WoJCAhAr9fj6elJWVkZ3t7eSPMZRg2PZdqk4ezcuYMOHTqQnp7uMNXUJ/yxK8nKyuLkyZNYrVantVlSUoJOp1OeUBSaFZe0cleoPVqtlsGDB/Pnn3/Sr18/tm7dyu2330ZWdhpSZSX75FYKcjM5nXyMQ/v30Cm+AzqdjsjIyGblLfP4448zYcIESkpKnNbmTz/9xPLly5udeU/h0kZR7gq1QgjByJEj2bVrF/369ePYsWPs3buPoSOm8/HHG/jrZB6jBrThh9VfML47bFrxOgldQwn11zSryIlTpkzhX//6F56enk5rs1u3bvTr18+l5r28vDwSExOb1Wep0Ly5pG3uzQ2LxcLx48cJCwsjJCSkqcU5j+joaGbNmsVXX33FP//5T5KTk/n22/VYbYK9B9KIbR/P+DEjKDdnkph0lJfujUZ1cin4tIPA7kivKHDzbdKZ/LRp05zeZo8ePZze5t85ffo0U6dOJSgoiAULFjB27NiLXiNnzpzh6NGjdO/enfLycpKTkwG7OTImJsblMis0LcrMvZGQUlJSUkJRUdEFZ175+fnMnTuXpUuXUlhYyHPPPcf+/fvPqWM2m8nLy8NsNjeC1OeiUqmYOnUqQ4cO5eOPPyY2NpYTJ04wePBg+vXrTH5RCbnlWn45pua6m++lWNuJ4jIj5vwDyOSv4OTnkPETsuw00lKOlLZGH0NLJSYmhgceeICOHTty3XXXMWXKFL777rsaz9m+fTsTJkxg69atfPHFFzz22GPce++9fP/99zWep9A6aJXKveoRtrnZQB9++GHuuusubLbqlVpAQACPP/44V155JUVFRaxbt46jR4+eU2fHjh1MnDiRP/+8cIKrqmiArnh812g0XHfddTzyyCNs3LiRnJwc/vrrEIlJGRzYn0jXbjG89vobjJk8B79OV3HGZyJJFb1IKwtBX1qILWcH8vgncOoLyP4NWZ6DtFkVU8NFyMzM5PHHH0en0/Hkk08ycOBAUlJSajxn5syZDndalUrFvHnziIw8N72ClBKj0dgkkwUF19IqzTLHjh0jMTGR2NhYl++ONJlMZGdn1youx+jRo6moqLigWcLDw4Orr74asCvoH3744bwdg7GxsUyfPr3GsL2ffvopW7Zs4b333nN65L+qSIharZbjx48zZ84cnnrqKbp2u4z+/WM4mnSI4cOuQKOJIDw8gtDQMIzGLhQW5JOSm47QnyZQnCHQnIVHWToi93fwirabbXxiwT0IIVrlnKNBSCl55plnuOWWWzhw4AAVFRUMGTKkxnNUKpXjWhNCEBYWxlVXXXXO9VdUVMQLL7xARkZGvbfAKzRPWqVy79evH126dHHqotmFyMvLY+3atYwbNw5vb288PDwIDg4+r54QglmzZlXTQvWo1epq22nXrh2PP/54jef6+voSHBzssgU+q9XKypUrKSsrY8eOHfTv3x+tmzeJiRmcycxkzJi/CA6OwcfHB5VKhaenJ1HRMURERqHX90BXkMfJvFS0hkyC1dn4GpPRlpxEuAeAd1tkYA/wigI3H6B5h9VtDLZs2cLDDz9MWVkZa9euRafT8cILL1zUzbSwsJDy8nLS0tLIzMxk8+bNlJaWcuONNzrqBAYG8tJLL7Fp0ybFJbiV0SqVu5eXV6PFZAkJCWHChAkEBgZy7bXX0qNHD1599dVG6ftCzJo1q043krqSk5PDN998Q3FxMffddx/btm1j4MCB7N+/n4MHt/PX/t20a9cLb++uCGFXGEII1Go1vr6++Pr6YmvbjuLiYvLzcsgsOIVXxRlCzQX4lB9EXXgQ4RkKvh0hoBvSKwJUWhqq6PPy8vj666+58sorW1SQt8jISMaOHcupU6cYMMAeI6pLly4XPS8vL4/77rsPsJv8evbsCcCwYcNcJqtC86FVKvfGxN3dnY4dO2KxWPjnP/9JVFRUU4tUowI827ZdX0Xp7+/PbbfdRnp6OoWFhXTu3Bm9Xk98fDz33HM7I0fGo9HkUlbWBl9fe/KOv9vU1Wo1gYGBBAYGYrHEodMVkpVzBktxCr62TELMxXiX74T8PQjPCAjoBn5xSM8wqpaK6ip/eno6H374Ie3bt29Ryj0pKYnY2FjeeustNm/eDEDfvn0vmhWsS5cutboJKLROFOXuJKoWGps7xcXFPPzww0yYMIEZM2bUqw1vb29uueUWwO6i9+qrr/LYY48RFh4G0oTkMCZTMZmZqXh793A87v/2229IKR2JT6qUs5ubG2Fh4YSEhFJR0ZkiXQFpOacRhgwCyCTAlIOHPhNVzg7wioDAHuATi9QGIlS1NyX06NGDtWvXEhYWVq9xNxU9e/bEZrPx3//+11HWqVOnJpRIoSWgKPc6UFFRQWlpKUFBQY1in7TZbOh0Ojw8PJwWUthms1FSUkJ5eXmD25JScvDgQXx8fNi9ezdXXXUVCC1CRqHVluLpUUBJSREBAUEAF00krVKp8PLywtPTk4jIaMrKyigszOdUbgpu+gxCNDn4GlPQlpxCaP3tCj6wO3jHgMYLUNU4m9dqtS0yQqWUEoPBcE7IBMW7SOFiKMq9DnzzzTcsXryY5cuX0759e5f3V1payuzZsxk1atRFF1FrS2BgIF988YVTFlutVivHjh1j/Pjx7NmzB7PZjFarRRKCIIvgEB0Z6Wn4+QWgUqkYNWpUrdqt8shx2OfbxFJSUkxebg5nClLwLE8nRFuIn/EAqsKDCI8Q8I8H/85IrxhQuTnacSVSSk6dOoW7uzsxMTEu6+/QoUOUl5ezcOFCh1Lv2rUrHTp0cEl/Cq0DRbnXgR49ejBt2jSnJFeuDe7u7lx11VVO3QHpzKTbubm5/Pnnn5SUlHD8+HF0Oh3h4eEgNEAMbm6l+PoWU1iYT0hIWJ1vKFXK0m6fDyIgIBCzuSPFxUXkZKeTXpKBn/U0QeYSfMp3oMrbCx6hENgNfDsiPcMRqurHaleSVbPf+i3U2mw2/vjjD/z8/Fy643PSpEkUFBTQvXt32rdvT3Z2NkFBQS7rT6F10CKVe5VpwcvLC61W22j99unThz59+jRafx4eHtx7772N1h/A77//zqpVq1iwYMFFt7cfPXqU+fPnM2zYMH7++WdOnDhBeHg4AoEkGEEA/gEFZGacJjAwuME3lSr/+tDQMIKDQ6io6EqRTkdGbipSn0GgOEOAKRcPfSZCuxPhGYEM6A6+7UHrV3nTAUxFUHQYyk6BlODdBhnQCzxC6uRjr1KpmDhxostNdDt37nS4QkZERDhcIZWZu0JNtEjlXlRUxIoVKxg8eLCS89HJZGVlcfDgwVrZ5M82s5xvclFhn72X4OdXQn5+DuHhUU4zXVT5z3t6ehIZFUVZWRkF+fmcyktBrT9NqCmPAGMyqqITlNs88QiOQxPcA9y8IWMd0phPamYhm3Ylc/O0fmh0h6DNVUifjrWWUQhBYGCgU8ZTE5GRkQwZMoSsrCwGDBiAm5ubw62xLphMJsrKyhBCNMs8BgrO5aLfrhDiEyFErhDi8FllTwkhMoUQ+ytfk8469pgQ4qQQ4pgQwrn55yrx8vJiwIABNe7SVKgf06ZNY82aNbX6bKts43//G0AgQAQAgfgHCPSGdKdvca/qs8o+H9uuHd36jSC69zSKgyeTZBnMbyleXHXnh3z37Upkykrkqc/BmIfFYmHNL8dJOpVvDwdhLoKszWBt+EKzs0lKSmLJkiWsX7+eRYsW8dVXX9V5QdVms7Fw4UI6dOhAly5d2Lhxo4ukVWgu1ObWvQSYUE35a1LKPpWvHwGEEN2Aa4Hulee8I6p2sTgRDw8PBgwY4JLIiVWLZBkZGXX+AZlMJnJyclp0nA6NRoOXl5dTZnUCNYIY1Go3Avz1FBZmu9TLo2o9ISAggLhOXenSbxTRfa+hW8IkjEFDyK4IBGlGSslvf6XTLsqf0KCzNrtV5ENZSrPzRBkzZgw7duxg165dbNu2jaCgIA4fPnzxE8/CarWyfPlyXnnlFUaNGqUED7sEuOgvWEq5DSisZXtTgeVSSqOUMgU4CSQ0QL5Gx2w2c8899/Dss8/W+dz09HRWrVpFdna2CyRroQhfIISAQBXl5RlUVFQ0ivIUQuDu7k58fDxvv/02M+b8C8/oBEBgsdrYdTCDX/amsf3PNA6eyLGfJM1gdl4SD2fh5uaGn5+f4/X3eEN1oV27dueEtdDpdDz44IN8/PHHTs1OpdD0NMTmfpcQ4h/AXuABKaUOiAZ2nVUno7KsxaDRaLjvvvvqlR4uIiKCESNGuOSJory8nLVr19KnT58WtoFFhSAGRAEBgeUUFmYTFdXunBpSSmw22zmBrpyJEMJunw8KQxar0SB56KYhFJdW8MGqP+kZV7WpSQVqd6f331DWr1/PTTfd5Hjftm3bel0DarWapUuXkpSUxKBBgwB7WIIXXniBLVu2KLFlWhn1ffZ+F+gI9AGygFfq2oAQ4jYhxF4hxN68vLx6iuF8VCoV48aN47LLLquzovH29qZHjx4uCVhWWFjIf//7X3766Sent10bUlNT+fPPP+s8uxMIwBsIx89PhdFkn72fTVpaGitWrKCwsLYPiHVHCAEe4QhPe9o/jVpFoJ8nd147EK1bpVLTBoB3+2YXqGzSpEkcPXrU8fr111/rrNw1Gg3vvvsuFRUVdO3alQULFgD/74Hk6uipCo1Pvb5RKWVO1d9CiA+BdZVvM4E2Z1WNqSyrro0PgA8ABgwY0LyMnM2Q8PBwli1bZvcjbwLefvttdu/ezY8//lh3s4AQCBmFEPmEhhrIy8sgJqajw65fZed3+cxR4w2hQyBjHVgNqFQCP+/KmbrKHUIH2xV8M8MZgfCEEIwfP57x48c7SSqF5k69lLsQIlJKmVX5djpQtbqzBlgmhHgViALigT8aLKUCGo2Grl27Nln/t9xyC1OnTq3XU4lAIIUXEIGXVyoF+WcoL4/E29t+k4iOjnaK51NaWhpr1qzh2muvJTQ09Hw5hED6dwW1JxTsBX2K3c/dKwaC+oNfp2Y3a6+OI0eOYLVa6dWrV1OLotCMuahyF0J8BYwEQoQQGcCTwEghRB/sW/xSgdsBpJRHhBArgETAAtwppWzWqzTOiJJ4KdC5c2c6d+7cwFbCESKXsDA9OTmniY3t4lQ7e2JiIh999BFDhgypVrkDCKFC+rQH77ZgMwPSHq5AaC4qh81m4/Tp03h7exMaGvr/144Em8WCSqOGs1xDncnfF6GrIm0q16zChbiocpdSVhfq8OMa6j8PPN8QoRoTo9HIY489Rs+ePbn55pubWpxWi33XqieCSDw8T1WGBI7Gz895maJGjRrFmjVrzksld54sQth3q14gNMGFsFgsbN26lejoaMaPH4+1wkj2pt859em3lJ08jcbHk9hrJ9N25gQ8o8KcqniTkpKYPXu2431ISAiffvppiwpdrNC4NPstalJKdu7cSWlpqcvaLy0txWAwuKR9V2Kz2fjkk09Yvnx5s/PNrg774mo44ENwiJWCgtMXzCdbHzw8PIiNjXVZSAqNRsPEiRNJSEjAWm4k6aWP+H3OA2z6bi2fHd7J7l27+POBF9l182OUZ+Y49Tupyqj0yCOPUFhYyIEDB1i5cqXT2ldofTR75Q52Tw29Xu+Stj08PHjvvfe44447XNK+K5FS8vPPP/Prr782tSjnUeXeeJ7yFlogCg8PNe7uBRQVFTmUoJQSq7X5JstWqVREREQQEBBAwd5DJP7vI/R6PUcxMAJ/EjFQYTWTvXknx99ZhrQ4zyKpUqmQUnLo0CHMZjODBw9WPFwUaqRFXB3Tpk3D3d01/sfOjJLY2KhUKt5+++0miRGSkZHBmTNn6NevX7Wfn81mY8OGDfj4+DB8+PD/D1OAAMKQZBMYVETWmdMEBAQghMBgMLBu3Tp69OhB9+7dG3lEtUdKScrn32M1VKDHijcqvFGhQWDChofNRtpX6+j6wM24Bwc4pc+oqCi6devG6tWruf3227n99tudnvy8OsrLyykvL1eiULZAmv3MvWoDSksNclReXs7GjRtJTU11ettVAaB8fX1durBWVFTETz/9RG5urqNsyZIl3HnnnRQVFV1QNk9Pz+pvykIDROPursHTS4dOV4CU0pGsw83N7aIyWa1WTp8+TXFxcT1HVX+kzUZ5dh5IiRaBERtWJDZAjf17MObpsFYYndZnYWEher2eiooKtm3bRnp6eo0b7QwGA1OmTOHyyy9ny5YtnDlzhunTp3P55Zfz7rvv1nq/wp49e3j11VcxGp03FoXGoWVqzBaE2WwmJyfHZWsGjYHRaCQnJ+ecdYnrr7+el1566YKzRyEEI0eOJCEhodobjyAEgT/BwZKiojQsFgseHh5ceeWVxMfHX1SmwsJCrrnmGt5///36D6yeCJUK9xB7NEgf1PigZj1FBKHBs/In5Rboh9rDebb/+Ph4br/9drKystixYwclJTWHSXBzc+Pqq68mJSWFvLw8PvnkE1JTUxk5ciSPPfbYRa/HrKwsfv/9d06dOsXx48c5ceJEszWXKVRPy7RHNIDGdn309fXl2muvbfSt3dnZ2ezfv5/hw4c3OEVfWFgY11133Tnml/bt29eYjaqmz9bu964GGYNGU4y3TxFFxQWEBP//Bq2Lufn5+vpy++2307t370Z3CRRC0G7OVZxe/gPWciMj8MeCxA2BCgEqQdtZE3HzrX8MmL9z+PBh/vOf/3DjjTdy8OBBOnbseM5xg8HAF198gcViAezKfe7cubzxxhuOOu3ateOaa645p6y0tJQvv/yS48ePM2fOHMD+2aelpfHZZ58RGxvLpEmT+Ouvv5q1qUzhfC65mXtpaSl33HFHo3kaCCFwc3NrdLPSli1bePDBB0lLS2twW1Vb1J05hqqQwEIEERKiorTkNGazGZvNxqZNm9i3b1+N53t4eHDTTTdRXFzM7t27G31WGTq4L/F3Xo/a0x0NAg9UqBEItYrQIf3odMcchJtz5k45OTmcOXOGnj17snPnTjw8PPh7yA6tVsvQoUMZPnw4w4cPZ8iQIeeZt/Lz8/njj3P3FLq7uzNs2DC6det2zg1Sp9Oxa9cu9Ho9AwcO5OTJk8rMvYVxSc7cnZUgujkzceJE4uLiqs3WY7FYMBqNTb6WIdAgZQwqlQ4//1J0ulyCgyMwmUxUVFSQl5eHn59fjYvpJpOpSRbENd6e9HrybgJ7d+XUp99QdiodjbcnsbMn0/6GKXi3c15O1a1bt3Ldddcxe/ZswsLCeOGFF+jSpcu58mg058ysjUYjTz31FFarlc2bN3P33XezatUqnn76aW6//XZHOAOtVkuPHj04c+bMOfIWFRXh5+dHXl4e0dHRSCnJz88nLCwMhZbBJafc/fz8WLp0aYtdoK0tQUFBjsh/f+fo0aPs3LmTq6++uhl4QfgBIQQEZJOWdprAwDAmTZrEiRMnmDx5Mg8//DAzZ86s9syqeCnOpqysjJSUFDp27FhjTBe1tyftrr+K2NmTsJnNCI0alUZjj6XjRDNRjx49mD9/PkuWLMFmsxEZGck999xT445hrVbLk08+ycKFCx0eYX/88QdSStRq9XlmwqosTcePH6djx47odDr69u2LTqfD3d2dTp06cfz4cUW5tyBat4arhqoLvbkq96pNVceOHePgwYOkpaU5PflHcHAwJpOpXglJ/k5ZWRm///77RRf4LoiwhwRWqbQEB1eQm2efQQYHBzNt2rTzZqjnnCoEKpXK6aGCc3Jy2L59OwUFBTWLXqnEVW4aNF6eqLVahAvCFvfo0YPXX3+d48eP88knn5CXl3fRfR9V5kCtVoubm9s576tb/zEajZw8eZIHH3wQg8GATqdj+PDhxMbG4ubmxqhRo5TMZy2MS27m3lyRUpKdnc3q1avZsWMHWq0WDw8PiouLCQwMZOrUqQwfPhwPD48G9xUUFMS6des4ePAg7733XoPaOnToELfeeiuvv/56nWfR+fn5mEwmIiMjgFB8fDIpLMjAaIwgJCTEEZbWmZSXl2M2m2t0H23Tpg0zZ85slPyotUWr1RITE8OcOXMcC5/O5pdffqGiooLk5GSKi4sZNWoUCQkJqNVqRbG3QJq9cpdSYjabm/Vsu6FIKUlKSmLRokV06dKFu+66i/LycgwGAyEhIeTn57Ny5Up2797N/fff32DvFzc3N5588skLhu6VUmI0GrHZbHh6etY4E+3evTuvv/46AwcOrJMMUkoWLlzoyF7l5haFSlVASGg5ubkZtGlT+0TVdWHnzp2cPn2a66677oK2fK1We8mZH7RaLfv37+fxxx/nhx9+QK1WExwcTHBwsBKcrIXS7JU7wPfff8+IESOaLJa5KykqKuLEiRO8/PLLXHXVVdhsNt58803Cw8MJCAggNTUVT09P5s6dy8qVK/nggw+46667arXR50KoVCouu+yyGus89dRTZGVl8dFHH9XYl5+fX7Uz9tq4nM6YMQOdTmc3EwgfkOF4e6dRWJCFwRDZ4JtYdcTFxREcHNxidyW7Cq1Wy4wZM5gwYQIvvfQS48ePV5R6C6dFXOFhYWENNkfo9XqSk5OJi4tzSaak+pKYmMiiRYsYM2YMZrOZnTt38sgjj1BeXk5ZWRnTpk0jNTWVjz/+mPnz57N48WKSkpJcHsu7S5cuhIaG1vsHbrVaefLJJwkLC+Oee+45rx0hBGPHjv3bWZEgcgkOMVBQkIGXl/Pjq7dt25a2bds6tc3WgEqlYsqUKfj5+fH000+32qfkS4lm/w0KIbj88ssbHEfj999/59prr2X//v3OEcxJtGvXDpvNRv/+/dm8eTMPP/ww3333HR9++CE//vgjixYtwtPTk7Fjx/LDDz8wcuRINm7ceF47UkrKy8sxmUwNlkkIwU033cQDDzxwwRnu1q1b+d///nfBaJpSSsrKyuoW8E14IkQk3t4qhLDv6lV8qxsXIQRqtVqZtbcCmr1ydxZ9+vRh4cKFTkg44VwKCwsJCQnh9OnTxMXFkZKSQk5ODqNHj8ZqtXL99dfz2Wef0bdvX06ePEnnzp05derUeUrPYrHw7bffsn379kaR+6+//mL9+vUX3C+g0Wh49dVXeeSRR2qtKOxBxSIReBEUbKawMF1R7vXEaDSyaNEinnnmGV5++WUMBgOvvPIKCxcuZMeOHcrneglwySj3sLAwZs+e3Qz8us9Fp9Ph5+eHTqcjLCyM1NRUevfuzebNm0lKSqKoqAg3NzcMBgNCCLy9vSkrKzsvlK5KpaJTp06NZnKYP38+q1atuqBHSdUMsO5hF7RANJ6eajSaPEpL6+lieYljs9k4ceIEgYGBvPbaa7z11lu8+eab/PHHH1x77bX1d11VaDG0CJt7ayYkJISioiLCw8P5888/mTRpEp9++ilXXnklR44cwcvLC41G49hNWlJSgr+//3k2UbVaXWePlYbg6enpWLswmUykpKQQEhJCcHBwg9q1Z2wKw2o9AyKXM2eS8fbu5bhJOMNccPr0aZKSkhg+fHiNm5QqKiowGo34+vo2exu02WwmKSnJMSNXqVS8//77rFq1CrCH3ejXrx/PPvssw4YNc9QzmUwcPXqU5ORkCgsLGz0G0qXO6dOnXda2otybmJiYGKSUhIeHc/q0PbZ5r169MJvNSClJT0/nn//8J7t376ZLly4cPnyY7t27NyubaEpKCiNGjODuu+/mP//5T8MbFG7k5QWw+tutDBiox2JTg8oNIQJRyRDg4vlOa6K0tJScnJyLbg7bs2cPx44dY86cOTXeBJoDFouFPXv2OEL5ajQa0tLSuO+++5g7dy4ajQaz2XzejN1kMvHHH3+wb98+/P39nRJC+euvv2bWrFlOuUbXrl3LyJEjawxvXBssFgurV6/mmmuuabBM4Lwx5ubmuszBQ1HuTYyXlxdDhw7l119/Zfr06Tz//PM8+uijfPPNN3zwwQesWbOGo0ePsn37du68805eeeUVnnvuuaYW+xxCQkK46667uPzyy53ToLS7WF59TRc8vQQbN67BaDQzfHhvgkPaohbdQdbsf18TXbt2pVOnThd1h+zQoQM+Pj4uS9vnTDw9PZk3b57jfVlZGV27dsXDw4POnTsTHh7Om2++yYQJExgyZIhjTD4+Ptxyyy34+fkRERHBiBEjGizL4cOHufXWW53ytJOZmckNN9zQ4H0HVTtwb7vttgbLBM4b44kTJ1i7dq1TZPo7F1XuQog2wGfYk19K4AMp5RtCiCDga6AdkArMklLqhP0X9wYwCTAAN0kp/3SJ9K0AtVrNnDlzePTRR4mIiGDu3LkcOXKEzZs34+fnx1dffUX79u255pprWLJkCVdddVW1wcCakuDgYJ544onzyg0GA6dOnbpojJbzseLhmYG7pwd6fQWRkUHkF5Tw/fe/ceNN4xHq46hFT6B+JoSqkAUXIzo6usXuzPT09GTjxo1YLBbc3NyIj49nx44dmEwmoqKizvs+wsLCCAgIcErf8fHxTnuybN++fYP2dFQhhCAuLs4JEtlx1hg9PT1p06aNEyQ6H3GxVXMhRCQQKaX8UwjhC+wDpgE3AYVSykVCiEeBQCnlI0KIScDd2JX7IOANKWX1EawqGTBggNy7d2+DB3Mx8vLyeO+995g+fTo9evRweX+1RUpJRkYGL774Ij179iQnJ4fTp0+j1+spLi6mW7duHDp0iFmzZnHDDTe0iJkkwPbt2/nXv/7F4sWLGT16dK3OkVIiKcAqDwAWR9nOHYmcOnWG6+eOQaVyRy36IghoVuaplkpRURHFxcVotVoiIiLq9Znm5+c73F4DAwPR6XSAPaRwREREndqSUpKVlYWUEi8vL4cpKTg4+IK7qi9ERUUFOTk555X7+vrW2bni7DGeTWRkZK1/k9nZ2ZjNZqKjox1/CyEICAhwfGZ+fn61Dn0hhNgnpRxQ7UEpZZ1ewPfAOOAYdqUPEAkcq/z7feC6s+o76l3o1b9/f3khbDabtFqt0mazXbBObUlKSpIJCQny22+/bXBbzsZms8nS0lK5Y8cOOWzYMJmQkCC7du0qBw0aJEeNGiVfe+01abFYnPI5OIOKigr5zTffyEOHDl2wTn5+vly+fLnMzc29YJ3ExES5YsUKWV5eLqW0fw4Wa5o0WX+SJutP0mhZL5OOfSKfe+GfMit3hTRa1kuT9SdpsZ5pNp9FS0an08kxY8ZIb29v2aZNG7l37956tXPzzTfLsWPHysjISDlp0iTp4+MjJ02aJEePHl3ntkwmk+zWrZvs3LmzvP/+++XAgQNlu3bt5BdffFGndmw2m9y8ebMMCQmR48aNk56enrJNmzZy0KBB8v77769TW2lpabJ3795yxIgRMiQkRLq5uckJEyZIPz8/eeDAgVq3M2rUKBkRESGLi4tlt27d5JQpU2RAQIB85ZVXpFarld7e3vKRRx6p9bUN7JUX0Kt1MhgJIdoBfYHdQLiUMqvqhoTdbAMQDaSfdVpGZVm9Wbt27UUj9NWG+Ph4fvzxRyZPntzgtpxNVc7RXbt2ccUVVxAbG0tGRgbz5s3Dzc2Ndu3aOT36YUMoLS3lf//7H99///0F6wQHBzN79mxCQ0MvWGfdunUsWrTobwt5/29uKSst538vfk1goC85ObrKCYNAXDpevC6lpKSEXbt2sXXrViIjI/nzz/pZUF966SVWrFjB7Nmz+fHHH+nVqxdjx451ZIaqC0uWLOH48eOcPHmSTz75hGuvvZbQ0NDz3H9rw9NPPw3YQ13MnDmTsWPH0qNHj1rnkK2ivLwcnU7Hn3/+iU6nw9PTk5tuugm1Wl2nPQOPP/644zMJCQnh+uuvx2KxYLVa6dGjB9u3b2fp0qVO8aKp9S9ECOEDrALuk1Kes+ReeQep064IIcRtQoi9Qoi9f88q83c8PDyc4qJVFQypuZo1zpw5ww8//MChQ4fw8PAgISGBzZs3o9Vq2bdvX6NuPDGbzaSmpl5wh2lgYCCfffZZgxeobr75Zr788kuHC6U9jK4/YPcgcHPTcMttk+jXPx4fbw9AAN4IcW5Ux+TkZDZu3EhFRUWD5LlUiYyMrDEpysXw9fXl448/ZtmyZSxYsIBRo0YxevToOq9ZFBUV8eabbzJlyhSsVitGo7HWJr0LtdemTRt+++03Pv/8c8aPH8+UKVPqPFaz2YzFYnFMskJCQhgwoHprSE2cvdO+Y8eOXHbZZYwaNQqwx/eJjIykrKysXjfFv1Mr5S6EcMOu2L+UUn5bWZxTaY+vssvnVpZnAmevEMRUlp2DlPIDKeUAKeWAmmZ2AOPHj3faYk9zZuvWrezevRuVSsWgQYPw8/NjzJgxpKSksHr1arKzsxtNluTkZKZOncp3331X7XG1Wk3nzp2x2WwsXryYjIyMevUTHBxMly5dzvFcEXijIgoQeHhqGTiwM/37x9M2NhyVSo2KaKqUfxUlJSXk5uY65UdxKaFSqXBzc+PRRx8lOTm53gr+vffeY8GCBTz55JNceeWVLF68mNWrV5OZed5Pv0bc3d25//77iYmJITQ0lKFDh/Liiy/WeaZdxezZs4mLi3N427zwwgusXLkSo9FYp3b0er0j5aC7uzvJycksWbKkzhOu8vJybDYbRUVFbNu2jS+//JKtW7cC9t/cQw89xMiRI2t82q0ttVlQFcBS7Iun951V/hJQIP9/QTVISvmwEGIycBf/v6D6ppQyoaY+GmtBtblz5MgRVq5c6TC/WK1W3NzcMJvNhISEcPPNN1/U68RkMnH69GkiIiLqvPh0NkVFRSxfvpwxY8YQHx9/wXq7d+/m1ltv5dVXX60mEFj9sF+TRizyL+wPiZWmGOGHihhUIgoh1OfUt9ls2Gw2NJqG+cDXhSp/+aqEFi0Rm83G6tWr+fnnn4mMjOThhx+u15PtkiVL2LNnD76+vkgp0ev1uLu7ExMTw/3331/n9hITE1mxYgU9e/Zk+/bt2Gw25s6dS0JCjarkPI4fP87ixYtxc3PDaDQ6dk0PGTKkTj7vFouFTz75hAMHDjjKPDw8MBqNPProo8TExNSqnbfffpukpCR8fX0pKytDq9Vis9mYPHkyq1evBmDcuHFMmzatVu3VtKBaG+U+DNgOHAKqjF4LsNvdVwBtgTTsrpCFlTeDt4AJ2F0h/ymlrFFzK8r9/6moqODOO++kQ4cOxMTEcP3119cpPG1ubi7ffPMNl19+eaNkqzeZTGRlZREeHu6URCJQ5TFThNX2F7m5JpJPWunatTvBwZGA9jzl/d5775GcnMxzzz3XqCa3AwcO8PvvvzNr1ixCQkIarV8FhSpqUu4X1RpSyt+wGzqrY0w19SVwZ50kVHBgs9nw8/Nj+vTprFixAqvVWiflHhgYyJQpUxocBqC2aLVaYmNjnd6uzZZNWVkFZzIlVqs3Xl5hCFG9yUCv1zdJBMn4+HiCg4MvCZOhQsvjktuhajQaWbduHV26dGmUmW19SE9PZ/PmzY4cnXXBzc2t1o+IZWVlrFu3jkGDBtG+ffv6iOp0bDYbJ08eR19+hF07U/H1CaNfv/Y1btG+7777HImfGxMvL69mH5ZA4dLlkvMnKy0t5cUXX6zRha+p6dSpE/Pnz6eiouKC8dKdQXZ2Ns888wyrVq2q94KVs7FarezatZNTJ3MIDQ3Ey8uHqKioGm9yarW6UW3tCgotgUtu5h4YGMjnn3/eaGaL2lC1AAX2gE+5ubm89NJLtGvXziWp5qpo27Ytjz32GAUFBRgMhloFZ5JSYjAYsNls+Pj41Fqhms1m9Ho9vr6+Nc6wNRrBxEkdsclSjh0FD/fQei8MG41GsrKyGuzmp6DQErnklHuVC19zY+PGjUgpmTFjBh9++GGj9KnVarnqqqvQ6XR1Mi9s2bKFkpISpk6dWmsFn5KSwpYtW5g2bRqRkZHV1rEvpJYSGGQhN8cNfZmZ9u0i621u+eOPP7jjjjt46623nBfUTEGhhXDJmWWaKz169HDEu6mytdfH5l5XAgICaN++fZ0UaLdu3fjzzz+ZPXs2RUVFtTonLCyMAQMGXCRdokTKHKS0kplhxt3dk7CwsHp/BvHx8dxxxx106tSpXucrKLRkmv3MXUpJQUEBAQEBrTZjvRCixSigquh6Y8eOxcvLq9b+3QEBAbVIJmJEkk9ZqaSoSBITU/uATNURERHB/Pnz632+gkJLpkXM3NevX09ubu7FK9YDKSWFhYWUlZW5pP3mgMlk4vXXX2f9+vVOa3PSpEk899xzDdoodTZ2k0whUpaTl2dDSjfCw8MvfqKCgkK1tIip8MCBA12W+9RgMHD99deTkJDgCDLU2jCZTGzYsIHi4mImTpzY1OJcACs2mY3FAmcyLfj7h+Lv7+8ys5SUEovF4jIvG51OR2ZmJp06dWq2sYzOxmq1NhuPqUuN+uUavjjNXrkLIejSpYvL2ndzc2Py5Mk1brFv6Xh7e7Ns2bJmq2SqFlKhhMICGwYDxMVFu9Rv/c8//+Tpp5/mueeeo1evXk5vPzU1lV27dhEVFdXskrJXx9atW9myZYtTYpoo1J6ioiKio6O5/fbbnd52s1furkar1XLXXXc1tRguRQhR6+D/TYWUuVitFrKzbHh5ebvcVdXd3d2lEUK7du1KTEzMRRaQmw9SSq655hr69evX1KJcUjRpmj2FSxubzcZ3332HVqtl8uTJLjKTGJHkUV4uycu30raN8+LUXIju3bvzySefAGCzWLHoDVgrjKjdtWi8vRAadYPG6uHh4fIxKCjUhKLcWxlnx1dxhiKWUvL111/j6enpkiQnUkrK9NlknsnGbPJE2tQX3ZHqDIQQSCmpyCng5IcrSP1yDRU5+bgF+BF77STib78W79hoZderQotFUe6tCCklr7zyCmVlZTzxxBNOsVmrVCrefPNNp2Syrx4rp08fZ8uWZGJi2hET3c5pHjg1IaXEmFfIH/MXcmbtL2RYDeyjDFEkGPO/DAp2HeCyJYvwbuv6G42CgitoEa6QjYmUEp1O50jK6wosFgt6vb5eacMuhl6vd6pbpxCC8PBwQkNDXaLkJGXEtoMhQzrgrvUkMrL+O1LrSsb3Wziz7hdsVguRaBmJPzYkRTYTedv3kvL599DIkSYVFJyFotz/htls5tZbb+WJJ55wWQjZY8eOsWzZMke2c2fyn//8hxdffNGFM23nIaUNKbNxdxcgvfD09G7QjtS6krpsHdJiRSAQwAkqKMOKF2qk1UbaVz9gMyuZnRRaJopZ5m+o1WomTpzoUve1kJAQunTpUmMY2/oghGj0sLf1xX7jrEBSgF4vKSy0ERMT0WjumtJqxVxqf8KRSKxI+uJNBTayMRGABkupHpvFiroZxBzT6XT8+uuvdOrUicOHDzNs2DCioqLq3M4vv/yCTqdj8uTJzdY1VsE5tFjlfurUKZKTkxkxYoRTI/6p1WrmzZvntPaqIzw8vFnuvrRarWRkZBAYGIifn5/L+5NSh5QGcrJtgBsREREu77MKoVbj3S4a3b4jAKRgJBEDGgS9sUfi9I6NQq1tHj+R5ORktmzZwoIFCyguLub999+vs3IvLi7m4Ycf5tixY6SlpSnKvZXT/J/dL0BxcbGSENnJ5OTkcPXVV/Ppp582Qm82bGRjNkuys6z4+wfg5+fXqIuXHW+agdrLA4EgDg+mEMQkAvFGhcpdS4d/Xo1oRvGM9uzZQ3p6Oh06dKjzuVJK1q1bx+HDh88pt9ls6HS6C67TZGZm8ueff9ZLXoWmpcUq9969ezNr1qxGzYRjs9n4/fffz/uBNCVSSseroQQEBHD77bczcuTIhgtWA/YdqSVAKUVFUFZmIzratTtSqyN89GV0+fc/UXu6V9rd7f/UWi0d511N25lXOKWfhn5HJ06cwGq1YjKZWLx4cb12U1ssFp577jnGjh2L2Wxm165dAJSUlPDqq6+yevXqahf4T548yc8//1wvuRWaluYzLakjrorHUBMmk4mnn36a+Ph43n777Ubt+0KcOHGC559/nvvuu4++ffs2qC0vLy9uvfVWJ0lWM/YdqWbOZFrw9LTvSG3MWbsQAo2XJ90fu52wEQNJXrqa8swc3IMDaXfDFCJGXYabn3NcMs1mM5s3byYmJqZeoQ727dtHmzZtGDt2LFdffTWRkZGYzeY6taHRaPjuu+9ITExk69attGvXDrDf0J999lk2bdpU7SJ8aWkpZ86cQUqpuIS2MFqscm8KtFotL7/8cqP4YdcWi8VCSUlJnX/sTYsJSS4GvaSwwL6Q2lSZkjRenkSOG0rE2CFIqw2hVjldiUkpMRqNDfqOsrOzKSkpIT09ndzc3DqHkxBC0LlzZyIjI1m2bFmtTTt6vZ68vDwsFkutwzsrNA8uqtyFEG2Az4BwQAIfSCnfEEI8BdwK5FVWXSCl/LHynMeAeYAVuEdKucEFsjc6KpXKJUGmGkLXrl1ZuXJlrZ9iKioqWLt2LT169KBr164ulu5cpJT2R3+RBxjJy7Nha6QdqRdDCIHQuOZJUKvVMnXq1HqPUaVS8Y9//AObzcYXX3yBlJKVK1fWqy0/Pz+uuuqqWtfX6/UUFxdjMBhaTJwcBTu1mblbgAeklH8KIXyBfUKITZXHXpNSvnx2ZSFEN+BaoDsQBWwWQnSSUrb6eKIlJSUcOnSI3r17N9rsXghRpyQmxcXFLFq0iGuvvbZRlLvNZqOwsJCcnByKi4s5fPgQY8a2JSpaQ1aWlcDAEHx9fZtcuVdhs9k4cOAA/v7+9Vq4rI6GZtTq3bs3zz777DllroyUejbl5eW4u7tTXl6uKPcWxkW1gpQyC8iq/LtUCJEERNdwylRguZTSCKQIIU4CCcBOJ8jbrNm9ezf33nsvH374IUOHDm1qcaolODiYL7/80uWhXW02G8ePH2flypUkJibi5+eH2WwmNfUk+w+EMHXqWKyWNnRoH9WsNlyVl5fz73//mz59+vDaa681tTiAPf/s0qVLHe9LS0vp1KmT024+NVFeXk5QUBAGg8HlfSk4lzrZ3IUQ7YC+wG5gKHCXEOIfwF7ss3sddsW/66zTMqjmZiCEuA24DaBt27b1kb3ZMWDAAF5//XV69ux5TnlVYoi6zrLry65duzh8+DBz5849LzKhRqNx+azParWyZcsWPvroI8aNG8fEiRPJz89HSklQcBnHju3j5ZeWM3ToaEaMGOVSWeqKh4cHzz33nMtDDteFUaNGsXXrVsf7xvJekVJSUVFBUFAQ5eXljdKngvOotaYRQvgAq4D7pJQlQoh3gWex2+GfBV4Bbq5te1LKD4APAAYMGODUff7OjoxYWwIDAxk/fvx55RaLhdWrVzvMD/369SMsLKxefdRmbFu2bOGHH35gxowZjR52VkrJX3/9xeLFi7n//vvZs2cPL7/8Mh06dEClspKc8hfR0SFMnHglmzZtZfv27UyYMKFRZawJtVrd7J661q1bd87GOovFwtdff+3yfqsmJX5+fhgMBsVjpoVRq+dhIYQbdsX+pZTyWwApZY6U0iqltAEfYje9AGQCbc46PaayrNFISkpi3bp1GI3Gxuz2ggghiIiIoLi4mAceeIBff/212noWi4XExETy8/Mv2NahQ4e46aabOHbsGCUlJRw+fPi8cd51112sWLGCgIAAZw6jVuj1ej766CP+9a9/sWXLFsxmMwsXLmTChAmMGXMZDz98DX6+QWzbtoPbb7+dL774guLi4kaXsyUxZswYtm/f7njt3LmT4cOHu7RPq9WK0WhECIG/vz+lpaUu7U/B+dTGW0YAHwNJUspXzyqPrLTHA0wHqnb2rAGWCSFexb6gGg/84VSpL4LZbMZkMrks8Fdd0Wg0jBgxgvLyctq1a0fnzp2rrZefn8/cuXO5/vrreeCBB6qtYzabHa6PGzZs4Omnn2b58uX06NHDUcff379JFr+klBw/fhyr1YqXlxf5+fncf//9vP7666g1KoTIp6iomLFjp3LwYA5Go5GoqCh27tzZjHO7Nj0BAQGNfqNOS0tj9+7dAAQFBSk34BZIbcwyQ4EbgENCiP2VZQuA64QQfbCbZVKB2wGklEeEECuAROyeNnc2tqdMr1696NmzZ7N7hPT09OSyyy674PGAgAAef/zxcxT13+nXr5/D9TEoKIgnnniiWa1ZJCYmEhcXx5YtW5gwYQI//fQTHTp0QK2GMn0F8XExrFv7M9OmTWPXrl1cdtllHDlyhAkTJjS776shSCmRVhs2sxmkRKV1Q6gblt2pipMnT2K1Wi84SXAGRUVF7Nixg5CQEAIDA8nKyrr4SQrNitp4y/wGVHdF/ljDOc8DzzdArgbRUNezpsLDw4Orr766xjpnL8pGR0cze/bsxhCt1hQWFhIUFERycjKRkZHs2LGD4cOH8/bbLyKlkdGjJ2Iw6OnQoQPbtm3D39+fQ4cONbXYTkVareTvOcSpj78hZ8tOpNVG8KBexN1yDeGjBqFq4GYgvV6P1era+ZLJZCIlJcWh3I8ePerS/hScj7JDVcEppKSkcOLECXx9fcnKyqJt27akpqbSqVMn9uzZydUzh1FQoCczM49evXpTUlJCUFAQeXl5LnfLrCtSSjIyMrBarcTGxtZpoiClJGvT7+y+9QlSMtP5WRYxAB86pWeR8/Mu+r70MB1umlHnyYfBYODpp592xIRZtGhRnc6vKyaTieTkZAYOHEhgYKBilmmBNB8HY4UWTWlpKTk5OXTr1o3k5GSGDx/Oxo0bGTFiBCq1FTc3qKgAg8HMtddey4YNG0hISGDv3r3069ev2T1p3XbbbcyePbvOUUdNhUUc+e/7lGdkEyjVdMWTMqwgJaaCIg49/y6G9Ow6y5OUlMTbb7/N1VdfzZw5c+oVy722SCkxmUwUFBRgMpkU5d5CUWbuCk6he/fudO3aFbPZjL+/P1lZWXTq1In333+f+fP/xf3338OpU6k89dTTrF69Gg8PD9zd3SktLaVfv35NLf55XHfddej1+jptsJJSojt4jMI9dt8CLQItKoz8f7TF8tNZZG/ZUa/ZO9g9qtRqtcudBaqUuk6nw8vLC5VKRXl5eaNGYVVoGJe0ci8tLcVkMhEUFNTsZo4tjaoonW5ubtx+++08+eST3HjjjZSUlPDLL79y9OhJAFJTU+nYsSNRUVF8/vnnzJ8/v9kpDCEE//jHP+p1rrlUj7Wi4oLHpdmCSVf3/Ly+vr4kJCSwbt06wL6wXhXZ0RVUeTKZzWZUKhX+/v4UFRU1u+9K4cJc0maZ33//nR9++KHFJvxITEzk2WefJTc3t6lFOYe4uDgeeughPv/8czw8PPjtt98YPXo0w4cPZ9++fURHR/PJJ59w0003MWjQoFZ1Y3UP8EPjY8/klIuZfZRxAD152CNCqjy0eITVfferWq12uEQGBATUOUKjlJLMzEzS0tJIT0+/aHJ2k8lEbGysY9YeEBBAUVFRneVWaDou6Zl7r1690Ov1LSbv6N85ceIE69atY+bMmfXe8eoKVCoVffv25ZVXXuH333/nyJEjjvALHh4eHD16lJdffpnIyMhWpdiFEAT07kLIZX3I3vQ7YbgxB/ticdUV5hsXS8S4IXUed1FRESaTid69ewPUOTCdxWJh3LhxdOnShZ9//pmPPvqImTNnXrC+0Wikbdu2jpm7otxbHpf0zD0qKor4+HiXB66y2WxkZWWh0+mc2u7EiRP58ccf6dSpk1PbrcJms5GZmVmvxTQhBAEBAWzdupVbbrkFsLvw3XrrrWzfvh0vL69WpdircPPzoedTd+EbH4tQqdEg0CAQQoVnVBi9nrm3XjN3gB07drB06VKWLl1KRkZGnc7VaDRs2bKFxYsX06NHD7Zs2XLOcSklZrOZ7Oxsx4JqbGws3t7e55hlFFoOl/TMvbGwWCxs2LCBqKioamPP1BetVuvSAFdGo5H169fTsWNHRo2qW4AvKSW7d+/myJEj5OXl0alTJyoqKtiwYQPZ2dls2bKFGTPqt6jYnBFCEHJZH4Z/9w6pn39P9uYdSKuV4Mv60OGmGQT161avMbdp04annnrK8b5jx451lsvLy4v58+eTnZ3NO++8A9j3JTz77LOkpaVxww03sHHjRh566CFMJhMDBgxg+PDhCCHo27dvC0sIo6Ao90ZAo9EwevToFrcYpdVqGTt2LH5+fnU+12q18u2333L48GH++c9/4ubmRmlpKWFhYWzfvp3ly5dzxRVXNKusVmC/EX/11VfExMTU+YZWhVCp8O/akd4v/Jtez90HEoTa/nRY35tZeno6CxYsQKvVYjab6dChA+3bt6/1+VarlX/9619s3bqVlStXOm4OgYGBvPrqq6xdu5bS0lL27dvniCsTHBxMhw4dEEIQHV1TlG+F5oii3GtJfn4+ZWVltG3bts5mHJVK1axCBNQWtVpdb48MlUrFlVde6Vj8s9lsaLVaAG699VZ69uzZZKn1asJkMvH555/Tu3fveit3+H8lLpy4njN16lRee+21em9giouLIzQ0lPXr11NYWHhOdiibzYbBYODYsWOOZNxarfb/x9HKnrAuBRTlXksOHDhAWloa119/fbNUSs0NlUrFuHHjGDt2LIcPH2bt2rWo1WpGjhxJQkJCs1UWHh4eLFmypNFDJV8MX19ftm3bxsiRI9HpdFxzzTV1Ol+tVp+XzelsrFYrxcXFmEwmDAYDRqNRuc5bOIpyryUDBgygW7duzTJJcFlZmWPTSXNTmgaDge+++4558+YRGhqKXq9vapFqRKVSuXT3Z3347bff+O677/juu+9ISUnhhRdecPp1aLVaycvLo0OHDo6dqYpyb9lc0t4ydcHf35/IyMhmlRKuikWLFjF37txaZcvJzs4mKSnJ5YGnqtDr9bi5uREcHIybmxsBAQHN7gbkaqxWK0lJSeTk5NTr/IyMDNzc3OjXrx9XX301w4YNIzu77iEMakKlUnHq1Cni4uLIz8/HaDQ6zGgKLZPmp6laAVUuhCUldd+JWB+GDh3KlVdeWasUfklJSezYsaPRPB98fHwwm80UFBRgNpvR6XTNJs5+Y2E2m9mxYwdJSUn1bmPDhg2kpKRw/Pjx89wYnYFarebUqVPEx8eTl5eH2WxWlHsLRzHLuICKigp+/PFHOnfuzIgRI1zeX10SXSQkJNCrV69G++F6enoybdo0lixZghCCMWPGMHDgwEbpu7mg1WqZOnUqnp6e9Tp/9OjRfP3111x//fVIKenZsyeDBw92qoxqtZqKigri4+NJSUlBo9E0y6dUhdqjKHcX4O7uzrhx4+qUDalqE0ldflRnJ95W1zIRhLe3N97e3rWWq6EIIejRo0eNCUiamqpE0EII3N3dnW42UqlUhISE1Pv8gIAAnnnmGdauXYvNZuOWW25x+v4GtVqNt7c3cXFxbNu2DQ8Pj0vOfNbaUG7NLqDKhTAwMLDW5+Tn57Ns2TJSUlJqfY6UkvXr17vkMd2ZVCVPOfvVnLDZbNx77708+uijTS1Ktaxdu5bBgweTmJjIgQMHWLt2rdNNW2q1mjZt2hAeHs7JkycVk0wrQJm5NzI2m42CggK8vLzOmUG7u7sTGRlZp009QgjCwsKUH2IDqXq6qK/ZxNWYzWb0ej1ffvklADqdjiFDhtC9e3en9aFWq7nrrrvw9/fnlVdeaXabyxTqjqLcG5ny8nJWr15Nt27dGDZsmKPcz8+PK664ok5tCSFqzMmqUDtUKhX33HNPU4txQYYOHcpLL73Ef//7X6SUBAcH18ozqq4EBgaiVqvp0KGD09tWaHwU5d7IuLu7M3z48AbZYC+ElJLffvsNo9HI6NGjlQWxVkKbNm24/vrrGTlyJL///jsdOnSoV0gIhUuLiyp3IYQHsA1wr6z/jZTySSFEe2A5EAzsA26QUpqEEO7AZ0B/oACYLaVMdZH8LQ6NRkPXrl1d1v4nn3xCcXExI0eOVJR7KyIyMpLIyEi6dOmCu7t7s9xMp9C8qM3M3QiMllKWCSHcgN+EEOuBfwOvSSmXCyHeA+YB71b+r5NSxgkhrgVeBGa7SH6Fv/HMM89gtVpbbIx6hZpRbOEKteWiUztpp6zyrVvlSwKjgW8qy5cC0yr/nlr5nsrjY0Rzc49opQghaNOmDe3atWt2HinNCSmlI36KgkJrpVbP7UIItRBiP5ALbAJOAUVSyqr8dBlAVUzQaCAdoPJ4MXbTzd/bvE0IsVcIsTcvL69Bg1BQqAsmk4lbbrmF559/3iXt5+fnk5qaetFUdo2NlPKcl0LrplbKXUpplVL2AWKABKBLQzuWUn4gpRwgpRwQGhra0OYUFGqNSqWid+/edO7c2SXtHzhwgC1btmAymVzSfn2w2Wy8//779OrVi4SEBA4cONDUIim4mDqtuEkpi4CtwGAgQAhRZbOPATIr/84E2gBUHvfHvrCqUAdsNht6vb7BybstFgs5OTkYDIaL1rVareTk5FwwcqOUkvz8/GrT7tlsNvLy8s6Lp3Oh/k0mE9nZ2ecpQIPBQE5OTrXjllKSl5dXY9q/qlRxFRUVF6zj5ubGI488wvXXX3+O/Hq93ikB1QYMGMCkSZOa1f4Dq9XKokWLmDlzJiEhIbz//vtNLZKCi6mNt0woYJZSFgkhPIFx2BdJtwIzsXvM3Ah8X3nKmsr3OyuP/yyVZ8A6o9PpWLVqFYMGDXIkRa4PaWlpTJ8+nfnz5zN//vwa62ZnZ3PllVcyZ84cHnroofOOl5eXM2vWLHr06MGbb755zjGDwcDMmTPp378/r776qqM8NTWVGTNmcNddd3Hbbbc5yrdv3869997L4sWLz0mKsWTJEt577z1Wr159Xio5vV7PNddcQ58+fXj99derHcPhw4e54YYbeOqpp2pMAP13srKy+OGHHxg9ejRxcXG1Pq86/P396xR6ojEZNmwYubm5DpNRcXEx77zzDsePH8fLy4v169c3uA+LxVKrIHa1wWq1olKpnLKG5Ey5nNVWQUGBy7JciYvpXSFEL+wLpGrsM/0VUspnhBAdsCv2IOAvYK6U0ljpOvk50BcoBK6VUibX1MeAAQPk3r17GzyY1oTVakWv16PVahuUOMJkMpGbm4uPjw8BAQE11q2aZXt5eVUbOsFms5GdnY2bmxt/N6VVHdNqtef48Ff17+vre47CMxgMFBYWEhQUdE76waKiIsrKyqrdeXuhPs6moqKC/Px8/P398fX1rXG8Z2M2mykvL8fDw6NZzbidhdlsJj4+nn/84x/s3r2bDh068O6772KxWMjOzmbt2rWEhYUxaNCgBve1cOFCnn76aaco5FdeeYUbb7yxwftCTCYTixYtYuHChQ2WCZw3xpSUFPbs2cO///3vep0vhNgnpRxQ3bGL3nqklAexK+q/lydjt7//vbwCqFuaGIXzUKvVTtmootVqiYmJqVVdjUZT4yyipkQWFzp2of69vLyqzSkbEBBwwZtQbRJpeHh41Hq8Z+Pm5taqfcfVajULFizg7bffxtPT0/EUp9FoiImJITg4mNDQ0Hp9dn/Hx8eH6Ohop+yz8PPzIyoqirCwsAa1YzQa8fHxccr4wHljdMVO4yqUHaoKCpcAKpWKW2+9lVtvvbXa4/3793daAvfZs2c7zRX3qquuqtMT2IXQaDR1Tk1YE84aY1hYGGPHjnWCROdzUbNMY6CYZRQUmgYpJX/88Qd79+4lJCSEWbNm1Utpbdq0iePHjwN2u/7vv/8O2HfWTp8+vc4yLVu2DCkl7dq1c3j2jB07ts4eTpmZmXz33Xfnlffq1Yvhw4fXSaZNmzZx4sQJR5kQAikls2fPrrXZaNWqVRQVFXHDDTewcuVKiouLUavVJCQksGPHDgD69etX63j9NZllzvN9bYpX//79pYKCQuNz5swZGRMTI4cMGSLDw8PlN998U692nnvuOfncc8/Jyy+/XEZGRkpvb2953333yREjRtS5rdTUVBkTEyOjoqLkzJkz5VVXXSV79OghP/vsszq1Y7PZ5Pvvvy8jIyPlc889J7t16ya7dOkip0+fLu+55546tfXXX39Jf39/OWbMGBkUFCQBec8990g/Pz+5f//+Wrdz4403yuDgYFlcXCzj4+PlI488In18fOT//vc/6efnJ4cOHSrnzp0rDQZDrdoD9soL6FUl+IiCwiVMVerDr7/+mk6dOlFYWFivdh5//HHuuOMOvL29qaioYO7cufTp06fOi9NWq5VPPvkEvV7PmTNnWLNmDffee2+9wzG/9dZblJWVcfz4cXx8fLjzzjvp0KFDnW3lYWFhdOzYkS1btlBYWEibNm248cYb6xzm46677nI8GfXr149p06Y51ta6devGN998w3fffceZM2fq1G51KMpdQUEBk8nUoB21RUVFzJkzh9OnT/Ppp5+Sk5NDu3btLuqh9Xd0Oh3vvvuuI5S1EKJBik6tVnPNNdcQGRnJH3/8QWZmJn369KmzG2NaWhonTpzgwQcfxN/f3+GV1RBKSkqQUtKli31PqM1mw2Qy1Tqr2sVQFlQVFC5hvLy86NixI0OHDsVms9U7lvuCBQvYtGkTTz31FKdOnWLDhg14eXmRm5tbp3YCAwP566+/2Lp1K4mJiYwfP56PPvqo3k8UCxcu5KGHHqJnz54ALF++nPbt2zve15agoCCCg4NZtmwZZWVlWK1WPv300zrvQv7rr78wGo2sXr2a/fv38/777/PHH38wYcIE9u/fz2WXXcbcuXNp27ZtndqtDmVBVUHhEic1NZW0tDR8fHzo169fvWaNSUlJ1Spyf39/+vTpU+f2CgsLOXnyJMHBwWRkZADQpUsXwsPD69ROUVFRtaEWoqOj67xZ7UJj7N+/f62jdf7111/n7eJWq9XExsaSnGzfDtSmTZta32RrWlBVlLuCgoJCC6Um5a7Y3BUUFBRaIYrNXUFBodlTXFzMvn37zimLj4+nTZs2TSRR80dR7goKCs0eq9VKUVERq1evJikpiXnz5jliFf3dyycwMBCdTgfYw1HU1U7fWlCUu4KCQrMnKCiIGTNmcOLECYqKili7di1FRUW88cYblJSUYLFYGDZsGIcOHSIhIYHt27czePBgbDYbmzZtamrxmwTF5q6goNDisFgsWK1WzGYzr7zyCu3bt+fGG2/Ey8uLH3/8kU6dOtG2bVuGDRvW1KI2GYpyV1BQaNEMGjSIyZMnc/XVVwMQFRWFyWSic+fO9faPbw0oZhkFBYUWwxVXXEHv3r2xWq1ERkbSpk0b2rZtyzXXXIO7uzsvvPACvXr1YufOnUgpXRZxsSWg+LkrKCgotFAUP3cFBQWFSwxFuSsoKCi0QhTlrqCgoNAKUZS7goKCQivkospdCOEhhPhDCHFACHFECPF0ZfkSIUSKEGJ/5atPZbkQQrwphDgphDgohOjn4jEoKCgoKPyN2rhCGoHRUsoyIYQb8JsQYn3lsYeklN/8rf5EIL7yNQh4t/J/BQUFBYVG4qIz98pUfWWVb90qXzX5T04FqpId7gIChBCRDRdVQUFBQaG21MrmLoRQCyH2A7nAJinl7spDz1eaXl4TQrhXlkUD6WednlFZ9vc2bxNC7BVC7M3Ly6v/CBQUFBQUzqNWyl1KaZVS9gFigAQhRA/gMaALMBAIAh6pS8dSyg+klAOklANCQ0PrJrWCgoKCQo3UyVtGSlkEbAUmSCmzKk0vRuBTIKGyWiZwdpDlmMoyBQUFBYVGojbeMqFCiIDKvz2BccDRKju6sCdcnAYcrjxlDfCPSq+Zy4BiKWWWC2RXUFBQULgAtfGWiQSWCiHU2G8GK6SU64QQPwshQgEB7Af+VVn/R2AScBIwAP90utQKCgoKCjVyUeUupTwI9K2mfPQF6kvgzoaLpqCgoKBQX5pFVEghRClwrKnlaCRCgPymFqIRUMbZulDG2TyJlVJW65HSXOK5H7tQ2MrWhhBi76UwVmWcrQtlnC0PJbaMgoKCQitEUe4KCgoKrZDmotw/aGoBGpFLZazKOFsXyjhbGM1iQVVBQUFBwbk0l5m7goKCgoITaXLlLoSYIIQ4Vhn//dGmlqchCCE+EULkCiEOn1UWJITYJIQ4Ufl/YGV5i417L4RoI4TYKoRIrIzxf29leasaaw25DNoLIXZXjudrIYS2sty98v3JyuPtmnQAdaQyQOBfQoh1le9b3TiFEKlCiEOVOSj2Vpa1quu2iiZV7pW7Xt/GHgO+G3CdEKJbU8rUQJYAE/5W9iiwRUoZD2ypfA/nxr2/DXvc+5aCBXhAStkNuAy4s/J7a21jrcpl0BvoA0yoDKnxIvCalDIO0AHzKuvPA3SV5a9V1mtJ3AsknfW+tY5zlJSyz1kuj63turUjpWyyFzAY2HDW+8eAx5pSJieMqR1w+Kz3x4DIyr8jsfv0A7wPXFddvZb2Ar7HHnOo1Y4V8AL+xJ54Jh/QVJY7rmFgAzC48m9NZT3R1LLXcnwx2BXbaGAd9rAirXGcqUDI38pa5XXb1GaZWsV+b+GEy/8PnJYNhFf+3SrGXvlI3hfYTSsc699zGQCngCIppaWyytljcYyz8ngxENyoAtef14GHAVvl+2Ba5zglsFEIsU8IcVtlWau7bqH57FC9JJBSSiFEq3FPEkL4AKuA+6SUJfYAoXZay1illFagT2Vk1NXYcxi0KoQQVwK5Usp9QoiRTSyOqxkmpcwUQoQBm4QQR88+2FquW2j6BdVLIfZ7zlnhkSOxzwChhY9d2PPprgK+lFJ+W1ncKscK5+QyGIw9dWTVxOjssTjGWXncHyhoXEnrxVBgihAiFViO3TTzBq1vnEgpMyv/z8V+s06glV63Ta3c9wDxlavyWuBa7PHgWxNrgBsr/74Ru326qrxFxr0X9in6x0CSlPLVsw61qrGK6nMZJGFX8jMrq/19nFXjnwn8LCuNtc0ZKeVjUsoYKWU77L/Bn6WU19PKximE8BZC+Fb9DYzHnoeiVV23Dpra6I899vtx7LbMx5tangaO5SsgCzBjt8/Nw26L3AKcADYDQZV1BXZPoVPAIWBAU8tfh3EOw267PIg9lv/+yu+xVY0V6AX8VTnOw8DCyvIOwB/YcxasBNwryz0q35+sPN6hqcdQjzGPBNa1xnFWjudA5etIlb5pbddt1UvZoaqgoKDQCmlqs4yCgoKCggtQlLuCgoJCK0RR7goKCgqtEEW5KygoKLRCFOWuoKCg0ApRlLuCgoJCK0RR7goKCgqtEEW5KygoKLRC/g/l4+WPh0bJOQAAAABJRU5ErkJggg==\n" }, "metadata": { "needs_background": "light" @@ -227,13 +227,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "outputs": [ { "data": { "text/plain": "{'width': 200,\n 'height': 200,\n 'EP_MAX_TIME': 100,\n 'seed': 0,\n 'reset_rng_episode': False,\n 'arrival': mobile_env.core.arrival.NoDeparture,\n 'channel': mobile_env.core.channels.OkumuraHata,\n 'scheduler': mobile_env.core.schedules.ResourceFair,\n 'movement': mobile_env.core.movement.RandomWaypointMovement,\n 'utility': mobile_env.core.utilities.BoundedLogUtility,\n 'handler': mobile_env.handlers.central.MComCentralHandler,\n 'bs': {'bw': 9000000.0, 'freq': 2500, 'tx': 30, 'height': 50},\n 'ue': {'velocity': 1.5, 'snr_tr': 2e-08, 'noise': 1e-09, 'height': 1.5},\n 'arrival_params': {'ep_time': 100, 'reset_rng_episode': False},\n 'channel_params': {},\n 'scheduler_params': {},\n 'movement_params': {'width': 200, 'height': 200, 'reset_rng_episode': False},\n 'utility_params': {'lower': -20, 'upper': 20, 'coeffs': (10, 0, 10)}}" }, - "execution_count": 9, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -254,20 +254,20 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "outputs": [ { "data": { "text/plain": "Text(0.5, 1.0, 'mobile-small-central-v0')" }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": { "needs_background": "light" @@ -293,20 +293,20 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "outputs": [ { "data": { "text/plain": "Text(0.5, 1.0, 'mobile-medium-central-v0')" }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -332,20 +332,20 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "outputs": [ { "data": { "text/plain": "Text(0.5, 1.0, 'mobile-large-central-v0')" }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -392,20 +392,20 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 9, "outputs": [ { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 20, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": { "needs_background": "light" @@ -466,9 +466,40 @@ "env.reset()\n", "# random first step\n", "env.step(env.action_space.sample())\n", - "plt.imshow(env.render(mode='rgb_array'))\n", - "\n", - "# FIXME: this breaks if I start BS IDs at 1, not at 0. why do I need these IDs anyways? why are they not set automatically?" + "plt.imshow(env.render(mode='rgb_array'))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Extending `mobile-env`: Adding a Handler for a Custom Observation Space\n", + "\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "data": { + "text/plain": "Box([-1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1.], [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.], (15,), float32)" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.observation_space" ], "metadata": { "collapsed": false, @@ -477,6 +508,85 @@ } } }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "data": { + "text/plain": "['connections', 'snrs', 'utility']" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.handler.ftrs" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 0. 0.387414 1. -1. 0.\n", + " 0. 0.5115964 1. -1. 0. 0.\n", + " 0.21166208 1. -1. ]\n" + ] + } + ], + "source": [ + "obs = env.reset()\n", + "obs, reward, done, info = env.step(env.action_space.sample())\n", + "print(obs)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Step 3: Single-Agent RL with `stable-baselines3`" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Step 4: Multi-Agent RL with Ray RLlib" + ], + "metadata": { + "collapsed": false + } + }, { "cell_type": "code", "execution_count": null, diff --git a/mobile_env/handlers/central.py b/mobile_env/handlers/central.py index 6145d87..c9ea8fd 100644 --- a/mobile_env/handlers/central.py +++ b/mobile_env/handlers/central.py @@ -7,11 +7,11 @@ class MComCentralHandler(Handler): - ftrs = ["connections", "snrs", "utility"] + features = ["connections", "snrs", "utility"] @classmethod def ue_obs_size(cls, env) -> int: - return sum(env.feature_sizes[ftr] for ftr in cls.ftrs) + return sum(env.feature_sizes[ftr] for ftr in cls.features) @classmethod def action_space(cls, env) -> spaces.Dict: @@ -40,7 +40,7 @@ def observation(cls, env) -> np.ndarray: """Select from & flatten observations from MA setting.""" # select observations considered in the central setting obs = { - ue_id: [obs_dict[key] for key in cls.ftrs] + ue_id: [obs_dict[key] for key in cls.features] for ue_id, obs_dict in env.features().items() } diff --git a/mobile_env/handlers/multi_agent.py b/mobile_env/handlers/multi_agent.py index 7706dd9..e50d720 100644 --- a/mobile_env/handlers/multi_agent.py +++ b/mobile_env/handlers/multi_agent.py @@ -8,11 +8,11 @@ class MComMAHandler(Handler): - ftrs = ["connections", "snrs", "utility", "bcast", "stations_connected"] + features = ["connections", "snrs", "utility", "bcast", "stations_connected"] @classmethod def ue_obs_size(cls, env) -> int: - return sum(env.feature_sizes[ftr] for ftr in cls.ftrs) + return sum(env.feature_sizes[ftr] for ftr in cls.features) @classmethod def action_space(cls, env) -> spaces.Dict: @@ -73,7 +73,7 @@ def observation(cls, env) -> Dict[int, np.ndarray]: # select observations for multi-agent setting from base feature set obs = { - ue_id: [obs_dict[key] for key in cls.ftrs] + ue_id: [obs_dict[key] for key in cls.features] for ue_id, obs_dict in features.items() } From 676b815740ce221fa506d9cce5fb9cd115b02fd8 Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Fri, 10 Dec 2021 11:33:49 +0100 Subject: [PATCH 11/14] plot utlity, extend demo notebook --- docs/images/utility.png | Bin 0 -> 21008 bytes docs/scripts/utility.py | 35 ++++ examples/demo.ipynb | 314 +++++++++++++++++++++------------ mobile_env/handlers/central.py | 4 +- 4 files changed, 242 insertions(+), 111 deletions(-) create mode 100644 docs/images/utility.png create mode 100644 docs/scripts/utility.py diff --git a/docs/images/utility.png b/docs/images/utility.png new file mode 100644 index 0000000000000000000000000000000000000000..83d3795632d5fa608b8c92ac7c3e5ccc38c702a6 GIT binary patch literal 21008 zcmdSBWmJ{h7e4xKQbIt|08B~*q(nqoMd=1 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zr@mNwxc+;@x?c=pNlX>Mfy0vLUkbi-0T*f=)D>qlSa{M(Rr{zx?qSdxPWVfB*F95pX56feA+fgL8;wgR0Z#ufSQ>Wj<$<==S}u^3430k^0sdI4jVZ^E;BZRbc}59i@Lf8V-X jX$M)ds!w2r`=kB{-^)W@eyIw;>X*UO)z4*}Q$iB}`5T{W literal 0 HcmV?d00001 diff --git a/docs/scripts/utility.py b/docs/scripts/utility.py new file mode 100644 index 0000000..0e92055 --- /dev/null +++ b/docs/scripts/utility.py @@ -0,0 +1,35 @@ +import matplotlib.pyplot as plt +import numpy as np + + +MIN_UTILITY = -20 +MAX_UTILITY = 20 + + +def log_utility(curr_dr): + """ + More data rate increases the utility following a log function: High initial increase, then flattens. + :param curr_dr: Current data rate + :param factor: Factor to multiply the log function with + :param add: Add to current data rate before passing to log function + :return: Utility + """ + # 4*log(0.1+x) looks good: around -10 for no dr; 0 for 0.9 dr; slightly positive for more + # 10*log10(0.1+x) is even better because it's steeper, is exactly -10 for dr=0, and flatter for larger dr + # with many UEs where each UE only gets around 0.1 data rate, 100*log(0.9+x) looks good (eg, 50 UEs on medium env) + + # better: 10*log10(x) --> clip to [-20, 20]; -20 for <= 0.01 dr; +20 for >= 100 dr + # ensure min/max utility are set correctly for this utility function + assert MIN_UTILITY == -20 and MAX_UTILITY == 20, "The chosen log utility requires min/max utility to be -20/+20" + if curr_dr == 0: + return MIN_UTILITY + return np.clip(10 * np.log10(curr_dr), MIN_UTILITY, MAX_UTILITY) + + +dr = [i for i in range(100)] +util = [log_utility(dr) for dr in dr] +plt.plot(dr, util) +plt.xlabel("Data Rate [Mbit/s]") +plt.ylabel("Utility (QoE)") +plt.title("User utility based on data rate") +plt.show() diff --git a/examples/demo.ipynb b/examples/demo.ipynb index 8fa1593..1569c29 100644 --- a/examples/demo.ipynb +++ b/examples/demo.ipynb @@ -17,7 +17,7 @@ "* `mobile-env` is written in pure Python and can be installed easily via [PyPI](https://pypi.org/project/mobile-env/)\n", "* It allows simulating various scenarios with moving users in a cellular network with multiple base stations\n", "* `mobile-env` implements the [OpenAI Gym](https://gym.openai.com/) interface such that it can be used with all common frameworks for reinforcement learning\n", - "* It supports both centralized, single-agent control and multi-agent control\n", + "* It supports both centralized, single-agent control and distributed, multi-agent control\n", "* It can be configured easily (e.g., adjusting number and movement of users, properties of cells, etc.)\n", "* It is also easy to extend `mobile-env`, e.g., implementing different observations, actions, or reward\n", "\n", @@ -56,13 +56,13 @@ "Requirement already satisfied: shapely==1.8.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (1.8.0)\n", "Requirement already satisfied: svgpath2mpl==1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (1.0.0)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (1.3.1)\n", - "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (8.4.0)\n", - "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.4.7)\n", "Requirement already satisfied: setuptools-scm>=4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (6.3.2)\n", - "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.8.1)\n", - "Requirement already satisfied: packaging>=20.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (21.3)\n", "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (4.28.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (8.4.0)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (21.3)\n", "Requirement already satisfied: cycler>=0.10 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (0.10.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.4.7)\n", + "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.8.1)\n", "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from gym>=0.19.0->mobile-env) (1.6.0)\n", "Requirement already satisfied: six in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from cycler>=0.10->matplotlib==3.5.0->mobile-env) (1.15.0)\n", "Requirement already satisfied: setuptools in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (46.1.3)\n", @@ -84,9 +84,7 @@ { "cell_type": "markdown", "source": [ - "`mobile_env` comes with a set of predefined scenarios,\n", - " registered as Gym environments,\n", - " that can be used out of the box for quick experimentation." + "`mobile_env` comes with a set of predefined scenarios, registered as Gym environments, that can be used out of the box for quick experimentation." ], "metadata": { "collapsed": false, @@ -132,18 +130,16 @@ { "cell_type": "markdown", "source": [ - "Here, we consider a small scenario with 5 users and 3 cells.\n", - "As the users move around, the goal is to connect the users to suitable cells,\n", - "ensuring that all users have a good Quality of Experience (QoE).\n", + "Here, we consider a small scenario (called `\"mobile-small-central-v0\"`) with 5 users and 3 cells.\n", + "As the users move around, the goal is to connect the users to suitable cells, ensuring that all users have a good Quality of Experience (QoE).\n", "\n", "Using coordinated multipoint (CoMP), users can connect to multiple cells simultaneously.\n", - "Connecting to more cells increases the user's data rate but also increases competition for resources\n", - "and may decrease data rates of other users.\n", - "Therefore, a good coordination policy needs to balance this trade-off, depending on available resources and\n", - "users' positions (affecting their path loss).\n", + "Connecting to more cells increases the user's data rate but also increases competition for resources and may decrease data rates of other users.\n", + "Therefore, a good coordination policy needs to balance this trade-off, depending on available resources and users' positions (affecting their path loss).\n", + "\n", + "To measure QoE, `mobile-env` defaults to a logarithmic utility function of the users' data rate, where -20 indicates bad QoE and +20 indicates good QoE. This can be easily configured and changed.\n", "\n", - "To measure QoE, `mobile-env` defaults to a logarithmic utility function of the users' data rate,\n", - "where -20 indicates bad QoE and +20 indicates good QoE. This can be easily configured and changed.\n", + "![Utility plot](../docs/images/utility.png)\n", "\n", "To get started, we will use random dummy actions." ], @@ -161,7 +157,7 @@ { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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Xr17ExMTQunVr0tLSOHbsGKNGjaJLly6cO3euQTLUhczMTDZu3Mi5c+cwmUyYzWZMJhP5+fl8+OGHDB06lI4dO1qjQfr5+TFs2DD69etHYmIiQUFBnDt3jqysLDQaDWq1moCAAMaMGUNiYmKNAc0ai3rfsoUQ7oBKSllU8X4s8B9gLXA7sLDi/x9tIWhLRKfTsX37dvr374+vr29Ti2Pl6NGjvPzyy7Ru3ZohQ4ZUWadLly506dLlim1oNBo++eSTak02KSkpPPjgg8yZM4cJEyY0WO6WiEajYfbs2RQXF7N06VKKiooYOXIkZrOZ8PBwYmNjCQ8PJyIiAm9vb6Bc2c6ZM4fffvuN999/H7PZzIgRI/Dw8MDb25sbb7yRzp07Ex8fz6uvvsr8+fPrbVoTQtCtWzeOHz/Ot99+yy233MLOnTvp06cP+fn5DBgwwGoCmzRpEuvWrcPDw4PBgwfj7+/PqlWrcHV15dZbb7XZ36wmIiMjueOOO8jMzOTLL78kJyeHzZs306ZNG7Kzs9mzZw9lZWXExsZy/PhxvL29+eWXX9BoNMycOZOgoCCSk5NZsWIFM2bMIC8vj++++w4hBDNnznQY77Z6+7kLIdpRPluH8pvEMinlS0IIf2AF0AZIptwVstoo+Fern7vZbCY3NxdPT896JZ2uLzqdjuzsbMLCwqrcsl9SUsLZs2dp166dXRNlpKen83//93/cdtttDB8+3G79KCi0VJTAYQqX8M033/Daa6+xatWqRvdQuBglabaCQsOoTrkrOziuQmJjY5k7dy5BQUFNKocQwvqqCqPRSFxcXKN7IJSWlnLq1Ckld6tCs0ZR7lchHTt25J///KfVRuuoFBYW8tRTT7Fs2bJG7ff8+fNs2rQJxYtLoTmjBA5rgVgsFnJzc3Fzc2sWG3WuhLe3Nx9++CEhISGN2m/r1q2ZNGlSkz3ZJCcn8+mnnzZqUmUPDw9MJlOzfFpxcXFBo9E0SRJqW3DDDTdYfeptiWJzb4Hk5OQwZcoUJk+ezCOPPNLU4ijUEYvFgtlsbtT9AUKIZr0foTnLr1arG+KtpCTruJpwc3Nj0qRJxMRU+Z0rODgqlUoJaKbQYJQryM5UBhhqzFmFm5sb//73vxk6dGjNlZspxcXFPPXUU/z8889NLYqCgkOizNztzB9//EFGRga33HKLkgbOhhgMBo4cOUJERERTi2JXjEYj//d//8c999xDSEgI8+fPx9nZmbvuuovOnTtb633xxRd4e3szZcqUKtspKyvDycnJbnGAUlJSWL16NSEhIUyaNAlXV1eklJSVlbF06VKuueYafHx8+PHHHxFCcP3116NSqVi9ejVqtZpJkyYRHh5uF9muRFZWFsuWLSM8PNy6i7e0tJTPPvuMcePGERUVBUBRURFr1qyhsLCQG264AVdXV3788UdKS0sZNWoUoaGh/PDDDxQWFjJs2DB69uzpEK69yszdznTs2JHevXs3u+Bajo6vry8rVqxg9uzZTS2KXTGbzXzzzTdkZGRQWlrKkiVLMJlMlJSU8MEHH/DBBx+waNEiPvroIz766CPr58rX4cOHWbRoEbfffjsLFy4kJSWF3377jQ8++IALFy7YRMaMjAweffRR607O999/3/qk+uGHH7Jo0SL27duHh4cHU6dORaVS8cgjj2A0Gpk6dSqurq589NFHNpGlLri7uxMTE8PSpUspLS0lMzOTV155hXfeeYcNGzZY623evJlDhw4xbNgwnnzySVQqFdOmTSMwMJBHH32UsrIybrrpJtq3b8/jjz9Oampqo4+lKpSZu52JjIy0W9tSSs6ePYtGo6F169YOMVtoLIQQzdoTqK688847+Pr6UlZWxttvv01kZCRPPvkk1157LWq1muDgYAICArBYLDzxxBP07duXXbt28frrr/PYY4/xzDPPsGnTJn766ScCAwNxcXHhrrvusolsiYmJbN26lf/+97/o9Xreffdd5s2bx5tvvsnbb7+N2WwmMTERHx8fiouL+emnn/D09CQkJAQpJdu2bWuS/Kfu7u6EhoayZcsWiouLiYuLY9GiRcyYMYONGzfyr3/9C4D8/Hw2btxIZmYmq1at4uWXX2b9+vV88skneHh4oNFoWLVqFR9//DEGg8Fh1ksU5d6MMZvNPProo3h5efHFF180tTgKdmTevHl06tSJb7/9FqPRiL+/P9dddx2//vorU6ZMoaysjMDAQCIiIggJCeHDDz+0xgUKCAjg0UcfJSYmhsmTJ6NWq9m4caPNQl44OztjsVjYunUrGzZswM/Pj9LSUpYtW0arVq0oKioiLi6OOXPm8Pzzz3PgwAFrtNAFCxawcePGRt3LsHz5cnbv3k2nTp0YM2aMtXzlypX4+/tz4MABMjIyOH78ONHR0fTv35/77ruPkpISVq9eTUlJCbNnz6Z9+/bcdNNNJCYmMn36dDp27MjEiRM5ffo0YWFhjTaeK6Eo92aMWq3mgQcecJhARVcDxcXFnD17lqioKLvG3bmYyty5Qgi0Wi1arZb8/Hz0ej3Dhg0jPj6ejh078tdff5GRkcH58+fp378/JpMJtVptDWs7duxYBgwYwOHDh+nWrZvN5OvduzcTJkzgq6++Ij4+nrfeeovFixezfft2XFxc+Mc//kF0dDR//fUXX331Fddeey1xcXF89tlnfPPNNwwZMsRmWaFqw9SpU5kyZQpJSUl8+OGHAGzdupWVK1cipeSdd97hjz/+wN/fn9deew0vLy/effddvLy8GDRoEPv27eOtt97i5MmT9OzZk9TUVN5//32ys7OJjIy069N6XVD83BVaFCUlJRw8eJDOnTvbJdLmqVOn2Lx5M5MmTWqU2ZmUkoyMDPz8/NBoNGRkZCClxNvb25omUKvVotFoKCkpuez8yoQdQUFBbNiwgSeffJKZM2dabce2Ijc319q/j48PBQUFhIaGolKpuHDhAk5OTmg0GnJzq44h6Orqir+/v83kqQ1lZWXWsNZubm74+fkB5Quoer0eb29vsrKy8PLysv6tPT090Wq11vUKd3d33NzcrLuZG3scSuAwhauGgwcPcuutt/LCCy9c0XOkIRgMBvLz863KtrlgsVisoWyjoqIcwmyg0HAU5a5w1VBcXMyOHTvo06cPAQEBTS2OgoJdUXaotgCU8Li1w8PDg7Fjxza1GAoKTY6i3JsJeXl5PP7440yaNInrr7++qcVRsCOFhYUcPnz4klykCi2XqKgo2rRpY/N2FeVuQ8xmM2VlZbi4uNh805LZbKawsLBZRu1TqBsmk4mCgoJGTRqt0HTY6zet2NxtSFJSEps2beK6666z+YJVZbJeJahU3ZBSWl0Clb+bQktDycTUSHh7exMdHY2np2edzzUajaSkpFBaWlrlcSEEGo3mEgUlpSQrK4ucnJxmG+7U3pw/f56bb75ZCTCmcNWhKHcb4uvry9ChQ/Hw8KCgoKBOj1s5OTmsW7eOpKSkWp8jpWTr1q1s3769HtJeHWg0Gvz9/XF3d29qURQUGpUazTJCiM+BiUCWlLJbRZkf8B0QCSQB06SUeaLcjeMdYAJQAtwhpdxfkxAtxSxTSUlJCcuWLaNTp07WLeA1YTAYSElJITQ0tNYxU6SUpKWlWWOLKF40l6N4GSm0ZBpqllkCjPtb2ZNAnJSyAxBX8RlgPNCh4jUH+LA+Ajd3tFotffv2pV27dnU6p3379pcodiml9VUVQgjCwsIICQmpteIym82cOXOGvLy8WsvWnKkpCXdDqOn7UVBoSmpU7lLK34G/7xmeBHxZ8f5LYPJF5V/JcnYBPkKIUBvJ2mzQaDT07t27wYuqJSUl/Otf/2Lp0qU2kgxKS0vZsmUL8fHxNmvzaqXSLPbXX38pCl7B4aivK2SwlDK94n0GEFzxPgxIuaheakVZOs0Yi8VCaWmpXVwcq0NKSVFRUZUxQ+qLq6srEydOxMvLy2ZtXq1IKTEYDIo/uoJD0mA/dymlFELUedoihJhDuenGLg78tiQ7O5sff/yRkSNHWrOzNAbu7u589tlnNnXhU6vVSlwRG6FSqbj22msBx7Tnm81mTCYTUP69WywW6xNGZaRJhZZLfZV7phAiVEqZXmF2yaooPw+0vqheeEXZZUgpFwOLoXxBtZ5yNAru7u52izJYHZXuj46KyWQiPT2dvLw8tFotoaGheHl5OazS0Ol0HDp0iC5duuDj49Pg9uxly7cVK1euZOHChbi5uREVFcXBgweJiIjgzJkz7Ny5E29v76YWUcGO1FdzrAVuBxZW/P/jReUPCCGWA/2BgovMN80WDw+PWnu9tDSMRiM6nQ5PT0/UarXVFLFz506+//57Lly4gLe3N3q9nrKyMvr378+UKVMICwtzOMWXkJDAPffcwyuvvMINN9zQ1OLYnWHDhhEdHc2JEyeYNWsWbm5uzJs3j0ceeURZI7gKqFG5CyG+BYYDAUKIVOBZypX6CiHEbCAZmFZR/WfK3SBPU+4KeacdZHYoLBYLJpPpsg1GLYXTp09bs/0EBQWh1+v54IMPOHz4MFOmTCEkJITs7GycnZ3x8vLit99+4/HHH+fJJ5+ke/fuDqXgO3XqxFtvvUXfvn2bWpRGofK7eeaZZ7juuuvw9vZm/PjxrFixwlonPz+f1157DYPBQFBQkM2yMynUDoPBQPfu3e0S7E4JP9BAMjMz2bBhA8OHD3eYDCy2JC8vj9OnT9OtWze0Wi2ffPIJhw4dYtasWXz77bcUFRXRtm1bioqKOH/+PGPGjMHHx4dvvvmGd95556rL7epIxMfHM3nyZHr06MHixYuJjY3l0UcfZcGCBZw8eRIfHx8sFgtFRUVs27aNwMBAevbs2dRiX1WcOXOGuLg4HnnkkXqdr4T8tSPOzs6EhobadAdkSUkJZ86cuczvvSnw9fWlX79+wP9m8XPnzuWTTz5h4sSJdOjQgZycHNzc3PDw8OCzzz6jY8eOjB8/niVLljB//nyHXjdoySQlJREeHo67uzvPPvss7du3Z/v27fTv39/6nahUKry9vXF3d8fV1RUPD48mlvrqwp6/76v6V6fX67FYLLi4uNR7dunj42P1mLAVe/bs4b777uODDz5g2LBhNm27vkgp2bhxI0OGDGHPnj3ExsYSHBzMf//7X4KCgtDpdAQEBHDPPffw4osv8thjj/H666+Tlpbm8N5QLZUJEyYwYcKEphZDoYloeUbiOrB9+3bWrFljdRdzFLp27cqCBQvo2rVrU4tiRa/Xc+jQIbp3786BAwcYOHAgn3zyCVOnTkWj0TBw4ECKiopITEwkNjaWI0eOEBgYSFZWlrJ4p6DQBFzVM/c2bdrg7e3tcAuhAQEBTJ8+vanFuASj0UhxcTGurq4YjUZcXFys8esPHTpEWloaI0aMIDExkaCgIC5cuICLi4tNN2ApKCjUHsfSao1Mx44d6devX6PuOm2uaLVafHx80Ol0uLq6UlxcjLu7O9KYxoiBrZl8/Qh27txBu3btSElJsZpq6hP+2J6kp6dz+vRpzGazzdosLCwkLy9PeUJRcCiuauWuUHu0Wi0DBgxg//799OnTh23btnHvvXNITz2FNBWiL9hHfm4m55JOc+Twfjp2aE9eXh6hoaEO5S3z9NNPM27cOAoLC23W5i+//MLy5csdzryncHWjKHeFWiGEYPjw4ezatYs+ffpw4sQJ9u7dx+CRs/hs2W52/HWWGyf25qf1yxk7xJ/NP31KbO82BPq7OlTkxBtuuIF//vOfuLq62qzNLl260KdPH7ua97Kzs4mPj3eov6WCY3NV29wdDZPJxMmTJwkKCiIgIKCpxbmMsLAwpk2bxrfffsudd95JYmIiq79fi9mi4redyUREXcN1E8ZSWpZJfMJxXnt+OqrizaAJBudIpNoPVG5NOpOfPHmyzdvs1q2bzdv8O+fOnWPSpEn4+fkxf/58Ro8eXeM1kpaWxvHjx+natSulpaUkJiYC5ebI8PBwu8us0LQoM/dGQkpJYWEh+fn5V5x55eTkMGvWLL788ktyc3N58cUXOXjw4CV1jEYj2dnZGI3GRpD6UlQqFZMmTWLQoEF89tlnREREcOrUKQYMGEC/2P4UFBkoMXjx6+4Cbpl1HwX6YAqKSjGWnEEWbYOiOCjZizRmIS16ZQZaB8LDw/n3v/9N+/btueWWW7jhhhv44Ycfqj1n+/btjBs3jm3btvHNN9/w1FNP8dBDD/Hjjz9We55Cy6BFKvfKR1hHs4E+/vjjPPDAA1gsliqP+/j48PTTTzNx4kTy8/NZv349x48fv6TOjh07GD9+PPv3XznBVWU0QHsoT41Gwy233MITTzzBpk2byMzM5MCBAyQkJPDnn3/St29fFi/+hFFjb8IraDBpRT1ISAkiOUOLTpePpfQYsnBjuaIvPYo05SGlWVH0NXD+/Hmefvpp8vLyePbZZ+nXrx9nz56t9pypU6da3WlVKhWzZ88mNPTS9ApSSvR6fZNMFhTsS4s0y5w4cYL4+HgiIiLsvjvSYDCQkZFRq7gcI0eOpKys7IpmCRcXF2666SagXEH/9NNPl+0YjIiI4MYbb6w2bO8XX3xBXFwcH330kc0j/1VGQtRqtZw8eZKZM2fy3HPP0aVLF/r27cvu3bsZPnw4KpWK4OAQAgOD0Os7kpubw9mcNIQ5G1/3Qnw9c3DRZiPKjoEmALQRSE0wqD0dagHWUZBS8p///Ie7776bQ4cOUVZWxsCBA6s9R6VSWf+WQgiCgoK4/vrrL/n75ufn8/LLL5OamlrvLfAKjkmLVO59+vQhOjrapotmVyI7O5t169YxZswY3N3dcXFxwd/f/7J6QgimTZtWRQtVo1arq2wnMjKSp59+utpzPT098ff3t9sCn9lsZuXKlRQXF7Njxw769u2Lk5MT8fHxpKWlMXXqVKKiohBCoFKpcHV1pVWrcEJCWqHT6cjLzeF0ZipaVS7+nsV4up5HaziPUHuAJgipjQSNP6hcAMcOq9sYxMXF8fjjj1NcXMy6devIy8vj5ZdfrtHNNDc3l9LSUpKTkzl//jxbtmyhqKiI22+/3VrH19eX1157jc2bNysuwS2MFqnc3dzcGi0mS0BAAOPGjcPX15cZM2bQrVs33nzzzUbp+0pMmzatTjeSupKZmcmqVasoKChg3rx5/P777/Tr14+DBw9y6NAhNm/efFlSEyEEarUaT09PPD09sbSOoKCggJycLM6npeLmlE+gdykermdQ6xMRGh/QhIJzBFLtC8LJ2k59yc7O5rvvvmPixInNKshbaGgoo0eP5syZM8TElMeIio6OrvG87Oxs5s2bB5Sb/Lp37w7A4MGD7SarguPQIpV7Y+Ls7Ez79u0xmUzceeedtGrVqqlFqlYBXmzbrq+i9Pb2Zs6cOaSkpJCbm0unTp3Q6XR06NCBBx98kAEDBlTbL5Q/mfj6+uLr64vJ1J68vFzSszMw5aTj6ZxPgHcB7q55oD+J0PiCUxvQtipX9PzP1FAXUlJS+OSTT2jbtm2zUu4JCQlERETw/vvvs2XLFgB69+5dY1aw6OjoWt0EFFominK3EZULjY5OQUEBjz/+OOPGjWPKlCn1asPd3Z27774bKHfRe/PNN3nqqacIDg6u9rw//vgDKaU18UmlcnZyciIoKJiAgEDKyjqQn59Lcs55RE4OPu4F+LhfwMU5B1VZPGj8QBtZbr5ReyJE7U1P3bp1Y926dQQFBdVr3E1F9+7dsVgsvPLKK9ayjh07NqFECs0BRbnXgbKyMoqKivDz82sU+6TFYiEvLw8XFxebhRS2WCwUFhZSWlra4LaklBw+fBgPDw9279592WLd36kpkbRKpcLNzQ1XV1dCQlpRXFxMbu4FzmSl4iQuEOClw9MtDa0hDaF2/5993ikARM32ea1W2ywjVEopKSkpuSRkguJdpFATinKvA6tWreK9995j+fLltG3b1u79FRUVMX36dEaMGFHjImpt8fX15ZtvvrHJYqvZbObEiROMHTuWPXv2YDQa0Wq1V6w/YsSIWrVb6ZHzP/t8GwoLC8nOziDtfBqumlwCvEvxck9EpT+LUHuBNhy04Uh1AAiNtR17IqXkzJkzODs7Ex4ebrf+jhw5QmlpKQsWLLAq9c6dO9OuXTu79KfQMlCUex3o1q0bkydPtkly5drg7OzM9ddfb9MdkLZMup2VlcX+/fspLCzk5MmT5OXlXdE0U59k0pX1K+3zPj4+GI3tKSjIJzM7jZQLWXi55OLnWYyH2zFU+pOg8gbnCNCEIjW+CFH1E9b/Zr6S+nrkWCwW/vrrL7y8vOy643PChAlcuHCBrl270rZtWzIyMvDz87Nbfwotg2ap3CtNC25ubtXOFG1Nr1696NWrV6P15+LiwkMPPdRo/QH8+eefrF69mvnz59e4vf348ePMnTuXwYMHs3XrVk6dOlWj3b0hVPrXBwYG4e8fQFlZGfn5eaReSEXmZOPrUYSPey4uxhyE2hWh8UNqI8ApBFTuWPfsWYrBkATGdECCJhCpbQdq7zopeZVKxfjx4+1uotu5c6fVFTIkJMTqCqnM3BWqo1kq9/z8fFasWMGAAQOUnI82Jj09ncOHD9fKJn+xmaW2JhdbUek/7+rqSmhouX3+Qk4OZ7LOo5bZBHqX4OORhkqfSqneCRePcDSukeW+87rdSEshSclZbP71KHfNGobGcBbcBiCdah/FUgiBr6+vfQdKuSvkwIEDSU9PJyYmBicnJ6tbY10wGAwUFxcjhHDIPAYKtqXGb1cI8bkQIksIcfSisueEEOeFEAcrXhMuOvaUEOK0EOKEEMK2+ecqcHNzIyYmptpdmgr1Y/Lkyaxdu7ZWf9uLTS31Mbs0lMo+K+3zEZGRdOl+DWHtRlBg7kNCahv+2FfK9TNe4oc1K5HFvyOL4sBSgMloZO0vB0g4kYbFbAGLDkr3g6x+0bcpSEhIYMmSJWzYsIGFCxfy7bff1nlB1WKxsGDBAtq1a0d0dDSbNm2yk7QKjkJtbt1LgHFVlL8lpexV8foZQAjRBZgBdK045wNxJaNnA3BxcSEmJsYukRMrF8lSU1Pr/AMyGAxkZmY26zgdGo0GNze3Zjmrq1xP8PHxIapDNNHdBhLWfgxdeoxAr+pMxgUtyPKYO3/sOkVk6wACAy7a5WkuAGO6w3mijBo1ih07drBr1y5+//13/Pz8OHr0aM0nXoTZbGb58uW88cYbjBgxQgkedhVQ4y9YSvk7kFvL9iYBy6WUeinlWeA0ENsA+Rodo9HIgw8+yAsvvFDnc1NSUli9ejUZGRl2kEyhLgghcHZ2pkOHDixatIgpN9+Fq08XQGAyWdi19zS//pnA9p0nOByfUnGWGSyOlxbQyckJLy8v6+vv8YbqQmRk5CVhLfLy8nj00Uf57LPPbJqdSqHpaYjN/QEhxD+AvcC/pZR5QBiw66I6qRVlzQaNRsO8efPqlR4uJCSEoUOH2uWJorS0lHXr1tGrV68Wt4FFSonFYrkk0JUtEUKU2+c1AchiFRqN5LF/TaCgsJTFX26je+dKTxcBovEW6GvLhg0buOOOO6yf27RpU69rQK1W8+WXX5KQkED//v2B8rAEL7/8MnFxcUpsmRZGfZ+9PwTaA72AdOCNujYghJgjhNgrhNibnZ1dTzFsj0qlYsyYMVxzzTV1VjTu7u5069bNLgHLcnNzeeWVV/jll19s3nZtSEpKYv/+/XaZ3SUnJ7NixQpyc2v7gFh3hBCg8UWo/SrMN2p8fdy4/+7RaLUVcxyVBzgFO1ygsgkTJnD8+HHr67fffquzctdoNHz44YeUlZXRuXNn5s+fD/zPA8ne0VMVGp96faNSyszK90KIT4D1FR/PA60vqhpeUVZVG4uBxQAxMTGOZeR0QIKDg1m2bJldXQ2rY9GiRezevZuff/65QWaBqqi089t95ihcwKUrlOwCqUelUuHlWXkjdgKXzuUK3sGwRSA8IQRjx45l7NixNpJKwdGpl3IXQoRKKdMrPt4IVK7urAWWCSHeBFoBHYC/GiylAhqNhs6dOzdZ/3fffTeTJk2yy1NJWFiYTTyfkpOTWbt2LTNmzCAwMPCy40IIpLY1qLRQdhJMmYAEtT84dwCt/XaZ2pJjx45hNpvp0aNHU4ui4MDUqNyFEN8Cw4EAIUQq8CwwXAjRi/LtfUnAvQBSymNCiBVAPGAC7pdSOvQqjS2iJF4NdOrUiU6dOtmlbVv93ePj4/n0008ZOHBglcq9vC8VUhMCHkEgKzJ1CTWgrlEOi8XCuXPncHd3JzAw8H/XjgSLyYRKowZRv4iVNfF3Dx4hBFJK5ZpVuCI1KncpZVWhDj+rpv5LwEsNEaox0ev1PPXUU3Tv3p277rqrqcVRaAAjRoxg7dq1l6WS+zvlClFdodRrj8lkYtu2bYSFhTF27FjMZXoyNv/JmS++p/j0OTQerkTMuI42U8fh2irIpoo3ISGB6dOnWz8HBATwxRdfNKvQxQqNi8M7M0sp2blzJ0VFRXZrv6ioiJISx3OBqwmLxcLnn3/O8uXLHc43uylwcXEhIiLCbiEpNBoN48ePJzY2FnOpnoTXPuXPmf9m8w/r+OroTnbv2sX+f7/KrrueovR8pk2/k8qMSk888QS5ubkcOnSIlStX2qx9hZaHwyt3KPfU0Ol0dmnbxcWFjz76iPvuu88u7dsTKSVbt27lt99+a2pRLqPSvfFKycCvdI7Z7LjJslUqFSEhIfj4+HBh7xHi//spOp2O45QwFG/iKaHMbCRjy05OfrAMabKdRVKlUiGl5MiRIxiNRgYMGKB4uChUS7O4OiZPnoyzs7Nd2rZllMTGRqVSsWjRoibZTZqamkpaWhp9+vSp8u9nsVjYuHEjHh4eDBkypFYmipKSEtavX0+3bt3o2rWrPcS2CVJKzn79I+aSMnSYcUeFOyo0CAxYcLFYSP52PZ3/fRfO/j426bNVq1Z06dKFNWvWcO+993LvvffaPPm5QsvC4WfulRtQmuN2eCh/nN60aRNJSUk2b7syAJSnp6ddF9by8/P55ZdfyMrKspYtWbKE+++/n/z8/CvK5urqWqebcmWyDicnpxrrms1mzp07R0FBQa3btxXSYqE0IxukRItAjwUzEgugrkgBqM/Ow1ymt1mfubm56HQ6ysrK+P3330lJSal2o11JSQk33HADw4YNIy4ujrS0NG688UaGDRvGhx9+qOxGvQponhqzGWE0GsnMzLTbmkFjoNfryczMvGRd4tZbb+W111674uxRCMHw4cOJjY2t9Y3HxcWFiRMn0qFDhxrr5ubmcvPNN/Pxxx/XbhA2RKhUOAeUR4P0QI0HajaQjx8aXCt+Uk6+XqhdbGf779ChA/feey/p6ens2LGDwsLCaus7OTlx0003cfbsWbKzs/n8889JSkpi+PDhPPXUU836elSoHc3THtEAGtv10dPTkxkzZjT61u6MjAwOHjzIkCFDGpyiLygoiFtuueUS80vbtm2rzUZVn79tpXsfUKObn6enJ/feey89e/ZsdJdAIQSRM6/n3PKfMJfqGYo3JiROCFQIUAnaTBuPk6ftNkQdPXqU//u//+P222/n8OHDtG/f/pLjJSUlfPPNN5hM5e6dTk5OzJo1i3feecdaJzIykptvvvmSsqKiIpYuXcrJkyeZOXPmZf1Wfh+Ky2Xz46qbuRcVFXHfffc1mqeBEAInJ6dGNyvFxcXx6KOPkpyc3OC2KreoN8YYLBYLmzdvZt++fdXWc3Fx4Y477qCgoIDdu3c3+iJs4IDedLj/VtSuzmgQuKBCjUCoVQQO7EPH+2YinGwzd8rMzCQtLY3u3buzc+dOXFxc+HvIDq1Wy6BBgxgyZAhDhgxh4MCBl5m3cnJy+OuvS/cUOjs7M3jwYLp06VKlAk9ISGDt2rUOu8itcGWuypm7rRJEOzLjx48nKiqqymw9JpMJvV7vkGsZUkoMBgNlZWVkZ2fj5eVVrd3eYDA0yYK4xt2VHs/+C9+enTnzxSqKz6SgcXclYvp1tL3tBtwjbbfbddu2bdxyyy1Mnz6doKAgXn75ZaKjoy+VR6O5ZBFar9fz3HPPYTab2bJlC//6179YvXo1zz//PPfee681nIFWq6Vbt26kp6dXKe/Ro0f5+eefGT16NM7OzqjVNW/2UnAQpJRN/urbt69sLCwWizQajdJsNjdan47GkSNH5OLFi+WFCxeaWpTLsFgs0mw2y+PHj8t+/frJlStX1ljXbDZLi8ViMxmKiork4cOHpU6nq1FWi8UizQajNOpKpEmvlxYbyyJl+fc1d+5c6erqKp2dneV9990njx8/XqNsBoNB6vV6aTAYLvlsMpkuq79p0ya5b9++y9p466235G233SY//fRTuX//fpuP7Wrn5MmT8o033qj3+cBeeQW96ljTtkag0vXR0WaslciKTVUnTpzg8OHDJCcn2zz5h7+/PwaDoV4JSf5OcXExf/75Z40LfLVFCIFKpcLf35/JkydfNkOtqq6tQwVnZmayfft2Lly4UKOsQghUTho0bq6otVqEHcIWd+vWjbfffpuTJ0/y+eefk52dXeO+j0pzoFarxcnJ6ZLPVa3/SCk5ceLEJdeDxWKhoKCA3r17s2vXLqKiomw6LgX7ctWZZRwVKSUZGRmsWbOGHTt2oNVqcXFxoaCgAF9fXyZNmsSQIUNwcXFpcF9+fn6sX7+ew4cP89FHHzWorSNHjnDPPffw9ttv1zniYE5ODgaDgdDQy/OWBgQEWMPS2pLS0lKMRmO17qOtW7dm6tSpjZIftbZotVrCw8OZOXNmlQufDcVsNvPWW28xYsQIAgICOHv2rNUEM2nSJGbOnImHh4dikmlGOLxyl1JiNBoderbdUKSUJCQksHDhQqKjo3nggQcoLS2lpKSEgIAAcnJyWLlyJbt37+bhhx9usPeLk5MTzz777BVD90op0ev1WCwWXF1dq/1Bd+3albfffpt+/frVSQYpJQsWLLBmr7JXyIC/s3PnTs6dO8ctt9xyRVu+VqslKCioUeRxFEwmE0ajkT179hAcHMzXX3+NyWQiJCSEoKAg3N3dFcXezHB45Q7w448/MnTo0CaLZW5P8vPzOXXqFK+//jrXX389FouFd999l+DgYHx8fEhKSsLV1ZVZs2axcuVKFi9ezAMPPFCrjT5XQqVScc0111Rb57nnniM9PZ1PP/202r68vLyqnLFf/Hh/JaUwZcoU8vLyGtVNNCoqCn9//2a7K9lemEwmhg0bxo4dO3B2dmbOnDksWbKEoqIiq1lHoXnRLK7woKCgBpsjdDodiYmJREVF2SUmeX2Jj49n4cKFjBo1CqPRyM6dO3niiScoLS2luLiYyZMnk5SUxGeffcbcuXN57733SEhIsHss7+joaAIDA+v9ozabzTz77LMEBQXx4IMPXtaOEILRo0fbQtQ60aZNG9q0adPo/To6ZrOZQYMG4ezsTHx8PO3bt+fmm2/m8OHDDZpIKDQdDq/chRAMGzaswe38+eefPPzww3z66acMGDDABpLZhsjISCwWC3379uXDDz/k+eefZ+nSpZw7dw5vb2/S09O58847GT16ND/99BPDhw9n06ZNlyl3KSVlZWWo1eoGmziEEJfk7KyKbdu2sWfPHh544IEqswRJKSkuLm6wCUmhcZBSEhwczJAhQ7juuutQq9X069ePXr16tVhzaEvnqvnWevXqxYIFC+yWcKK+5ObmEhAQwLlz54iKiuLs2bNkZmYycuRIzGYzt956K1999RW9e/fm9OnTdOrUiTNnzlzm5WIymfj+++/Zvn17o8h94MABNmzYcMX9AhqNhjfffJMnnnhCeaRvAvR6PQsXLuQ///kPr7/+OiUlJbzxxhssWLCAHTt2XHb9aDQaNBoNQgirmUytVttkAV+haXD4mbutCAoKuiTZgaOQl5eHl5cXeXl5BAUFkZSURM+ePdmyZQvnz5+nX79+ODk5UVJSghACd3d3iouLsVgsl9iqVSoVHTt2xMfHp1Hknjt3LnfccccV+7tYSSg0PhaLhVOnTtGrVy8WLlyIxWJh0aJFdO7cmSVLlnDkyJFL4gKp1Wplht7CuGqUu6MSEBBAfn4+wcHB7N+/nwkTJvDFF18wceJEjh07hpubGxqNxrqbtLCwEG9v78t+iJWP0Y2Fq6urde3CYDBw9uxZAgIC8Pf3t0n7JpOJ/Px8PDw8cLnYq8UGTwHnzp0jISGBIUOGVJt4uqysDL1ej6enp8MrPqPRSEJCgnVGrlKp+Pjjj1m9ejVQHnajT58+vPDCCwwePNhaz2AwcPz4cc6dO0dRUZFDrUddDZw7d85ubSvKvYkJDw+32jvPnTuHj48PPXr0wGg0IqUkJSWFO++8k927dxMdHc3Ro0fp2rWrQ5k6zp49y9ChQ/nXv/7F//3f/9mkzezsbFat+pbRo3vRuVMoIED4gPQHNA1S8kVFRWRmZta4OWzPnj2cOHGCmTNnVnsTcARMJhN79uyxhvLVaDQkJyczb948Zs2ahUajwWg0XrbZzGAw8Ndff3H48GG8vb1tkhTnu+++Y9q0aTa5RtetW8fw4cOrDW9cG0wmE2vWrOHmm29usExguzFmZWXZ7YaqKPcmxs3NjUGDBvHbb79x44038tJLL/Hkk0+yatUqFi9ezNq1azl+/Djbt2/n/vvv54033uDFF19sarEvISAggAceeMAmC98ASIm3l5rpN0fj5lbGTz+vwaA3MmRIL/wDwhGiM0jXeiv4zp0707FjxxrdIdu1a4eHh0ej+eA3BFdXV2bPnm39XFxcTOfOnXFxcaFTp04EBwfz7rvvMm7cOAYOHGgdk4eHB3fffTdeXl6EhIQwdOjQBsty9OhR7rnnHps87Zw/f57bbrutwfsO9Ho9p0+fZs6cOQ2WCWw3xlOnTrFu3TqbyPR3alTuQojWwFdAMCCBxVLKd4QQfsB3QCSQBEyTUuaJ8lvZO8AEoAS4Q0q53y7StwDUajUzZ87kySefJCQkhFmzZnHs2DG2bNmCl5cX3377LW3btuXmm29myZIlXH/99VUGA2tK/P39eeaZZy4rLykp4cyZM7Rv376OM18zbq7ncXN1Rqcro1WoHzkXCvnxx9+5445xqNVnQHQB6mfTrwxZUBNhYWGEhYXVq4+mxtXVlU2bNmEymXBycqJDhw7s2LEDg8FAq1atLvs+goKCbLZe06FDB5s9WbZt29YmrphCCJuGT7DVGF1dXWndurUNJLocUVNsESFEKBAqpdwvhPAE9gGTgTuAXCnlQiHEk4CvlPIJIcQE4F+UK/f+wDtSyv7V9RETEyP37t3b4MHURHZ2Nh999BE33ngj3bp1s3t/tUVKSWpqKq+++irdu3cnMzOTc+fOodPpKCgooEuXLhw5coRp06Zx2223NYuZJMD27dv55z//yXvvvcfIkSNrd5KUQC7Io4CpokiyY8cxTp85z22zxqBSOYPoAXjbxAZ/tZOfn09BQQFarZaQkJB6Ka2cnByrScfX15e8vDygPKRwSEhIndqSUpKeno6UEjc3N6spyd/f/4q7qq9EWVkZmZmZl5V7enri5+dXp7YuHuPFhIaG1vo3mZGRgdFoJCwszPpeCIGPj4/1b+bl5VXr0BdCiH1SypgqD14potiVXsCPwBjgBOVKHyAUOFHx/mPglovqW+td6VVdVMjKyH+2iEaXkJAgY2Nj5ffff9/gtmyNxWKRRUVFcseOHXLw4MEyNjZWdu7cWfbv31+OGDFCvvXWW9JkMjlMVL6ysjK5atUqeeTIkSvWycnJkcuXL5dZWVlXrBMfHy9XrFghS0tLywssFinNKVKat0hp3iItps3y1Ikv5Ssvz5Y5Wd9Li2lzxbGM8roKDSIvL0+OGjVKuru7y9atW8u9e/fWq5277rpLjh49WoaGhsoJEyZIDw8POWHCBDly5Mg6t2UwGGSXLl1kp06d5MMPPyz79esnIyMj5TfffFOndiwWi9yyZYsMCAiQY8aMka6urrJ169ayf//+8uGHH65TW8nJybJnz55y6NChMiAgQDo5Oclx48ZJLy8veejQoVq3M2LECBkSEiILCgpkly5d5A033CB9fHzkG2+8IbVarXR3d5dPPPFErX/n2CoqpBAiEugN7AaCpZTplTckys02AGFAykWnpVaU1Zt169bVGKGvNnTo0IGff/6Z6667rsFt2ZrKnKO7du3i2muvJSIigtTUVGbPno2TkxORkZE2j37YEIqKivjvf//Ljz/+eMU6/v7+TJ8+ncDAwCvWWb9+PQsXLvxbLtT/XZZFRaUsfPVbfH09yczMu8g/27G9V5oLhYWF7Nq1i23bthEaGsr+/fWzoL722musWLGC6dOn8/PPP9OjRw9Gjx5tzQxVF5YsWcLJkyc5ffo0n3/+OTNmzCAwMBCLxVLntp5//nmgPNTF1KlTGT16NN26datzDtnS0lLy8vLYv38/eXl5uLq6cscdd6BWq+sUWfXpp5+2/k0CAgK49dZbMZlMmM1munXrxvbt2/nyyy9t4kVT61+IEMIDWA3Mk1JesuRecQepU+xYIcQcIcReIcTev2eV+TsuLi428ZlWq9X4+/s7rFkjLS2Nn376iSNHjuDi4kJsbCxbtmxBq9Wyb9++Rs2GYzQaSUpKuqL3hK+vL1999VWDF6juuusuli5d+j8XSiFAeAHlm2ecnNTMmTORvn074u7uAgjAHYTHJSaZxMRENm3aRFlZWYPkuVoJDQ2tUzLzv+Pp6clnn33GsmXLmD9/PiNGjGDkyJF1XrPIz8/n3Xff5YYbbsBsNqPX62tv0rtCe61bt+aPP/7g66+/ZuzYsdxwww11HqvRaMRkMlknWQEBAcTEVG0NqY6L9xa0b9+ea665hhEjRgDlAetCQ0MpLi6u103x79RKuQshnChX7EullN9XFGdW2OMr7fJZFeXngYtXCMIryi5BSrlYShkjpYypbmYHMHbs2EbbnNOUbNu2jd27d6NSqejfvz9eXl6MGjWKs2fPsmbNGjIyMhpNlsTERCZNmsQPP/xQ5XG1Wk2nTp2wWCy89957pKam1qsff39/oqOj/+a54k65pU/g6upMbL9oYvp2JCIiGJVKDbSiUvlXUlhYSFZWlk1+FFcTKpUKJycnnnzySRITE+ut4D/66CPmz5/Ps88+y8SJE3nvvfdYs2YN589f9tOvFmdnZx5++GHCw8MJDAxk0KBBvPrqq3WeaVcyffp0oqKirN42L7/8MitXrkSv19epHZ1Oh06no1+/fjg7O5OYmMiSJUvqPOEqLS3FYrGQn5/P77//ztKlS9m2bRtQ/pt77LHHGD58eLVPu7WlNguqAviS8sXTeReVvwZckP9bUPWTUj4uhLgOeID/Lai+K6WMra6PxlpQdXSOHTvGypUrreYXs9mMk5MTRqORgIAA7rrrrhq9TgwGA+fOnSMkJKTOi08Xk5+fz/Llyxk1ahQdOnS4Yr3du3dzzz338Oabb9ouEJiUgAHkIf73kFg+oxeEgggFob6ousRisWCxWKxb6BuDSn/5iIiIZhtcy2KxsGbNGrZu3UpoaCiPP/54vZ5slyxZwp49e/D09ERKiU6nw9nZmfDwcB5++OE6txcfH8+KFSvo3r0727dvx2KxMGvWLGJjq1Ull3Hy5Enee+89nJyc0Ov1qNVq1Go1AwcOrJPPu8lk4vPPP+fQoUPWMhcXF/R6PU8++STh4eG1amfRokUkJCTg6elJcXExWq0Wi8XCddddx5o1awAYM2YMkydPrlV71S2o1ka5Dwa2A0eASqPXfMrt7iuANkAy5a6QuRU3g/eBcZS7Qt4ppaxWcyvK/X+UlZVx//33065dO8LDw7n11lvrFJ42KyuLVatWMWzYsEtyatoLg8FAeno6wcHBtotDIiWQj7QcIjPLwOnTJqI7d8HfPxSB9jIPmY8++ojExERefPHFRjW5HTp0iD///JNp06YREBDQaP0qKFRSnXKvUWtIKf+g3NBZFaOqqC+B++skoYIVi8WCl5cXN954IytWrMBsNtdJufv6+nLDDTfYLAxATWi1WiIiImzersWSia64jNTzFkxmN9zcghCiapOBTqejqKioUdckoHyB3t/f/6owGSo0P666Hap6vZ7169cTHR3dKDPb+pCSksKWLVusOTrrgpOTU60fEYuLi1m/fj39+/enbdu29RHV5lgsFk6fPklZ6TF27DyLp0cQvfvEVLtFe968eUgpGz1QmZubm8OHJVC4ernq/MmKiop49dVXq3Xha2o6duzI3LlzKSsro6SkxG79ZGRk8J///IfVq1fXe8HK1pjNZnbt2snp05kEBvri6uZBq1atqr3JqdXqRrW1Kyg0B666mbuvry9ff/11o5ktakPlAhSUB3zKysritddeIzIy0q7JLtq0acNTTz3FhQsXKCkpqVVwJiklJSUlWCyWOiVMNhqN6HQ6PD09q51hazSCiROisMgiEo6Ds3NAvReG9Xo96enpDXbzU1Bojlx1yr3Shc/R2LRpE1JKpkyZwieffNIofWq1Wq6//nry8vLqZF6Ii4ujsLCQSZMm1VrBnz17lri4OCZPnkxoaGjVlaREUIyvn5msTCeKiw1ERobW29zy119/cd999/H+++/bLqiZgkIz4aozyzgq3bp1s8a7qbS118fmXld8fHxo27ZtnRRoly5d2L9/P9OnTyc/P79W5wQFBRETE3PJJo7LkSAzQZpJSTXi7OxKUFBQvf8GHTp04L777qNjx471Ol9BoTnj8DN3KSUXLlzAx8enxWasF0I0GwVUGV1v9OjRuLm51dq/28fHpxbJRPTABYqKJHn5FsLDax+QqSpCQkKYO3duvc9XUGjONIuZ+4YNG8jKyqq5Yj2QUpKbm0txcbFd2ncEDAYDb7/9Nhs2bLBZmxMmTODFF19s0EapS6iIBillKVnZZqR0Ijg4uMbTFBQUqqZZTIX79etX5/CctaWkpIRbb72V2NhYa5ChlobBYGDjxo0UFBQwfvz4phbnCphBZmIyQep5I97egXh7e9vNLCWlxGQy2c3LJi8vj/Pnz9OxY0eHjWV0MWaz2WE8pq42KnfN2hqHV+5CCKKjo+3WvpOTE9ddd121W+ybO+7u7ixbtsxxlYyUQBFQRM4FCyUlEBUVZle/9f379/P888/z4osv0qNHD5u3n5SUxK5du2jVqpXdJia2ZNu2bcTFxdkkpolC7cnPzycsLIx7773X5m07vHK3N1qtlgceeKCpxbArQohaB/9vMmQ2ZrOJ9HQTbm7udndVdXZ2tmuE0M6dOxMeHl7DArLjIKXk5ptvpk+fPk0tylVFk6bZU7i6sVgs/PDDD2i1Wq677jo7mUn0QA4lpZLsHDOtW9swTs0V6Nq1K59//jkAFpMZk64Ec5ketbMWjbsbQqNu0FhdXFzsPgYFhepQlHsL4+L4KrZQxFJKvvvuO1xdXe2T5ERKdLoM0tLSMRhcsVjUNe5ItQVCCKSUlGVe4PQnK0haupayzBycfLyImDGBDvfOwD0iTNn1qtBsUZR7C0JKyRtvvEFxcTHPPPOMTWzWKpWKd9991yaZ7KvGzLlzp9gSd4bw8EjCwiJt54FTDVJK9Nm5/DV3AWnrfiXVXMI+ihH5glH/TeXCrkNcs2Qh7m3sf6NRULAHzcIVsjGRUpKXl2dNymsPTCYTOp2uXmnDakKn09nUrVMIQXBwMIGBgXZScsVERgoGDWyPVutGaGj9d6TWldQf40hb/ysWs4lQtAzHGwuSfIuB7O17Ofv1jxWLvQoKzQ9Fuf8No9HIPffcwzPPPGO3ELInTpxg2bJl1mzntuT//u//ePXVV+0407YhsnxHqouzQEo3XF3dGrQjta4kLVuPNJkRCARwijKKMeOGGmm2kPztT1iMSmYnheaJYpb5G2q1mvHjx9vVfS0gIIDo6Ohqw9jWByFEo4e9rTdSAmVALsU6yYVcM+HhIY3mrinNZoxF5U84EokZSW/cKcNCBgZ80GAq0mExmVE7QMyxvLw8fvvtNzp27MjRo0cZPHgwrVq1qnM7v/76K3l5eVx33XWO6xqrYBOarXI/c+YMiYmJDB061KYR/9RqNbNnz7ZZe1URHBzskLsvzWYzqamp+Pr64uXlZf8OZR5SlpCRYQacCAkJsX+fFQi1GvfIMPL2HQPgLHriKUGDoCflkTjdI1qh1jrGTyQxMZG4uDjmz59PQUEBH3/8cZ2Ve0FBAY8//jgnTpwgOTlZUe4tnGbw7F41BQUFSkJkG5OZmclNN93EF1980Qi9WYAMjEZJWroJb28fvLy8GnXxsv0dU1C7uSAQROHCDfgxAV/cUaFy1tLuzpsQDhTPaM+ePaSkpNCuXbs6nyulZP369Rw9evSScovFQl5eXosOv3G10myVe8+ePZk2bVqjZsKxWCz8+eefl/1AmhIppfXVUHx8fLj33nsZPnx4wwWrDimBQiTF5OVLioslYWH23ZFaFcEjryH6kTtRuzpX2N3L/6m1WtrPvok2U6+1ST8N/Y5OnTqF2WzGYDDw3nvv1Ws3tclk4sUXX2T06NEYjUZ27doFQGFhIW+++SZr1qyxywK/QtPhONOSOmKveAzVYTAYeP755+nQoQOLFi1q1L6vxKlTp3jppZeYN28evXv3blBbbm5u3HPPPTaSrAZkNhazkfPnTbi6uuHv79+os3YhBBo3V7o+dS9BQ/uR+OUaSs9n4uzvS+RtNxAy4hqcvGzjkmk0GtmyZQvh4eH1CnWwb98+WrduzejRo7npppsIDQ3FaDTWqQ2NRsMPP/xAfHw827ZtIzIyEii/ob/wwgts3ry5eSzCK9SaZqvcmwKtVsvrr7/eKH7YtcVkMlFYWFjnH3vTYgCy0eksXLhgJiw8pMkyJWncXAkdM4iQ0QORZgtCrbL5TUZKiV6vb9B3lJGRQWFhISkpKWRlZdU5nIQQgk6dOhEaGsqyZcvqZdpRaF7UqNyFEK2Br4BgQAKLpZTvCCGeA+4BsiuqzpdS/lxxzlPAbMAMPCil3GgH2RsdlUpllyBTDaFz586sXLmy1k8xZWVlrFu3jm7dutG5c2c7S3cpUkosFgsqkQ3oycq2YG6kHak1IYRAaOzzJKjVapk0aVK9x6hSqfjHP/6BxWLhm2++QUrJypUr69WWl5cX119/fb3OVWhe1GbmbgL+LaXcL4TwBPYJITZXHHtLSvn6xZWFEF2AGUBXoBWwRQjRUUrZ4uOJFhYWcuTIEXr27Nlos3shRJ2SmBQUFLBw4UJmzJjRKMrdYrGQm5tLZmYmBQUFHD16hLGjIwgLcyIt3YSvbwCenp5NrtwrsVgsHDp0CG9vb5vNbhuaUatnz5688MILl5TZM1KqQsugRq0gpUwH0iveFwkhEoCwak6ZBCyXUuqBs0KI00AssNMG8jo0u3fv5qGHHuKTTz5h0KBBTS1Olfj7+7N06VK7h3a1WCycPHmSlStXEh8fj5eXF0ajkeSk0xw6FMjkSaMwmlrTtm0rh7L1lpaW8sgjj9CrVy/eeuutphYHKM8/++WXX1o/FxUV0bFjR8W0olAtdbK5CyEigd7AbmAQ8IAQ4h/AXspn93mUK/5dF52WShU3AyHEHGAOQJs2beoju8MRExPD22+/Tffu3S8pr0wMUddZdn3ZtWsXR48eZdasWZdFJtRoNHaf9ZnNZuLi4vj0008ZM2YM48ePJycnByklgf46jp/Yz39fW86gQSMZOnSEXWWpKy4uLrz44ot2DzlcF0aMGMG2bdusn7du3dqE0ig0F2qtaYQQHsBqYJ6UslAI8SHwAuV2+BeAN4C7atuelHIxsBggJibGpvv8bR0Zsbb4+voyduzYy8pNJhNr1qyxmh/69OlDUFBQvfqozdji4uL46aefmDJlSqOHnZVScuDAAd577z0efvhh9uzZw+uvv067du1QqcycPXuA8LBAxo+fyObN29i+fTvjxo1rVBmrQ61WO9xT1/r16y/ZWGcymfjuu++aUCKF5kCtnoeFEE6UK/alUsrvAaSUmVJKs5TSAnxCuekF4DzQ+qLTwyvKGo2EhATWr1+PXq9vzG6viBCCkJAQCgoK+Pe//81vv/1WZT2TyUR8fDw5OTlXbOvIkSPccccdnDhxgsLCQo4ePXrZOB944AFWrFiBj4+PLYdRK3Q6HZ9++in//Oc/iYuLw2g0smDBAsaNG8foUdfw5OPT8fT05fffd3DvvffyzTffUFBQ0OhyNidGjRrF9u3bra+dO3cyZMiQRum70r9eoflRG28ZAXwGJEgp37yoPLTCHg9wI1C5s2ctsEwI8SblC6odgL9sKnUNGI1GDAaD3QJ/1RWNRsPQoUMpLS0lMjKSTp06VVkvJyeHWbNmceutt/Lvf/+7yjpGo9Hq+rhx40aef/55li9fTrdu3ax1vL29myQDkJSSkydPYjabcXNzIycnh4cffpi3334bjUaFSlwgLz+f0aMncehwJnq9nlatWrFz504Hzu3a9Pj4+DTJjRrKg9ydOXNG8bBphtTGLDMIuA04IoQ4WFE2H7hFCNGLcrNMEnAvgJTymBBiBRBPuafN/Y3tKdOjRw+6d+/uMB4Ylbi6unLNNddc8biPjw9PP/30JYr67/Tp08fq+ujn58czzzzjUGsW8fHxREVFERcXx7hx4/jll19o164dGjXodHqiosJZt24rkydPZteuXVxzzTUcO3aMcePGOdz31RCklEizBYvRCFKi0joh1A3L7lTJ6dOnMZvNV5wk2JK0tDQOHjyoKPdmSG28Zf4Aqroif67mnJeAlxogV4NoqOtZU+Hi4sJNN91UbZ2LF2XDwsKYPn16Y4hWa3Jzc/Hz8yMxMZHQ0FB27NjBkCFDWLTovyD1jBw5jpISHe3ateP333/H29ubI0eONLXYNkWazeTsOcKZz1aRGbcTabbg378HUXffTPCI/qicnBrUvk6nw2xunPlScXExGRkZSCmb5W/qakbZoapgE86ePcupU6fw9PQkPT2dNm3akJSURMeOHdmzZyfTpg4m50Ix589n06NHTwoLC/Hz8yM7O9vubpl1RUpJamoqZrOZiIiIOik1KSXpm/9k9z3PcPZ8CltlPjF40DElncytu+j92uO0u2NKnRVlSUkJzz//vDUmzMKFC+t0fn3R6XTk5OSg1+uVnLDNDMdxMFZo1hQVFZGZmUmXLl1ITExkyJAhbNq0iaFDh6JWW9A4QVmZQFdiZMaMGWzcuJHY2Fj27t1Lnz59HG5WOGfOHKZPn17nqKOG3HyOvfIxpakZ+Eo1nXGlGDNIieFCPkde+pCSlIw6y5OQkMCiRYu46aabmDlzZr1iudeHkpIS60uheaHM3BVsQteuXencuTNGoxFvb2/S09Pp2LEjH3/8MXPn/pOHH36QM2eSeO6551mzZg0uLi44OztTVFREnz59mlr8y7jlllvQ6XR12mAlpSTv8Aly95T7FmgRaFGh53/RFkvPpZMRt6Nes3co96hSq9WN5ixQWlqKl5cXpaWljdKfgu24qpV7UVERBoMBPz8/h5s5Njcqo3Q6OTlx77338uyzz3L77bdTWFjIr7/+xvHjpwFISkqiffv2tGrViq+//pq5c+c2atjm2iCE4B//+Ee9zjUW6TCXlV3xuDSaMOTVPT+vp6cnsbGxrF+/HihfWK+M7GhPysrK8PPzU5R7M+SqNsv8+eef/PTTT8024Ud8fDwvvPACWVlZTS3KJURFRfHYY4/x9ddf4+Liwh9//MHIkSMZMmQI+/btIywsjM8//5w77riD/v37t6gbq7OPFxqP8kxOWRjZRzGH0JFNeURIlYsWl6C6735Vq9VWl0gfHx+c6rgoK6Xk/PnzJCcnk5KSUqvY7VJKq3JXzDLNj6t65t6jRw90Ol3zyTv6N06dOsX69euZOnVqvXe82gOVSkXv3r154403+PPPPzl27Jg1/IKLiwvHjx/n9ddfJzQ0tEUpdiEEPj2jCbimFxmb/yQIJ2ZSvlhceYV5RkUQMmZgncedn5+PwWCgZ8+eAHUOTGcymRgzZgzR0dFs3bqVTz/9lKlTp1Z7jsViQUqJp6cnJSUlisdMM+Oqnrm3atWKDh062D1wlcViIT09nby8PJu2O378eH7++Wc6duxo03YrsVgsnD9/vl47SIUQ+Pj4sG3bNu6++26g3PPinnvuYfv27bi5ubVIReHk5UH35x7As0MEQqVGg0CDQAgVrq2C6PGfh+o1cwfYsWMHX375JV9++SWpqal1Olej0RAXF8d7771Ht27diIuLu6xOWloaUkoMBgO7d+8mKSnJ+j3u2bOn2p3TCo7HVT1zbyxMJhMbN26kVatWVcaeqS9ardauAa70ej0bNmygffv2jBhRtwBfUkp2797NsWPHyM7OpmPHjpSVlbFx40YyMjKIi4tjypT6LSo6MkIIAq7pxZAfPiDp6x/J2LIDaTbjf00v2t0xBb8+Xeo15tatW/Pcc89ZP7dv377Ocrm5uTF37lwyMjL44IMPgPJ9CS+88AIpKSmUlZUxYcIEUlNTWbRoEd27d0dKyTXXXMPJkycdJpyHQu1QlHsjoNFoGDlypMMtHNaEVqtl9OjReHl51flcs9nM999/z9GjR7nzzjtxcnKiqKiIoKAgtm/fzvLly7n22msdKqsVlN+Iv/32W8LDw+t8Q6tEqFR4d25Pz5cfoceL80CCUJc/Hdb3ZpaSksL8+fPRarUYjUbatWtH27Zta32+2Wzmn//8J9u2bWPlypXWm4Ovry9vvvkmGzZsYMGCBZSUlFhvxkVFRajVasLCwujatWu95FZoOhTlXktycnIoLi6mTZs2dTbjqFQqhwoRUFvUanW9PTJUKhUTJ060Lv5ZLBa0Wi0A99xzD927d2+y1HrVYTAY+Prrr+nZs2e9lTv8T4kLG67nTJo0ibfeeqveG5iioqIIDAxkw4YN5ObmXpIdSkpJaWkpeXl55Obm0rp1aw4cOICvry8qle1TDyrYH0W515JDhw6RnJzMrbfe6pBKydFQqVSMGTOG0aNHc/ToUdatW4darWb48OHExsY6rLJwcXFhyZIlDrcb09PTk99//53hw4eTl5fHzTffXKfz1Wr1ZdmcLqYq5b59+3Z8fHwcKpmKQu1RlHstiYmJoUuXLnV2QWsMiouLMRgM+Pr6OpzSLCkp4YcffmD27NkEBgai0+maWqRqUalUjbb7s7b88ccf/PDDD/zwww+cPXuWl19+2ebXYeWTVV5eHvn5+fTo0QO1Wk1JSYmi3JspyrdWS7y9vQkNDXXIC33hwoXMmjWrVhtNMjIySEhIaLTAUzqdDicnJ/z9/XFycsLHx8fhbkD2xmw2k5CQQGZmZr3OT01NxcnJiT59+nDTTTcxePBgMjLqHsKgOqSUtGrVigsXLqDX63Fzc8PHx4e8vDyHvOYVakb51uxApQthYWHddyLWh0GDBjFx4sRapfBLSEhgx44dGI3GRpCs3B/baDRy4cIFjEYjeXl5DhNnv7EwGo3s2LGDhISEerexceNGzp49y8mTJ6t0Y2woFouF8PBwcnJyMJlMODs7ExgYiEqlUpR7M0Uxy9iBsrIyfv75Zzp16sTQoUPt3l9dEl3ExsbSo0cP6+KmvXF1dWXy5MksWbIEIQSjRo2iX79+jdK3o6DVapk0aRKurq71On/kyJF899133HrrrUgp6d69OwMGDLCpjFJKgoODKSoqwmg0otVqCQkJIS0tTVHuzRRFudsBZ2dnxowZU6dsSFJKjEYjGo2m1j+mixNvq2uZCMLd3R13d/day9VQhBB069at2gQkTU3lNnshBM7OzjY3G6lUKgICAup9vo+PD//5z39Yt24dFouFu+++2+b7G6SU+Pv7U1JSgtFoxNnZmdDQUNzd3RXl3kxRvjU7UOlC6OvrW+tzcnJyWLZsGWfPnq31OVJKNmzYYJfHdFtSmTzl4pcjYbFYeOihh3jyySebWpQqWbduHQMGDCA+Pp5Dhw6xbt06m5u2LBYLgYGBpKSkYDAYcHFxISwsjJCQEEW5N1OUmXsjY7FYuHDhAm5ubpfMoCtnSnXZ1COEICgoqNFMLC2VyqeL+ppN7I3RaESn07F06VIA8vLyGDhwoE03FkkpiYiIYPTo0Tg7O6PVamnfvj2hoaE260OhcVGUeyNTWlrKmjVr6NKlC4MHD7aWe3l5ce2119apLSFEtTlZFWqHSqXiwQcfbGoxrsigQYN47bXXeOWVV6zmE1uH4K00SUVERFjLnJycmiTRuoJtUJR7I+Ps7MyQIUMaZIO9ElJK/vjjD/R6PSNHjlQep1sIrVu35tZbb2X48OH8+eeftGvXrl4hIarDEc1lCg2jRuUuhHABfgecK+qvklI+K4RoCywH/IF9wG1SSoMQwhn4CugLXACmSymT7CR/s0Oj0dC5c2e7tf/5559TUFDA8OHDFeXegggNDSU0NJTo6GicnZ1tvolJCTHQ8qjNzF0PjJRSFgshnIA/hBAbgEeAt6SUy4UQHwGzgQ8r/s+TUkYJIWYArwLT7SS/wt/4z3/+g9lsbrYx6hWqx16B1pSZe8ujxqmdLKe44qNTxUsCI4FVFeVfApMr3k+q+EzF8VFCuWoaBSEErVu3JjIyUvmhVoOUkpKSEiWE7UUoM/eWR62e24UQaiHEQSAL2AycAfKllJX56VKBsIr3YUAKQMXxAspNN39vc44QYq8QYm92dnaDBqGgUBcMBgN33303L730kl3az8nJISkpqVap7BoTKeUlr4tRZu4tj1opdymlWUrZCwgHYoHohnYspVwspYyRUsYEBgY2tDkFhVqjUqno2bMnnTp1skv7hw4dIi4uDoPBYJf264PFYuHjjz+mR48exMbGcujQoUuOKzP3lkedVtyklPnANmAA4COEqLTZhwPnK96fB1oDVBz3pnxhVaEOWCwWdDpdg5N3m0wmMjMza5Xg2Gw2k5mZecXIjVJKcnJyqky7Z7FYyM7OviyezpX6NxgMZGRkXKYAS0pKyMzMrHLcUkqys7OrTftnNBrJyMigrKzsinWcnJx44oknuPXWWy+RX6fT2SSgWkxMDBMmTHCo/Qdms5mFCxcydepUAgIC+Pjjjy85rszcWx618ZYJBIxSynwhhCswhvJF0m3AVMo9Zm4Hfqw4ZW3F550Vx7fKqy1SlA3Iy8tj9erV9O/f35oUuT4kJydz4403MnfuXObOnVtt3YyMDCZOnMjMmTN57LHHLjteWlrKtGnT6NatG+++++4lx0pKSpg6dSp9+/blzTfftJYnJSUxZcoUHnjgAebMmWMt3759Ow899BDvvffeJUkxlixZwkcffcSaNWsuSyWn0+m4+eab6dWrF2+//XaVYzh69Ci33XYbzz33XI0JoC8mPT2dn376iZEjRxIVFVXr86rC29vbYf3DBw8eTFZWltVkVFBQwAcffEBCQgLu7u5s3ry5wX2YTKZaBbGrDWaz2WZPFbaUy1ZtXbhwgbCwsJor1gNRk94VQvSgfIFUTflMf4WU8j9CiHaUK3Y/4AAwS0qpr3Cd/BroDeQCM6SUidX1ERMTI/fu3dvgwbQkzGYzOp0OrVbboMQRBoOBrKwsPDw88PHxqbZu5Szbzc2tytAJFouFjIwMnJyc+LsprfKYVqu9xIe/sn9PT89LFF5JSQm5ubn4+fldkn4wPz+f4uLiKnfeXqmPiykrKyMnJwdvb288PT2rHe/FGI1GSktLcXFxcagZt60wGo106NCBf/zjH+zevZt27drx4YcfYjKZyMjIYN26dQQFBdG/f/8G97VgwQKef/55myjkN954g9tvv73B+0IMBgMLFy5kwYIFDZYJbDfGs2fPsmfPHh555JF6nS+E2CeljKnqWI23HinlYcoV9d/LEym3v/+9vAyoW5oYhctQq9U22aii1WoJDw+vVV2NRlPtLKK6RBZXOnal/t3c3KrMKevj43PFm1BtEmm4uLjUerwX4+Tk5JCJWGyFWq1m/vz5LFq0CFdXV+tTnEajITw8HH9/fwIDA+v1t/s7Hh4ehIWF2WSfhZeXF61atSIoKKhB7ej1ejw8PGwyPrDdGG290/hilB2qCgpXASqVinvuuYd77rmnyuN9+/a1WQL36dOn28x+f/3119fpCexKaDSaOqcmrA5bjTEoKIjRo0fbQKLLqdEs0xgoZhkFhaZBSslff/3F3r17CQgIYNq0afVSWps3b+bkyZNAuV3/zz//BMp31t544411lmnZsmVIKYmMjLR69owePbrOHk7nz5/nhx9+uKy8R48eDBkypE4ybd68mVOnTlnLhBBIKZk+fXqtzUarV68mPz+f2267jZUrV1JQUIBarSY2NpYdO3YA0KdPn1rH66/OLHOZ72tTvPr27SsVFBQan7S0NBkeHi4HDhwog4OD5apVq+rVzosvvihffPFFOWzYMBkaGird3d3lvHnz5NChQ+vcVlJSkgwPD5etWrWSU6dOlddff73s1q2b/Oqrr+rUjsVikR9//LEMDQ2VL774ouzSpYuMjo6WN954o3zwwQfr1NaBAwekt7e3HDVqlPTz85OAfPDBB6WXl5c8ePBgrdu5/fbbpb+/vywoKJAdOnSQTzzxhPTw8JD//e9/pZeXlxw0aJCcNWuWLCkpqVV7wF55Bb2qBB9RULiKqUx9+N1339GxY0dyc3Pr1c7TTz/Nfffdh7u7O2VlZcyaNYtevXrVeXHabDbz+eefo9PpSEtLY+3atTz00EP1Dsf8/vvvU1xczMmTJ/Hw8OD++++nXbt2dbaVBwUF0b59e+Li4sjNzaV169bcfvvtdQ7z8cADD1ifjPr06cPkyZOta2tdunRh1apV/PDDD6SlpdWp3apQlLuCggIGg6FBO2rz8/OZOXMm586d44svviAzM5PIyMgaPbT+Tl5eHh9++KE1lLUQokGKTq1Wc/PNNxMaGspff/3F+fPn6dWrV53dGJOTkzl16hSPPvoo3t7eVq+shlBYWIiUkujo8j2hFosFg8FQ66xqNaEsqCooXMW4ubnRvn17Bg0ahMVioV27dvVqZ/78+WzevJnnnnuOM2fOsHHjRtzc3MjKyqpTO76+vhw4cIBt27YRHx/P2LFj+fTTT+v9RLFgwQIee+wxunfvDsDy5ctp27at9XNt8fPzw9/fn2XLllFcXIzZbOaLL76o8y7kAwcOoNfrWbNmDQcPHuTjjz/mr7/+Yty4cRw8eJBrrrmGWbNm0aZNmzq1WxXKgqqCwlVOUlISycnJeHh40KdPn3rNGhMSEqpU5N7e3vTq1avO7eXm5nL69Gn8/f1JTU0FIDo6muDg4Dq1k5+ff1moBYCwsLA6b1a70hj79u1b62idBw4cuGwXt1qtJiIigsTE8u1ArVu3rvVNtroFVUW5KygoKDRTqlPuis1dQUFBoQWi2NwVFBQcnoKCAvbt23dJWYcOHWjdunUTSeT4KMpdQUHB4TGbzeTn57NmzRoSEhKYPXu2NVbR3718fH19ycvLA8rDUdTVTt9SUJS7goKCw+Pn58eUKVM4deoU+fn5rFu3jvz8fN555x0KCwsxmUwMHjyYI0eOEBsby/bt2xkwYAAWi8UmkS6bI4rNXUFBodlhMpkwm80YjUbeeOMN2rZty+23346bmxs///wzHTt2pE2bNgwePLipRW0yFOWuoKDQrOnfvz/XXXcdN910EwCtWrXCYDDQqVOnevvHtwQUs4yCgkKz4dprr6Vnz56YzWZCQ0Np3bo1bdq04eabb8bZ2ZmXX36ZHj16sHPnTqSUdou42BxQ/NwVFBQUmimKn7uCgoLCVYai3BUUFBRaIIpyV1BQUGiBKMpdQUFBoQVSo3IXQrgIIf4SQhwSQhwTQjxfUb5ECHFWCHGw4tWrolwIId4VQpwWQhwWQvSx8xgUFBQUFP5GbVwh9cBIKWWxEMIJ+EMIsaHi2GNSylV/qz8e6FDx6g98WPG/goKCgkIjUePMvSJVX3HFR6eKV3X+k5OAymSHuwAfIURow0VVUFBQUKgttbK5CyHUQoiDQBawWUq5u+LQSxWml7eEEM4VZWFAykWnp1aU/b3NOUKIvUKIvdnZ2fUfgYKCgoLCZdRKuUspzVLKXkA4ECuE6AY8BUQD/QA/4Im6dCylXCyljJFSxgQGBtZNagUFBQWFaqmTt4yUMh/YBoyTUqZXmF70wBdAbEW188DFQZbDK8oUFBQUFBqJ2njLBAohfCreuwJjgOOVdnRRnnBxMnC04pS1wD8qvGauAQqklOl2kF1BQUFB4QrUxlsmFPhSCKGm/GawQkq5XgixVQgRCAjgIPDPivo/AxOA00AJcKfNpVZQUFBQqJYalbuU8jDQu4rykVeoL4H7Gy6agoKCgkJ9cYiokEKIIuBEU8vRSAQAOU0tRCOgjLNloYzTMYmQUlbpkeIo8dxPXClsZUtDCLH3ahirMs6WhTLO5ocSW0ZBQUGhBaIodwUFBYUWiKMo98VNLUAjcrWMVRlny0IZZzPDIRZUFRQUFBRsi6PM3BUUFBQUbEiTK3chxDghxImK+O9PNrU8DUEI8bkQIksIcfSiMj8hxGYhxKmK/30ryptt3HshRGshxDYhRHxFjP+HKspb1FiryWXQVgixu2I83wkhtBXlzhWfT1ccj2zSAdSRigCBB4QQ6ys+t7hxCiGShBBHKnJQ7K0oa1HXbSVNqtwrdr0uojwGfBfgFiFEl6aUqYEsAcb9rexJIE5K2QGIq/gMl8a9n0N53Pvmggn4t5SyC3ANcH/F99bSxlqZy6An0AsYVxFS41XgLSllFJAHzK6oPxvIqyh/q6Jec+IhIOGizy11nCOklL0ucnlsaddtOVLKJnsBA4CNF31+CniqKWWywZgigaMXfT4BhFa8D6Xcpx/gY+CWquo1txfwI+Uxh1rsWAE3YD/liWdyAE1FufUaBjYCAyreayrqiaaWvZbjC6dcsY0E1lMeVqQljjMJCPhbWYu8bpvaLFOr2O/NnGD5v8BpGUBwxfsWMfaKR/LewG5a4Fj/nssAOAPkSylNFVUuHot1nBXHCwD/RhW4/rwNPA5YKj770zLHKYFNQoh9Qog5FWUt7roFx9mhelUgpZRCiBbjniSE8ABWA/OklIXlAULLaSljlVKagV4VkVHXUJ7DoEUhhJgIZEkp9wkhhjexOPZmsJTyvBAiCNgshDh+8cGWct1C0y+oXg2x3zMvCo8cSvkMEJr52EV5Pt3VwFIp5fcVxS1yrHBJLoMBlKeOrJwYXTwW6zgrjnsDFxpX0noxCLhBCJEELKfcNPMOLW+cSCnPV/yfRfnNOpYWet02tXLfA3SoWJXXAjMojwffklgL3F7x/nbK7dOV5c0y7r0on6J/BiRIKd+86FCLGquoOpdBAuVKfmpFtb+Ps3L8U4GtssJY68hIKZ+SUoZLKSMp/w1ulVLeSgsbpxDCXQjhWfkeGEt5HooWdd1aaWqjP+Wx309Sbst8uqnlaeBYvgXSASPl9rnZlNsi44BTwBbAr6KuoNxT6AxwBIhpavnrMM7BlNsuD1Mey/9gxffYosYK9AAOVIzzKLCgorwd8BflOQtWAs4V5S4Vn09XHG/X1GOox5iHA+tb4jgrxnOo4nWsUt+0tOu28qXsUFVQUFBogTS1WUZBQUFBwQ4oyl1BQUGhBaIodwUFBYUWiKLcFRQUFFoginJXUFBQaIEoyl1BQUGhBaIodwUFBYUWiKLcFRQUFFog/w+6wNNaIci1fQAAAABJRU5ErkJggg==\n" }, "metadata": { "needs_background": "light" @@ -200,11 +196,10 @@ "the color represents the user's current QoE (red = bad, yellow = ok, green = good).\n", "\n", "A line between a user and a cell indicates that the user is connected to the cell.\n", - "Again, the color indicates the QoE that's achieved via the connection.\n", + "Again, the line color indicates the QoE that's achieved via the connection.\n", "Note that users can connect to multiple cells simultaneously using coordinated multipoint (CoMP).\n", "\n", - "Before training a reinforcement learning agent to properly control cell selection,\n", - "we will look at some configuration options of `mobile-env`.\n", + "Before training a reinforcement learning agent to properly control cell selection, we will look at some configuration options of `mobile-env` next.\n", "\n", "\n", "\n", @@ -212,8 +207,7 @@ "\n", "#### Predefined Scenarios\n", "\n", - "We can choose between one of the [three predefined scenarios](https://mobile-env.readthedocs.io/en/latest/components.html#scenarios):\n", - "Small, medium, and large\n", + "We can choose between one of the [three predefined scenarios](https://mobile-env.readthedocs.io/en/latest/components.html#scenarios): Small, medium, and large\n", "\n", "By default, these are available for either a single, centralized agent (e.g., `\"mobile-small-central-v0\"`)\n", "or multiple agents (e.g., `\"mobile-small-ma-v0\"`), which affects the observations, actions, and reward." @@ -254,20 +248,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 1.0, 'mobile-small-central-v0')" - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -276,13 +262,16 @@ } ], "source": [ - "# render the small environment (same as above)\n", - "env = gym.make(\"mobile-small-central-v0\")\n", - "env.reset()\n", - "# random first step\n", - "env.step(env.action_space.sample())\n", - "plt.imshow(env.render(mode='rgb_array'))\n", - "plt.title(\"mobile-small-central-v0\")" + "def plot_env(env_name):\n", + " \"\"\"Create env, take a random step, and then render the environment.\"\"\"\n", + " env = gym.make(env_name)\n", + " env.reset()\n", + " env.step(env.action_space.sample())\n", + " plt.imshow(env.render(mode='rgb_array'))\n", + " plt.title(env_name)\n", + "\n", + "# plot small env from earlier\n", + "plot_env(\"mobile-small-central-v0\")" ], "metadata": { "collapsed": false, @@ -293,20 +282,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 1.0, 'mobile-medium-central-v0')" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": { "needs_background": "light" @@ -315,13 +296,7 @@ } ], "source": [ - "# create and render the medium environment\n", - "env = gym.make(\"mobile-medium-central-v0\")\n", - "env.reset()\n", - "# random first step\n", - "env.step(env.action_space.sample())\n", - "plt.imshow(env.render(mode='rgb_array'))\n", - "plt.title(\"mobile-medium-central-v0\")" + "plot_env(\"mobile-medium-central-v0\")" ], "metadata": { "collapsed": false, @@ -332,20 +307,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 1.0, 'mobile-large-central-v0')" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -354,13 +321,7 @@ } ], "source": [ - "# create and render the large environment\n", - "env = gym.make(\"mobile-large-central-v0\")\n", - "env.reset()\n", - "# random first step\n", - "env.step(env.action_space.sample())\n", - "plt.imshow(env.render(mode='rgb_array'))\n", - "plt.title(\"mobile-large-central-v0\")" + "plot_env(\"mobile-large-central-v0\")" ], "metadata": { "collapsed": false, @@ -376,11 +337,9 @@ "\n", "It is also easy to define a custom scenario by subclassing `mobile-env`'s `MComCore` base class.\n", "\n", - "Here, we create a custom scenario with two cells and three users,\n", - "where one user is stationary and the other two users move quickly with 5 m/s.\n", + "Here, we create a custom scenario with two cells and three users, where one user is stationary and the other two users move quickly with 10 m/s.\n", "\n", - "We also configure the environment to simulate each episode with identical user positions and movement.\n", - "By default, users appear and move randomly in each episode.\n", + "We also configure the environment to simulate each episode with identical user positions and movement. By default, users appear and move randomly in each episode.\n", "We also set the episode length to 10 (instead of default 100 steps)." ], "metadata": { @@ -392,20 +351,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -427,12 +378,12 @@ " # 10 steps per episode\n", " \"EP_MAX_TIME\": 10,\n", " # identical episodes\n", - " \"seed\": 42,\n", + " \"seed\": 1234,\n", " 'reset_rng_episode': True,\n", " })\n", " # faster user movement\n", " config[\"ue\"].update({\n", - " \"velocity\": 5,\n", + " \"velocity\": 10,\n", " })\n", " return config\n", "\n", @@ -464,9 +415,15 @@ "# init and render the custom scenario\n", "env = CustomEnv()\n", "env.reset()\n", - "# random first step\n", - "env.step(env.action_space.sample())\n", - "plt.imshow(env.render(mode='rgb_array'))" + "for _ in range(10):\n", + " # here, use random dummy actions by sampling from the action space\n", + " dummy_action = env.action_space.sample()\n", + " obs, reward, done, info = env.step(dummy_action)\n", + "\n", + " # render the environment\n", + " plt.imshow(env.render(mode='rgb_array'))\n", + " display.display(plt.gcf())\n", + " display.clear_output(wait=True)" ], "metadata": { "collapsed": false, @@ -479,7 +436,11 @@ "cell_type": "markdown", "source": [ "#### Extending `mobile-env`: Adding a Handler for a Custom Observation Space\n", - "\n" + "\n", + "Handlers define the observation and action spaces as well as the reward for an environment.\n", + "Hence, when designing a new Markov Decision Process for a reinforcement learning approach, you can quickly validate it by implementing a new handler in `mobile-env`.\n", + "\n", + "Let's first look at the default handler (for single-agent RL) and then add a new handler with a custom observation space." ], "metadata": { "collapsed": false @@ -491,7 +452,7 @@ "outputs": [ { "data": { - "text/plain": "Box([-1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1. -1.], [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.], (15,), float32)" + "text/plain": "['connections', 'snrs', 'utility']" }, "execution_count": 17, "metadata": {}, @@ -499,7 +460,7 @@ } ], "source": [ - "env.observation_space" + "env.handler.features" ], "metadata": { "collapsed": false, @@ -508,21 +469,123 @@ } } }, + { + "cell_type": "markdown", + "source": [ + "By default, observed features are:\n", + "* The current connections between users and cells (binary)\n", + "* The signal-to-noise-ratio (SNR) between all users and cells (normalized to [0,1])\n", + "* The current utility (i.e., QoE) of each user (normalized to [-1,1])" + ], + "metadata": { + "collapsed": false + } + }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 40, "outputs": [ { - "data": { - "text/plain": "['connections', 'snrs', 'utility']" - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Raw observations: [ 0. 0. 0.38170958 1. -1. 1.\n", + " 0. 1. 0.2665389 0.47082826 0. 0.\n", + " 0.34874913 1. -1. ]\n", + "\n", + "Observations for user 1:\n", + "Current connections to the 2 cells: [0. 0.]\n", + "SNR to the 2 cells: [0.38170958 1. ]\n", + "Current utility: -1.0\n", + "\n", + "Observations for user 2:\n", + "Current connections to the 2 cells: [1. 0.]\n", + "SNR to the 2 cells: [1. 0.2665389]\n", + "Current utility: 0.47082826495170593\n", + "\n", + "Observations for user 3:\n", + "Current connections to the 2 cells: [0. 0.]\n", + "SNR to the 2 cells: [0.34874913 1. ]\n", + "Current utility: -1.0\n" + ] + } + ], + "source": [ + "env = CustomEnv(config={\"seed\": 42})\n", + "obs = env.reset()\n", + "obs, reward, done, info = env.step(env.action_space.sample())\n", + "\n", + "print(\"Raw observations: \", obs)\n", + "\n", + "# connections and SNR are per user and station, utility just per user\n", + "obs_per_user = 2 * env.NUM_STATIONS + 1\n", + "for ue in range(env.NUM_USERS):\n", + " print(f\"\\nObservations for user {ue + 1}:\")\n", + " offset = ue * obs_per_user\n", + " print(f\"Current connections to the {env.NUM_STATIONS} cells: {obs[offset:offset+env.NUM_STATIONS]}\")\n", + " print(f\"SNR to the {env.NUM_STATIONS} cells: {obs[offset+env.NUM_STATIONS:offset+2*env.NUM_STATIONS]}\")\n", + " print(f\"Current utility: {obs[offset+2*env.NUM_STATIONS]}\")\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } + } + }, + { + "cell_type": "markdown", + "source": [ + "Now, let's extend the observation space by adding an observation that indicates whether a user is connected to any cell at all, i.e., a single binary entry per user.\n", + "\n", + "For that, we create a new handler that inherits from the existing central handler and simply overwrite the relevant parts: The available features and the observations." ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 88, + "outputs": [], "source": [ - "env.handler.ftrs" + "from mobile_env.handlers.central import MComCentralHandler\n", + "import numpy as np\n", + "\n", + "\n", + "class CustomHandler(MComCentralHandler):\n", + " # let's call the new observation \"any_connection\"\n", + " features = MComCentralHandler.features + [\"any_connection\"]\n", + "\n", + " # overwrite the observation size per user\n", + " @classmethod\n", + " def ue_obs_size(cls, env) -> int:\n", + " \"\"\"Increase observations by 1 for each user for the new obs\"\"\"\n", + " # previously: connections for all cells, SNR for all cells, utility\n", + " prev_size = env.NUM_STATIONS + env.NUM_STATIONS + 1\n", + " return prev_size + 1\n", + "\n", + " # add the new observation\n", + " @classmethod\n", + " def observation(cls, env) -> np.ndarray:\n", + " \"\"\"Concatenated observations for all users\"\"\"\n", + " # get all available obs from the env\n", + " obs_dict = env.features()\n", + "\n", + " # add the new observation for each user (ue)\n", + " for ue_id in obs_dict.keys():\n", + " any_connection = np.any(obs_dict[ue_id][\"connections\"])\n", + " obs_dict[ue_id][\"any_connection\"] = int(any_connection)\n", + "\n", + " # select the relevant obs and flatten into single vector\n", + " flattened_obs = []\n", + " for ue_id, ue_obs in obs_dict.items():\n", + " flattened_obs.extend(ue_obs[\"connections\"])\n", + " flattened_obs.append(ue_obs[\"any_connection\"])\n", + " flattened_obs.extend(ue_obs[\"snrs\"])\n", + " flattened_obs.extend(ue_obs[\"utility\"])\n", + "\n", + " return flattened_obs" ], "metadata": { "collapsed": false, @@ -533,22 +596,51 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 90, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 0. 0. 0.387414 1. -1. 0.\n", - " 0. 0.5115964 1. -1. 0. 0.\n", - " 0.21166208 1. -1. ]\n" + "New, raw observations: [0.0, 0.0, 0, 0.42142308, 1.0, -1.0, 0.0, 0.0, 0, 0.08438771, 1.0, -1.0, 1.0, 0.0, 1, 1.0, 0.059552114, 0.65321183]\n", + "\n", + "Observations for user 1:\n", + "Current connections to the 2 cells: [0.0, 0.0]\n", + "NEW: Any connection?: 0\n", + "SNR to the 2 cells: [0.42142308, 1.0]\n", + "Current utility: -1.0\n", + "\n", + "Observations for user 2:\n", + "Current connections to the 2 cells: [0.0, 0.0]\n", + "NEW: Any connection?: 0\n", + "SNR to the 2 cells: [0.08438771, 1.0]\n", + "Current utility: -1.0\n", + "\n", + "Observations for user 3:\n", + "Current connections to the 2 cells: [1.0, 0.0]\n", + "NEW: Any connection?: 1\n", + "SNR to the 2 cells: [1.0, 0.059552114]\n", + "Current utility: 0.6532118320465088\n" ] } ], "source": [ + "# create the env with the new handler and check the obs\n", + "env = CustomEnv(config={\"handler\": CustomHandler})\n", "obs = env.reset()\n", "obs, reward, done, info = env.step(env.action_space.sample())\n", - "print(obs)" + "\n", + "print(\"New, raw observations: \", obs)\n", + "\n", + "# connections and SNR are per user and station, utility just per user\n", + "obs_per_user = 2 * env.NUM_STATIONS + 2\n", + "for ue in range(env.NUM_USERS):\n", + " print(f\"\\nObservations for user {ue + 1}:\")\n", + " offset = ue * obs_per_user\n", + " print(f\"Current connections to the {env.NUM_STATIONS} cells: {obs[offset:offset+env.NUM_STATIONS]}\")\n", + " print(f\"NEW: Any connection?: {obs[offset+env.NUM_STATIONS]}\")\n", + " print(f\"SNR to the {env.NUM_STATIONS} cells: {obs[offset+env.NUM_STATIONS+1:offset+2*env.NUM_STATIONS+1]}\")\n", + " print(f\"Current utility: {obs[offset+2*env.NUM_STATIONS+1]}\")" ], "metadata": { "collapsed": false, @@ -560,6 +652,10 @@ { "cell_type": "markdown", "source": [ + "Now, the observations include an additional entry per user that indicates whether the user is connected to any cell at all. Maybe that's useful for the RL approach?\n", + "\n", + "Let's try to train an RL agent with the custom observations on our custom environment.\n", + "\n", "## Step 3: Single-Agent RL with `stable-baselines3`" ], "metadata": { diff --git a/mobile_env/handlers/central.py b/mobile_env/handlers/central.py index c9ea8fd..1a6e675 100644 --- a/mobile_env/handlers/central.py +++ b/mobile_env/handlers/central.py @@ -14,13 +14,13 @@ def ue_obs_size(cls, env) -> int: return sum(env.feature_sizes[ftr] for ftr in cls.features) @classmethod - def action_space(cls, env) -> spaces.Dict: + def action_space(cls, env) -> spaces.MultiDiscrete: # define multi-discrete action space for central setting # each element of a multi-discrete action denotes one UE's decision return spaces.MultiDiscrete([env.NUM_STATIONS + 1 for _ in env.users]) @classmethod - def observation_space(cls, env) -> spaces.Dict: + def observation_space(cls, env) -> spaces.Box: # observation is a single vector of concatenated UE representations size = cls.ue_obs_size(env) return spaces.Box(low=-1.0, high=1.0, shape=(env.NUM_USERS * size,)) From 186ca6c9598f7995fbb5013691c4dec3dd0f4783 Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Sun, 12 Dec 2021 18:58:16 +0100 Subject: [PATCH 12/14] more work on demo --- .gitignore | 1 + examples/demo.ipynb | 1100 ++++++++++++++++++++++---- examples/tutorial.ipynb | 1631 +++++++++++++++++++++++++++++++++------ 3 files changed, 2328 insertions(+), 404 deletions(-) diff --git a/.gitignore b/.gitignore index 715b946..8e7c3e2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ .idea +results* # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/examples/demo.ipynb b/examples/demo.ipynb index 1569c29..c183791 100644 --- a/examples/demo.ipynb +++ b/examples/demo.ipynb @@ -3,7 +3,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true, "pycharm": { "name": "#%% md\n" } @@ -30,7 +29,7 @@ "This demonstration consists of the following steps:\n", "\n", "1. Installation and usage of `mobile-env` with a dummy actions\n", - "2. Configuration of `mobile-env` and adjustment of the observation space\n", + "2. Configuration of `mobile-env` and adjustment of the observation space (optional)\n", "3. Training a single-agent reinforcement learning approach with [`stable-baselines3`](https://github.com/DLR-RM/stable-baselines3)\n", "4. Training a multi-agent reinforcement learning approach with [Ray RLlib](https://docs.ray.io/en/latest/rllib.html)\n", "\n", @@ -43,6 +42,15 @@ { "cell_type": "code", "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -55,47 +63,49 @@ "Requirement already satisfied: pygame==2.1.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (2.1.0)\n", "Requirement already satisfied: shapely==1.8.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (1.8.0)\n", "Requirement already satisfied: svgpath2mpl==1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from mobile-env) (1.0.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.4.7)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (1.3.1)\n", - "Requirement already satisfied: setuptools-scm>=4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (6.3.2)\n", "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (4.28.1)\n", + "Requirement already satisfied: setuptools-scm>=4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (6.3.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.8.1)\n", "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (8.4.0)\n", - "Requirement already satisfied: packaging>=20.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (21.3)\n", "Requirement already satisfied: cycler>=0.10 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (0.10.0)\n", - "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.4.7)\n", - "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (2.8.1)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib==3.5.0->mobile-env) (21.3)\n", "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from gym>=0.19.0->mobile-env) (1.6.0)\n", "Requirement already satisfied: six in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from cycler>=0.10->matplotlib==3.5.0->mobile-env) (1.15.0)\n", - "Requirement already satisfied: setuptools in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (46.1.3)\n", - "Requirement already satisfied: tomli>=1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (1.2.2)\n" + "Requirement already satisfied: tomli>=1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (1.2.2)\n", + "Requirement already satisfied: setuptools in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib==3.5.0->mobile-env) (46.1.3)\n" ] } ], "source": [ "# installation via PyPI\n", "!pip install mobile-env" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "`mobile_env` comes with a set of predefined scenarios, registered as Gym environments, that can be used out of the box for quick experimentation." - ], "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "`mobile_env` comes with a set of predefined scenarios, registered as Gym environments, that can be used out of the box for quick experimentation." + ] }, { "cell_type": "code", "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -119,16 +129,15 @@ "env = gym.make(\"mobile-small-central-v0\")\n", "\n", "print(f\"\\nSmall environment with {env.NUM_USERS} users and {env.NUM_STATIONS} cells.\")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "Here, we consider a small scenario (called `\"mobile-small-central-v0\"`) with 5 users and 3 cells.\n", "As the users move around, the goal is to connect the users to suitable cells, ensuring that all users have a good Quality of Experience (QoE).\n", @@ -142,22 +151,27 @@ "![Utility plot](../docs/images/utility.png)\n", "\n", "To get started, we will use random dummy actions." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "code", "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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yMli7di1JSUlYLBasVqvdll752Wq12qNBhoeHk5CQYA8JnJGRga+vLxMmTLDb6wcPHszw4cPt42wK1Fm5CyE8hBBele+B8cBh4Bfg1opqtwI/11fIlkpxcTFbt27l9OnTjS3KORw+fJhXX32VuLi4i9bp2rUr06ZNu+iPUqfT8cknnzB37tyLtpGcnMz111/Pb7/9Vm+ZWyo6nc5ub//22285cuQIo0ePxmq1EhERwYABAxgzZgxt2rQhOjqa4cOHExwczF133YVWq+W9994jMTGRLl26MGTIEHx8fJg2bRpdunQhMjKS1157rV43VyEEMTExuLu7891333HjjTeyfft28vLyWLlyJQsWLKBNmzZ06dKF3Nxc+vTpYw9OtmvXLiwWC99++22V8eGdRdu2bbntttvw9/dn+fLlZGdns379eoxGI9999x2FhYWsWrWK/Px8duzYQY8ePfD19WXx4sVMnTqVK664AldXVxYuXMiIESMQQvDpp5/y9ddfM3PmzCazllNnP3chRHtss3Ww2e4XSylfEUIEAEuBSCARmytktVHwL1c/d6vVSm5uLl5eXnVKOl1XiouLycrKIjw8vMot+yUlJZw+fZr27ds7NVFGWloa//rXv7jlllvsszkVFZWaowYOUzmHb775htdff50ffviBjh07NpocatJsFZX6UZ1yV3dwXIYMGDCAe++9l+Dg4EaVQwhhf1WF2Wxm48aN9ljfDUVpaSknTpxoUj7LKiq1RVXulyGdOnXinnvuwcfHp7FFqZaCggKeeeYZFi9e3KD9pqamsm7dOlQvLpXmjBo4rAWiKAq5ubm4u7s3mcWduuDj48OHH37YoG5yAK1bt2bKlCmN9mSTmJjIp59+2qBJlT09PbFYLM3yacXV1RWdTtcoSagdwTXXXMOoUaMc3q5qc2+BZGdnM336dKZOncqjjz7a2OKo1BJFUeyueA1FpV96c6U5y6/Vauu8EVBN1nGZ4e7uzpQpU+jXr8rvXKWJo9Fo1IBmKvVGvYKcTGWAoYacVbi7u/PYY48xfPjwS1duphQVFfHMM8+wevXqxhZFRaVJos7cncyff/5Jeno6N954o5oGzoGYTCYOHTpEmzZtGlsUp2I2m/nXv/7F7NmzCQ0N5dlnn8XFxYXbb7+dLl262Ot98cUX+Pj4MH369CrbKSsrQ6/XOy0OUHJyMsuXLyc0NJQpU6bg5uZGYmIi33//Pe7u7owZM4bOnTuzZcsW9u3bR+/evenatSvLly/HarUyadIkOnTo4BTZLkZmZiaLFy8mIiKCadOmkZaWxo8//ohWq2Xq1Kn2pNvHjh1j3bp1lJSUEBkZyZAhQ+wB1yZPnkxkZCSlpaV89dVXDBs2jK5duzboOC6GanN3MgkJCRQWFtKtWzf1UduBSCkpLS1Fr9e36JtmWVkZHTp04Pvvv6dz58506NCBO+64g1mzZrFz507A9rf46quv8PHxYerUqec8JQ4bNoytW7eyZcsWevTowf/93/8RHx/PkSNHmDlzJgEBAfWWMT09nYceeogJEybwxx9/0K1bNx5//HHWrl3LqlWr+M9//oOrqyuHDh3irbfe4p133kGn0yGEoLCwkN9//53vv/+ejz/+uEEXz4uLi/n777958803+frrr+1hiNevX8/Ro0d59dVXAdsNtqysjI8//hiLxcI999xDWVkZK1eu5LfffuPjjz9mzZo1zJ07lwceeIB//vOfDTYG1c+9EWnbti3du3d3imKXUhIfH09SUlKzXUyqK0II3N3dW7RiP5t33nmH5557jrKyMt5++23+/PNPHnvsMdatW8emTZsICQkhIiICRVF48skn+f7773nkkUf4/fffefTRR+nevTvr1q1j5syZzJ8/nz/++AMPDw+HyBYfH8+mTZsYPXo0V1xxBV988QVSSpKSklixYgVTpkzhX//6F7Gxsfzwww+MGDGCZ555Bo1Gg5+fH2vWrMFsNl80TpGz8PDwICwsjA0bNlBUVISvry8Gg4EXXniBgwcP2uvp9XpOnTrFf//7XwICAvD19cXLy4vVq1ej0WhYuXIlc+fOZfjw4WzZsqVBx1AdqlmmGWO1Wnn88cfx9vbmiy++aGxxVJzIww8/TOfOnfnuu+8wm80EBARw1VVX8fvvvzN9+nTKysoICgqiTZs2hIaG8uGHHzJs2DAAAgMDefzxx+nXrx9Tp05Fq9Wydu1ah4W8qAwUtmnTJtasWYO/vz8AgwYN4ssvvyQjI4PZs2fzyiuvEBISwuzZs3n66ae5/fbbWbJkCWvXrmXx4sUOu9lciiVLlrBz5046d+7MuHHj7OXl5eUsWLCAgoIC7rrrLnu5lJKUlBR0Oh233HILJSUlPPHEE+zcuZNly5bx8ssvI4Tg4MGDGI1GTp061eAmpqpQlXszRqvV8sADD6hp+BqQoqIiTp8+TVRUlFPj7pxNZahZIQQGgwGDwUBeXh7l5eWMGDGC2NhYOnXqxK5du0hPTyc1NZWBAwdisVjOCWM7fvx4Bg0axMGDB4mJiXGYfL1792bSpEl89dVXxMbG8tZbbzF//nw8PDw4evQof/31F8OGDcPX1xdFUVi6dCl9+vQhPj6e9957j2HDhrFjxw6io6OJiIhwmFwX47rrrmP69OkkJCTw4YcfArBp0yYSEhJ47bXX6N+/PyUlJeTk5PD5559z//33M2/ePCZOnIhOp2PNmjV8/vnnjBs3jt9//53PPvuMoKAgXn31VY4ePUq7du2cPoaaoNrcVVoUJSUl7N+/ny5duuDn5+fw9k+cOMH69euZMmWKfcHNmUgpSU9Px9/fH51OR3p6OlJKfHx87GkCDQYDOp2OkpKSC86vTNgRHBzMmjVrePrpp7npppt4+umnHWoqzM3Ntffv6+tLfn4+3t7edhm9vb3R6XT2sLkeHh64ubmdE1Y6KCioQScqZWVl9v7d3d2xWCz2cL2urq74+PiQmZlJaGgo2dnZeHl54e7uTmlpKTk5OfZ2goODMRgMFBQU2J+qGgo1cJjKZcP+/fu5+eabefnlly/qOVIfTCYTeXl5dmXbXFAUhd27d1NWVkZUVFSD3JhUnI+q3FUuG4qKiti2bRt9+vQhMDCwscVRUXEq6g7VFoAaHrdmeHp6Mn78+MYWQ0Wl0VGVezPBaDTy5JNPMmXKFCZPntzY4qg4kYKCAg4ePNhk0rWpOJeoqCgiIyMd3q6q3B2I1WqlrKwMV1dXh+8EtFqtFBQUNMuofSq1w2KxkJ+fT3l5eWOLotIAOOs3rdrcHUhCQgLr1q3jqquucviCVWWyXjWoVO2oTHqs1WrVv5tKi0PdodpA+Pj4EB0dXaeddmazmeTkZEpLS6s8LoRAp9Odo6CklGRmZpKdnX3Z7VCtKampqVx//fVqgDGVyw5VuTsQPz8/hg8fjqenJ/n5+bV63MrOzmblypUkJCTU+BwpJZs2bWLr1q11kPbyQKfTERAQ0GC7H1VUmgqXNMsIIT4HrgYypZQxFWX+wPdAWyABmCGlNAqbG8c7wCSgBLhNSrnvUkK0FLNMJSUlJSxevJjOnTvbt4BfCpPJRHJyMmFhYTXOniSl5MyZM2i1WkJCQlQvmipQvYxUWjL1NcssAiacV/Y0sFFK2RHYWPEZYCLQseJ1F/BhXQRu7hgMBvr27Uv79u1rdU6HDh3OUexSSvurKoQQhIeHExoaWmPFZbVaOXXqFEajscayNWculYS7Plzq+1FRaUwuqdyllFuA3POKpwBfVrz/Eph6VvlX0sYOwFcIEeYgWZsNOp2O3r1713tRtaSkhH/+8598++23DpIMSktL2bBhA7GxsQ5r83Kl0iy2a9cuVcGrNDnq6goZIqVMq3ifDoRUvA8Hks+ql1JRlkYzRlEUSktLneLiWB1SSgoLC6uMGVJX3NzcuPrqq/H29nZYm5crUkpMJpPqj67SJKm3n7uUUgohaj1tEULchc104xQHfkeSlZXFzz//zOjRo4mKimqwfj08PPjss88c6sKn1WrVuCIOQqPRcOWVVwJN055vtVqxWCyA7XtXFMX+hFEZaVKl5VJX5Z4hhAiTUqZVmF0yK8pTgdZn1YuoKLsAKeVCYCHYFlTrKEeD4OHh4bQog9VR6f7YVLFYLKSlpWE0GjEYDISFheHt7d1klUZxcTEHDhyga9eu+Pr61rs9Z9nyHcWyZcuYN28e7u7uREVFsX//ftq0acOpU6fYvn07Pj4+jS2iihOpq+b4BbgVmFfx/89nlT8ghFgCDATyzzLfNFs8PT1r7PXS0jCbzRQXF+Pl5YVWq7WbIrZv386PP/5ITk4OPj4+lJeXU1ZWxsCBA5k+fTrh4eFNTvHFxcUxe/Zs/vOf/3DNNdc0tjhOZ8SIEURHR3Ps2DFmzZqFu7s7Dz/8MI8++qi6RnAZcEnlLoT4DhgJBAohUoAXsCn1pUKIO4BEYEZF9dXY3CBPYnOF/IcTZG5SKIqCxWK5YINRS+HkyZP2bD/BwcGUl5fzwQcfcPDgQaZPn05oaChZWVm4uLjg7e3NH3/8wZNPPsnTTz9N9+7dm5SC79y5M2+99RZ9+/ZtbFEahMrv5vnnn+eqq67Cx8eHiRMnsnTpUnudvLw8Xn/9dUwmE8HBwQ7LzqRSM0wmE927d3dKsDs1/EA9ycjIYM2aNYwcOZK2bds2tjgOx2g0cvLkSWJiYjAYDHzyySccOHCAWbNm8d1331FYWEi7du0oLCwkNTWVcePG4evryzfffMM777xD69atm5SCv5yIjY1l6tSp9OjRg4ULFzJgwAAef/xx5syZw/Hjx+2ZkQoLC9m8eTNBQUH07NmzscW+rDh16hQbN27k0UcfrdP5ashfJ+Li4kJYWJhDd0CWlJTY8zDWdEOTs/Dz86N///7A/2bx9957L5988glXX301HTt2JDs7G3d3dzw9Pfnss8/o1KkTEydOZNGiRTz77LNNet2gJZOQkEBERAQeHh688MILdOjQga1btzJw4ED7d6LRaPDx8bFnRvL09GxkqS8vnPn7vqx/deXl5SiKgqura51nl76+vnaPCUexe/du7rvvPj744ANGjBjh0LbripSStWvXMmzYMHbv3s2AAQMICQnhv//9L8HBwRQXFxMYGMjs2bOZO3cuTzzxBG+88QZnzpxp8t5QLZVJkyYxadKkxhZDpZFoeUbiWrB161ZWrFhhdxdrKnTr1o05c+bQrVu3xhbFTnl5OQcOHKB79+78/fffDB48mE8++YTrrrsOnU7H4MGDKSwsJD4+ngEDBnDo0CGCgoLIzMxUF+9UVBqBy3rmHhkZiY+PT5NbCA0MDGTmzJmNLcY5mM1mioqKcHNzw2w24+rqao9ff+DAAc6cOcOoUaOIj48nODiYnJwcXF1dHboBS0VFpeY0La3WwHTq1In+/fs36K7T5orBYMDX15fi4mLc3NwoKirCw8MDaUpl1JDWTJ04jO3bt9G+fXuSk5Ptppq6hD92JmlpaZw8eRKr1eqwNgsKCjAajeoTikqT4rJW7io1x2AwMGjQIPbt20efPn3YvHkzd999F+npCaCxkH5yEzmZqSTFH+PQ/t106tgeo9FIWFhYk/KWee6555gwYQIFBQUOa/O3335jyZIlTc68p3J5oyp3lRohhGDkyJHs2LGDPn36cOzYMfbs2cuQ4dP59IsN7D+Zxah+Efy64hvGd4P1S99mQJcggnx0TSpy4jXXXMM999yDm5ubw9rs2rUrffr0cap5Lysri9jY2Cb1t1Rp2lzWNvemhsVi4fjx4wQHBxMYGNjY4lxAeHg4M2bM4LvvvuMf//gH8fHxLF/xG1YFdh9MpE27jowfM5xScyqxcUd5/aFwNCe/BM+24NcN6d4K9F6NOpOfOnWqw9uMiYlxeJvnk5SUxJQpU/D39+fZZ59l7Nixl7xGzpw5w9GjR+nWrRulpaXEx8cDNnNkRESE02VWaVzUmXsDIaWkoKCAvLy8i868srOzmTVrFl9++SW5ubnMnTuX/fv3n1PHbDaTlZWF2WxuAKnPRaPRMGXKFIYMGcJnn31GmzZtOHHiBIMGDaZP785kGwvILNHz+zEtN97+EPmGTuQXlWPOPoCM/w5Ofg0pvyGLkpCWUqRUGnwMzZWIiAgee+wxOnTowI033sg111zDTz/9VO05W7duZcKECWzevJlvvvmGZ555hoceeoiff/652vNUWgYtUrlXPsI2NRvok08+yQMPPICiVK3UfH19ee6557j66qvJy8tj1apVHD169Jw627ZtY+LEiezbd/EEV5XRAJ3x+K7T6bjxxht56qmnWLduHRkZGfz99yHi4lLYf+AIXbuE89bb7zDmqpvw7jSZM54TiSvrQWJRIMWFuSgZ25DHP4dT30D6n8jSDKRiVU0NlyA1NZXnnnsOo9HICy+8QP/+/Tl9+nS151x33XV2d1qNRsMdd9xBWNi56RWklJSXlzfKZEHFubRIs8yxY8eIjY2lTZs2Tt8daTKZSE9Pr1FcjtGjR1NWVnZRs4SrqyvXXnstYFPQv/766wU7Btu0acO0adOqDdv7xRdfsHHjRj766COHR/6rjIRoMBg4fvw4N910Ey+++CJdu1xB3z6tiIs7yNDhE9HpQgkJCSUoKJjy8mhyc7I5nZmMKE7CT5zBz5yGa1EyIvMvcA+3mW0824CLP0K0yDlHvZBS8u9//5s777yTAwcOUFZWxuDBg6s9R6PR2K81IQTBwcFMnjz5nOsvLy+PV199lZSUlDpvgVdpmrRI5d6nTx+io6Mdumh2MbKysli5ciXjxo3Dw8MDV1dXAgICLqgnhGDGjBlVtFA1Wq22ynbatm3Lc889V+25Xl5eBAQEOG2Bz2q1smzZMoqKiti2bRt9+/ZFb/AgNi6V1NQUxozZS0BABJ6enmg0Gtzc3GgVHkFoWCuKi2Mw5mRxMisBQ0kqAdp0vMrjMRScRLj4gkck0i8G3FuB3hNo2mF1G4KNGzfy5JNPUlRUxMqVKzEajbz66quXdDPNzc2ltLSUxMREUlNT2bBhA4WFhdx66632On5+frz++uusX79edQluYbRI5e7u7t5gMVkCAwOZMGECfn5+3HDDDcTExDB//vwG6ftizJgxo1Y3ktqSkZHBDz/8QH5+Pg8//DBbtmyhf//+7N+/n4OH/mT//h20bd8TD4+uCGFTGEIItFotXl5eeHl5oUS2JT8/n+ysDFJzTuFedoYgcw6epQfR5h5EuAWBVwfw7Yp0DwWNgfoq+qysLL7//nuuvvrqZhXkLSwsjLFjx3Lq1Cn69bPFiIqOjr7keVlZWTz88MOAzeTXvXt3AIYOHeo0WVWaDi1SuTckLi4udOjQAYvFwj/+8Q9atWrV2CJVqwDPtm3XVVH6+Phw1113kZycTG5uLp07d6a4uJiOHTvy4D/vYuSIdug06RQVReLlZUvecb5NXavV4ufnh5+fHxZLFEZjLmkZZ7Dkn8ZLSSXQnI9H6XbI3o1wCwXfruAdhXQLpnKpqLbyJycn88knn9CuXbtmpdzj4uJo06YN7733Hhs2bACgd+/el8wKFh0dXaObgErLRFXuDqJyobGpk5+fz5NPPsmECROYPn16ndrw8PDgzjvvBGwuevPnz+eZZ54hJDgYSTlY92My5ZGadhoPj+72x/0///wTKaU98Umlctbr9QQHhxAYGERZWWfyjDkkZiQhSlLwJRVfUwauxaloMraBeyj4xYBnG6TBD6GpuSkhJiaGlStXEhwcXKdxNxbdu3dHURT+85//2Ms6derUiBKpNAdU5V4LysrKKCwsxN/fv0Hsk4qiYDQacXV1dVhIYUVRKCgooLS0tN5tSSk5ePAgnp6e7Ny507ZYhwtStMagL8DVJYuCgjx8ff0BLplIWqPR4O7ujpubG6Fh4RQVFZGbm82pzNPoi1MI1GXgVX4aQ8EphMHHpuD9uoFHBOjcAU21s3mDwdAsI1RKKSkpKTknZILqXaRyKVTlXgt++OEHFixYwJIlS2jXrp3T+yssLGTmzJmMGjXqkouoNcXPz49vvvnGIYutVquVY8eOMX78eHbv3o3ZbMZgMIAmCKQvgf65JKedxtvbF41Gw6hRo2rUbqVHjt0+37oNBQX5ZGVmcCbnNG6lyQQacvEuP4Am9yDCNRB8OoJPZ6R7BGj09naciZSSU6dO4eLiQkREhNP6O3ToEKWlpcyZM8eu1Lt06UL79u2d0p9Ky0BV7rUgJiaGqVOnOiS5ck1wcXFh8uTJDt0B6cik25mZmezbt4+CggKOHz+O0WgkJCQEgR40kegN+Xh5GMnNzSYwMLjWN5RKZWmzz/vj6+uH2dyB/Pw8MtKTSS5IwduahL+5AM/SbWiy9oBrEPh1Ba8OSLcQhKbqsdqUZOXst24LtYqisGvXLry9vZ2643PSpEnk5OTQrVs32rVrR3p6Ov7+/k7rT6Vl0CyVe6Vpwd3d3TZTbCB69epFr169Gqw/V1dXHnrooQbrD+Cvv/5i+fLlPPvss5fc3n706FHuvfdehg4dyqZNmzhx4gQhISEgBBAI+OPrk0XKmQT8/ALqfVOp9K8PCgomICCQsrIu5BmNpGQmIItT8BNn8DVl4lqcijBsR7iFIn27gVc7MHiDqOjflAd5h6HoFEgJHq2Rvj3ANbBWPvYajYaJEyc63US3fft2uytkaGio3RVSnbmrVEezVO55eXksXbqUQYMGqTkfHUxaWhoHDx6skU3+bDPLhSYXrW32rs/D2zOP7OwMQkJaOcx0Uek/7+bmRlirVhQVFZGTnc2prNNoi5MIMmXhWx6PJu8EpYobrgFR6AJiQO8BKauQ5dkkpOayfkc8t0/tg854CFpPRnp2qLGMQgj8/PwcMp7qCAsLY/DgwaSlpdGvXz/0er3drbE2mEwmioqKEEI0yTwGKo7lkt+uEOJzIUSmEOLwWWUvCiFShRD7K16Tzjr2jBDipBDimBDCsfnnKnB3d6dfv37V7tJUqRtTp07ll19+qdHfttI2fv77igKE8AcC8PWVlBQnOnyLe2Wflfb5Nm3b0rXPcMJ7TiU/4CriLIP487Q7k+//hJ9+XIY8vQx56msoz8JisfDL78eJO5VtCwdhzoO0DWCt/0Kzo4mLi2PRokWsWbOGefPm8d1339V6QVVRFObMmUP79u2Jjo5m3bp1TpJWpalQk1v3ImBCFeVvSSl7VbxWAwghugI3AN0qzvlAVO5icSCurq7069fPKZETKxfJUlJSav0DMplMZGRkNOs4HTqdDnd3d8fM6oRt9q7V6PHxKiA3J92pXh6V6wm+vr5EdepCdJ9RhPe+nq4DJlHuP5j0Mj+QZqSU/Pl3Mm1b+RDkf9Zmt7JsKDrd5DxRxowZw7Zt29ixYwdbtmzB39+fw4cPX/rEs7BarSxZsoQ333yTUaNGqcHDLgMu+QuWUm4BcmvY3hRgiZSyXEp5GjgJDKiHfA2O2WzmwQcf5OWXX671ucnJySxfvpz09HQnSNY8EcIHRAh+voLSkiTKysoaRHkKIXBxcaFjx468//77TL/pHtzCBwACi1Vhx8EUft+TyNZ9iRw8kWE7SZrB7LgkHo5Cr9fj7e1tf50fb6g2tG3b9pywFkajkccff5zPPvvModmpVBqf+tjcHxBC/B+wB3hMSmkEwoEdZ9VJqShrNuh0Oh5++OE6pYcLDQ1l+PDhTnmiKC0tZeXKlfTq1auZbWDRgCYSQSZ+vkXk5qbRqtW5bqRSShRFOSfQlSMRQtjs8/7ByHwtOiRP3DaY/MIyFi7fR/eoyk1NGtC6OLz/+rJmzRpuu+02++fIyMg6XQNarZYvv/ySuLg4Bg4cCNjCErz66qts3LhRjS3Twqjrs/eHQAegF5AGvFnbBoQQdwkh9ggh9mRlZdVRDMej0WgYN24cV1xxRa0VjYeHBzExMU4JWJabm8t//vMffvvtN4e3XRMSEhLYt29f7Wd3QoDwBNEKby+BqTyZsrKyc6okJiaydOlScnNr+oBYe4QQ4BqCcLOl/dNpNfh5u3H/Df0x6CuUmsEXPNo1uUBlkyZN4ujRo/bXH3/8UWvlrtPp+PDDDykrK6NLly48++yzwP88kJwdPVWl4anTNyqlzKh8L4T4BFhV8TEVaH1W1YiKsqraWAgsBOjXr1/TMnI2QUJCQli8eLHN1bAReP/999m5cyerV6+utVlAIJAiAqHJIMi/lKzMZCJaR9nt+pV2fqfPHHUeEDQYUlaBtQSNRuDtUTFT17hA0CCbgm9iOCIQnhCC8ePHM378eAdJpdLUqZNyF0KESSnTKj5OAypXd34BFgsh5gOtgI7ArnpLqYJOp6NLly6N1v+dd97JlClT6vZUIgQCDxDhuLufIjs3hdLSVnh42G4S4eHhDvF8SkxM5JdffuGGG24gKCioCjEE0qcLaN0gZw8Un7b5ubtHgH9f8O7U5GbtVXHkyBGsVis9evRobFFUmjCXVO5CiO+AkUCgECIFeAEYKYTohW2LXwJwN4CU8ogQYikQC1iA+6WUTXqVxhFREi8HOnfuTOfOnevXiKYVQqYTHFRERmYSbdpEO9TOHhsby6effsrgwYOrVO4AQmiQnu3AIxIUMyBt4QqE7pJyKIpCUlISHh4eBAUF/e/akaBYLGh02ooNXI6/ls5fhK6MtKlesyoX45LKXUpZVajDz6qp/wrwSn2EakjKy8t55pln6N69O7fffntji9NyEQKkG4hw3FyPo9OkUVQUjre34zJFjRo1il9++eWCVHIXiiJsu1UvEprgYlgsFjZv3kx4eDjjx4/HWlZO+vq/OPXFjxSdTELn6UabG64i8roJuLUKdqjijYuLY+bMmfbPgYGBfPHFF80qdLFKw9Lkt6hJKdm+fTuFhYVOa7+wsJCSkhKntO9MFEXh888/Z8mSJU3ON7tKhABNGAgvAgPM5GQnXjSfbF1wdXWlTZs2TgtJodPpmDhxIgMGDMBaWk7c65/y102Psf6nlXx1eDs7d+xg32OvseP2ZyhNzXDod1KZUempp54iNzeXAwcOsGzZMoe1r9LyaPLKHWyeGsXFxU5p29XVlY8++oj77rvPKe07EyklmzZt4o8//mhsUS6g0r3xfOUtcAERgauLBhdDJnl5eXYlKKXEam26ybI1Gg2hoaH4+vqSs+cQsf/9lOLiYo5SwnB8iKWEMquZ9A3bOf7BYqTFcRZJjUaDlJJDhw5hNpsZNGiQ6uGiUi3N4uqYOnUqLi7O8T92ZJTEhkaj0fD+++83SoyQlJQUzpw5Q58+far8+ymKwtq1a/H09GTYsGH/M1FUzt7lGfz98jiTnoCvry9CCEpKSli1ahUxMTF069atgUdUc6SUnP76Z6wlZRRjxQMNHmjQITCh4KooJH63ii6P3Y5LgK9D+mzVqhVdu3ZlxYoV3H333dx9990OT36u0rJo8jP3yg0ozTXIUWlpKevWrSMhIcHhbVcGgPLy8nLqwlpeXh6//fYbmZmZ9rJFixZx//33k5eXd1HZ3NzcqrwpC3SgicTFoMXdNQejMQcppT1Zh16vv6RMVquVpKQk8vPz6zyuuiIVhdL0LJASA4JyFKxIFECL7XsozzJiLSt3WJ+5ubkUFxdTVlbGli1bSE5OrnajXUlJCddccw0jRoxg48aNnDlzhmnTpjFixAg+/PBDdTfqZUDz1JjNCLPZTEZGhtPWDBqC8vJyMjIyzlmXuPnmm3n99dcvOnsUQjBy5EgGDBhQ9Y1HBIPwJcDfSp4xAYvFgqurK1dffTUdO3a8pEy5ublcf/31fPzxx3UeV10RGg0ugbZokJ5o8UTLGvLwR4dbxU9K7+eN1tVxtv+OHTty9913k5aWxrZt2ygoqD5Mgl6v59prr+X06dNkZWXx+eefk5CQwMiRI3nmmWcuej0qikJxcXGTNY2p1JzLTrlLKe2vhsDLy4sbbrihwc0M6enp/Pbbbw5ZqwgODubGG288J0Vdu3btGDly5EVn2WdHbKziIAItaNqg02nxdM8hPy/nnCqX+n68vLy4++67GTNmTIMrIiEEbW+ajNbNBYFgOD5Mwo+BeKFBgEYQOWMieq+6x4A5n8OHD/Ovf/2LW2+9ld69e9OhQ4dzjpeUlLBw4UI++OADPvjgAxYtWsSMGTPOCYXRtm1brr/++nPOKyws5KOPPmLNmjUoisKZM2d46qmn7NdNeXk5OTk5DfqbUXEMl51yLyws5L777mswTwMhBHq9vsHNShs3buTxxx8nMTGx3m1VblF36BgqQgILTSCBAYKCAltIYEVRWL9+PXv37q32dFdXV2677Tby8/PZuXNngyueoEG96Xj/zWjdXNAhcEWDFoHQagga3IdO992E0DtmLScjI4MzZ87QvXt3tm/fjqurK+eH7DAYDAwZMoRhw4YxbNgwBg8efMGNNzs7m127zt1T6OLiwtChQ+natStCCPbs2cPRo0fJyspCSsmqVat44YUXePfdd0lKSnLIeFQahua5klgPpJQOSxDdlJk4cSJRUVFVZuuxWCyUl5c3/lqG0CFFGzSaHHy88jDmZhAQGIbJZKKsrIysrCy8vb2rXUw3mUyNsiCu83Cjxwv/xK9nF0598QNFp5LRebjRZuZVtLvlGjzaOi6n6ubNm7nxxhuZOXMmwcHBvPrqq0RHR58rj053ztNheXk5L774IlarlQ0bNvDPf/6T5cuX89JLL3H33XfbwxkYDAZiYmJIS0uzJzy/8sorOXXqFIGBgezfv5+nnnqKsrKyOgXTU2k8Ljvl7u3tzZdfftlsF2hrir+/vz3y3/kcPXqU7du3c+211zZ+Lk7hAyIYX58zJCYn4ucfwqRJkzhx4gRXXXUVTz75JNddd13Vp1bES3E0RUVFnD59mg4dOlQb00Xr4UbbmyfTZuYkFLMZodOi0ekqnkoct8AdExPDvffey6JFi1AUhbCwMB588MFqdwwbDAZeeOEF5syZY/cI27VrF1JKtFptlXF8Kt1SJ06cyIoVK9BqtQQFBREWFtZsPcouZ1q2hquCygu9qSr3yk1Vx44d4+DBgyQmOj6DUUBAACaTqU4JSc6nqKiIv/7665ILfBdDVIQE1mhdCPArJivzDEIIAgICmDp16gUz1HPOFQKNRuPwUMEZGRls3bqVnJycautVrilo9Dp07m5oDQaEE8IWx8TE8Pbbb3P8+HE+//xzsrKyLrmWUmkONBgM6PX6cz5fLECb0WjE29ubTp06ERISwmeffcaMGTPUUMDNFNEUFkn69esn9+zZ09hiNCpSStLT01mxYgXbtm3DYDDg6upKfn4+fn5+TJkyhWHDhuHq6lrvvsrLy5k6dSqRkZF89NFH9VJG27dv54477uDtt9+u9Sw6Ozsbk8lEWFgoKEdRLMkkJrvRKmKAQ8ZZFaWlpZjN5mrdR00mE3l5efj5+dXILbMlsH79ek6dOoWrqyu33norhYWF5Obm0qZNGzV+jRM5ceIEK1eu5NFHH63T+UKIvVLKflUda/LPWlJKzGZzk55t1xcpJXFxccybN4/o6GgeeOABSktLKSkpITAwkOzsbJYtW8bOnTt55JFH8PDwqFd/er2eF1544aKhe6WUlJeXoygKbm5u1f64u3Xrxttvv03//v1rJYOUkjlz5tizV+l1EWi0WQQFlpCZmUzr1lFOUSrbt28nKSmJG2+88aK2fIPBQHBwcJXHWjLJycmMHj0asHkjOXv/hIpzafLKHeDnn39m+PDhjRbL3Jnk5eVx4sQJ3njjDSZPnoyiKLz77ruEhITg6+tLQkICbm5uzJo1i2XLlrFw4UIeeOCBes0oNRoNV1xxRbV1XnzxRdLS0vj000+r7cvb27vKGXtNom1Onz4do9GIVqtFCC+kCMXDPYEcYyolJa3qfROriqioKAICAlQbchUkJSURGRmpKvQWQrO4woODg+v9mF5cXEx8fDxRUVFOyZRUV2JjY5k3bx5jxozBbDazfft2nnrqKUpLSykqKmLq1KkkJCTw2Wefce+997JgwQLi4uKcHss7OjqaoKCgOv/QrVYrL7zwAsHBwTz44IMXtCOEYOzYseeeVJHQI9CvhJzsZNzdOztc0URGRp7jr69io6ysjDNnzhAREdHYoqg4iCZv5xBCMGLEiHrH0fjrr7+44YYb2L9/v2MEcxBt27ZFURT69u3Lhg0bePLJJ/npp5/45JNPWL16NfPmzcPNzY2xY8fy66+/MnLkSNatW3dBO1JKSktLMZlM9ZZJCMFtt93GY489dtEZ7ubNm/nvf/970WiaUkqKiopqtYlKCHeECMfDQ4MgjcLCQnXjTAOh1+u5/fbbnbbWodLwNHnl7ih69erFnDlz6p9wwsHk5uYSGBhIUlISUVFRnD59moyMDEaPHo3VauXmm2/mq6++onfv3pw8eZLOnTtz6tSpC5SexWLhxx9/ZOvWrQ0i999//82aNWsuul9Ap9Mxf/58nnrqqZrPvoUATTgIdwL8ysnNSVKVex0pLy9n3rx5/Pvf/+aNN96gpKSEN998kzlz5rBt27YL/q5arZYuXbqoJpkWxGWj3IODg5k5c2bj+3WfR6X7mdFoJDg4mISEBHr27MmGDRuIi4sjLy8PvV5PSUkJQgg8PDwoKiq6IJSuRqOhU6dODWZyuPfee1m+fDl+fn5VHhdCXNSfunpcQLTGzU2DTptOYWHdXCwvdxRF4cSJE/j5+fHWW2/x3nvv8e6777Jr1y5uuOGGOruuqjQfmoXNvSUTGBhIXl4eISEh7Nu3j0mTJvHFF19w9dVXc+TIEdzd3dHpdPbdpAUFBfj4+FzgOaTVamvtsVIf3Nzc7GsXJpOJ06dPExgYSEBAQP0arggJbDWnIsjkzJlTeHr0/N9NwgEzy6SkJOLi4hg2bFi1m5TKysooLy/Hy8uryXtqmc1m4uLi7DNyjUbDxx9/zPLlywFb2I0+ffrw8ssvM3ToUHs9k8nE0aNHiY+PJzc3V/Vpb2CcGdJBVe6NTEREBFJKQkJCSEpKwtfXlx49emA2m5FSkpyczD/+8Q927txJdHQ0hw8fplu3bk3q8fn06dMMHz6cf/7zn/zrX/+qd3sCPVm5Afz40xYG9ClEibCg1etAGwTaUBD6ein5wsJCMjIyLrk5bPfu3Rw7doybbrqp2ptAU8BisbB79257KF+dTkdiYiIPP/wws2bNQqfTYTabL5ixm0wmdu3axd69e/Hx8XFICOXvv/+eGTNmOOQaXblyJSNHjqx36AOLxcKKFSsuCJxWVxw1xszMTKc5eKjKvZFxd3dnyJAh/PHHH0ybNo1XXnmFp59+mh9++IGFCxfyyy+/cPToUbZu3cr999/Pm2++ydy5cxtb7HMIDAzkgQceYMSIEQ5r08fLk5mTI3F3U1i39idMJgvDBscQENQG4dYX8Kyzgu/SpQudOnW6pDtk+/bt8fT0dFraPkfi5ubGHXfcYf9cVFREly5dcHV1pXPnzoSEhPDuu+8yYcIEBg8ebB+Tp6cnd955J97e3oSGhjJ8+PB6y3L48GFmz57tkKed1NRUbrnllnrvOygvL+fkyZPcdddd9ZYJHDfGyk1MzuCSyl0I0Rr4CggBJLBQSvmOEMIf+B5oCyQAM6SURmG7lb0DTAJKgNuklPucIn0LQKvVctNNN/H0008TGhrKrFmzOHLkCBs2bMDb25vvvvuOdu3acf3117No0SImT55cZTCwxiQgIIDnn3/+gvKSkhJOnTp1yRgtF2LFXXsK90A9xSVltAr1Izu3kJ9X7eC2Wa5oxWFw609d5yaVIQsuRXh4OOHh4XXqo7Fxc3Nj3bp1WCwW9Ho9HTt2ZNu2bZhMJlq1anXB9xEcHIyvr69D+u7YsaPDnizbtWvnkF3CQgiioqIcIJENR43Rzc2N1q1bO0CiC7lk+AEhRBgQJqXcJ4TwAvYCU4HbgFwp5TwhxNOAn5TyKSHEJOCf2JT7QOAdKWXVEawqaKjwA1lZWXz00UdMmzaNmJgYp/dXU6SUpKSk8Nprr9G9e3cyMjJISkqiuLiY/Px8unbtyqFDh5gxYwa33HJLs5hJAmzdupV77rmHBQsW2Hc+XhIpwZoBpTsAc0WRZNvOY5yMT+eWG4aj0biB22DQBjjEBn+5k5eXR35+PgaDgdDQ0DoprezsbLvbq5+fH0ajEbCFFA4NDa1VW1JKe5RKd3d3uykpICDgoruqL0ZZWRkZGRkXlHt5edXaueLsMZ5NWFhYjX+T6enpmM1mwsPD7e+FEPj6+tr/Zt7e3hd1VDif6sIPnJO8oiYv4GdgHHAMm9IHCAOOVbz/GLjxrPr2ehd79e3bV14MRVGk1WqViqJctE5NiYuLkwMGDJA//vhjvdtyNIqiyMLCQrlt2zY5dOhQOWDAANmlSxc5cOBAOWrUKPnWW29Ji8XikL+DIygrK5M//PCDPHTo0EXrZGdnyyVLlsjMzMyL1omNjZVLly6VpaWltgJFkbL8pJQFS6UsWCqV/O/lif3vyv+8eJPMTvhMKvnf246Zkmx1VeqF0WiUY8aMkR4eHrJ169Zyz549dWrn9ttvl2PHjpVhYWFy0qRJ0tPTU06aNEmOHj261m2ZTCbZtWtX2blzZ/nII4/I/v37y7Zt28pvvvmmVu0oiiI3bNggAwMD5bhx46Sbm5ts3bq1HDhwoHzkkUdq1VZiYqLs2bOnHD58uAwMDJR6vV5OmDBBent7ywMHDtS4nVGjRsnQ0FCZn58vu3btKq+55hrp6+sr33zzTWkwGKSHh4d86qmnavw7B/bIi+jVWhmMhBBtgd7ATiBESplWeUPCZrYBCAeSzzotpaKszqxcufKSEfpqQseOHVm9ejVXXXVVvdtyNJU5R3fs2MGVV15JmzZtSElJ4Y477kCv19O2bVuHRz+sD4WFhfz3v//l559/vmidgIAAZs6cSVBQ0EXrrFq1innz5p23kPe/y7KwqIx583/Cz9eDjMy8igmD4DLy4nUqBQUF7Nixg82bNxMWFsa+fXWzoL7++ussXbqUmTNnsnr1anr06MHYsWOxWCy1bmvRokUcP36ckydP8vnnn3PDDTcQFBR0gftvTXjppZcAW6iL6667jrFjxxITE1PrHLKlpaUYjUb27duH0WjEzc2N2267Da1WW6u9GM8995z9bxIYGMjNN9+MxWLBarUSExPD1q1b+fLLLx3iRVPjX4gQwhNYDjwspTxnyb3iDlKr3SZCiLuEEHuEEHvOzypzPq6urg5x0dJqtQQEBDRZs8aZM2f49ddfOXToEK6urgwYMIANGzZgMBjYu3dvg27oMZvNJCQkXHSHqZ+fH1999VW9F6huv/12vv322/+5UAoBWn8Qtrgyer2Wu/4xlr692+Ph4QoIEF6g9TnHJBMfH8+6desoKyurlzyXK2FhYdUmRbkUXl5efPbZZyxevJhnn32WUaNGMXr06FqvWeTl5fHuu+9yzTXXYLVaKS8vr7lJ7yLttW7dmj///JOvv/6a8ePHc80119R6rGazGYvFYp9kBQYG0q9f1daQ6jh7p32HDh244oorGDVqFGALWBcWFkZRUVGdbornUyPlLoTQY1Ps30opf6wozqiwx1fa5TMrylOBs1cIIirKzkFKuVBK2U9K2a+6mR3A+PHjHbbY05TZvHkzO3fuRKPRMHDgQLy9vRkzZgynT59mxYoVpKenN5gs8fHxTJkyhZ9++qnK41qtls6dO6MoCgsWLCAlJaVO/QQEBBAdHX2u54rGG/RtAIGbq4EBfaPo17sDbVoHodFoQd/OrvwrKSgoIDMz0yE/issJjUaDXq/n6aefJj4+vs4K/qOPPuLZZ5/lhRde4Oqrr2bBggWsWLGC1NQLfvrV4uLiwiOPPEJERARBQUEMGTKE1157rdYz7UpmzpxJVFSU3dvm1VdfZdmyZZSXl9eqneLiYoqLi+nfvz8uLi7Ex8ezaNGiWk+4SktLURSFvLw8tmzZwrfffsvmzZsB22/uiSeeYOTIkdU+7daUmiyoCuBLbIunD59V/jqQI/+3oOovpXxSCHEV8AD/W1B9V0o5oLo+1HjuNo4cOcKyZcvs5her1Yper8dsNhMYGMjtt99+Sa8Tk8lEUlISoaGhtV58Opu8vDyWLFnCmDFj6Nix40Xr7dy5k9mzZzN//vwLA4HVFSlBlkHZNqTFiO2hUIDWF6FvZ1P8QndWdYmiKCiKgk6nazDTVaW/fJs2bZpt3HdFUVixYgWbNm0iLCyMJ598sk5PtosWLWL37t14eXkhpaS4uBgXFxciIiJ45JFHat1ebGwsS5cupXv37mzduhVFUZg1axYDBlSrSi7g+PHjLFiwAL1eT3l5uX3X9ODBg2vl826xWPj88885cOCAvczV1ZXy8nKefvrpGgdce//994mLi8PLy4uioiIMBgOKonDVVVexYsUKAMaNG8fUqVNr1F51C6o1Ue5Dga3AIaDS6PUsNrv7UiASSMTmCplbcTN4D5iAzRXyH1LKajW3qtz/R1lZGffffz/t27cnIiKCm2++uVbhaTMzM/nhhx8YMWLEOTk1nYXJZCItLY2QkBDHBZ2SEpRcpGkHGZkWTiYIort0IyAwHCFcLvCQ+eijj4iPj2fu3LkNanI7cOAAf/31FzNmzCAwMLDB+lVRqaReyTqklH9iW8GqijFV1JfA/bWSUMWOoih4e3szbdo0li5ditVqrZVy9/Pz45prrql/GIAaYjAYaNOmjcPbVSypFBeVkZKuxyK9cfcMQ2iqvnkUFxc3SgTJjh07EhAQcFmYDFWaH5fdDtXy8nJWrVpFdHR0g8xs60JycjIbNmyw5+isDXq9vsaPiEVFRaxatYqBAwfSrl27uojqcBRF4eTJ45QVHmHbrmS8vELp3af6GPwPP/ywPfFzQ+Lu7t7kwxKoXL5cdv5khYWFvPbaa9W68DU2nTp14t5776WsrOyi8dIdQXp6Ov/+979Zvnx5nResHI3VamXHjm2cPJVFUKAvbu6etGrVqtqbnFarbVBbu4pKc+Cym7n7+fnx9ddfN5jZoiZULkCBLeBTZmYmr7/+Om3btnVKqrlKIiMjeeaZZ8jJyaGkpKRGwZmklJSUlKAoCp6enjVWqGazmeLiYry8vKqdYeu0gqvHR6JY84k7acDFLaTOC8Pl5eWkpaXV281PRaU5ctkp90oXvqbGunXrkFIyffp0Pvnkkwbp02AwMHnyZIxGY63MCxs3bqSgoIApU6bUWMGfPn2ajRs3MnXqVMLCwqquJCVC5uPnU0ZmtoGiEg1t24fV2dyya9cu7rvvPt577z2HBjVTUWkOXHZmmaZKTEyMPd5Npa29Ljb32uLr60u7du1qpUC7du3Kvn37mDlzJnl5eTU6Jzg4mH79+l0iXaIEaypIK8lnJC4ubgQHB9f5b9CxY0fuu+8+OnXqVKfzVVSaM01+5i6lJCcnB19f3xabsV4I0WwUUGV0vbFjx+Lu7l5j/25fX99LJxORZWDNpLBYYMwXRLSueUCmqggNDeXee++t8/kqKs2ZZjFzX7NmDZmZmZeuWAeklOTm5lJUVOSU9psCJpOJt99+mzVr1jiszUmTJjF37tx6bZQ6BylByULKYjKzBRIDISEhlz5PRUWlSprFVLh///5Oy31aUlLCzTffzIABA+xBhloaJpOJtWvXkp+fz8SJExtbnItgBWsqFiukpEt8fHzx8fFxmllKSonFYnGal43RaCQ1NZVOnTo12VhGZ2O1WpuMx9TlRt1yDV+aJq/chRBER0c7rX29Xs9VV11V7Rb75o6HhweLFy9uukpGSlDyQMkjO1dQUiqI6hjuVL/1ffv28dJLLzF37lx69Ojh8PYTEhLYsWMHrVq1anJJ2ati8+bNbNy40SExTVRqTl5eHuHh4dx9990Ob7vJK3dnYzAYeOCBBxpbDKcihKhx8P9Gw5qG1WomLVODu7un011VXVxcnBohtEuXLkRERFxiAbnpIKXk+uuvp0+fPo0tymVFo6bZU7m8URSFn376CYPBwFVXXeUcM4ksAyWdkjJBVg60jnRgnJqL0K1bNz7//HMAFIsVS3EJ1rJytC4GdB7uCJ22XmN1dXV1+hhUVKpDVe4tjLPjqzhCEUsp+f7773Fzc3NOkhMpKS5K5UxyJiaLB4p0ueSOVEcghEBKSVlGDic/WUrCt79QlpGN3tebNjdMouPdN+DRJlzd9arSbFGVewtCSsmbb75JUVERzz//vENs1hqNhnfffdchmeyrxkJS4nE2bE4mIqId4RGtHOeBUw1SSsqzctl17xzOrPydFGsJeylC5AnG/DeFnB0HuGLRPDwinX+jUVFxBs3CFbIhkVJiNBrtSXmdgcViobi4uE5pwy5FcXGxQ906hRCEhIQQFBTkHCWnFNI2wsyQK9pgcHEnLKzuO1JrS8rPGzmz6ncUq4UwDIzEBwVJnmIia+seTn/9s22xV0WlGaIq9/Mwm83Mnj2b559/3mkhZI8dO8bixYvt2c4dyb/+9S9ee+01J860HYhUwJqCqwtIPHFz86jXjtTakrB4FdJiRSAQwAnKKMKKO1qkVSHxu19RzGpmJ5XmiWqWOQ+tVsvEiROd6r4WGBhIdHR0tWFs64IQosHD3tYZKUGWgpJJUYkgxwgRrUMbzF1TWq2YC21POBKJFUlvPChDIR0TvuiwFBajWKxom0DMMaPRyB9//EGnTp04fPgwQ4cOpVWrVrVu5/fff8doNHLVVVc1XddYFYfQbJX7qVOniI+PZ/jw4Q6N+KfVarnjjjsc1l5VhISENMndl1arlZSUFPz8/PD29nZ+h0o2UikhPRNAT2hoqPP7rEBotXi0Dce49wgApyknlhJ0CHpii8Tp0aYVWkPT+InEx8ezceNGnn32WfLz8/n4449rrdzz8/N58sknOXbsGImJiapyb+E0g2f3qsnPz1cTIjuYjIwMrr32Wr744osG6M1mkjFbJGcyBT6+thtKQy5edrhtOlp3VwSCKFy5Bn8m4YcHGjQuBtr/41pEE4pntHv3bpKTk2nfvn2tz5VSsmrVKg4fPnxOuaIoGI3GFh1+43Kl2Sr3nj17MmPGjAbNhKMoCn/99dcFP5DGREppf9UXX19f7r77bkaOHFl/waqjYkeqVPIx5msoKobwcOfuSK2KkNFXEP3oP9C6uVTY3W3/tAYDHe64lsjrrnRIP/X9jk6cOIHVasVkMrFgwYI67aa2WCzMnTuXsWPHYjab2bFjBwAFBQXMnz+fFStWOGWBX6XxaDrTklrirHgM1WEymXjppZfo2LEj77//foP2fTFOnDjBK6+8wsMPP0zv3r3r1Za7uzuzZ892kGTVIcGahqKYSU0TuLnZdqQ25KxdCIHO3Y1uz9xN8PD+xH+5gtLUDFwC/Gh7yzWEjroCvbdjXDLNZjMbNmwgIiKiTqEO9u7dS+vWrRk7dizXXnstYWFhmM3mWrWh0+n46aefiI2NZfPmzbRt2xaw3dBffvll1q9f3zwW4VVqTLNV7o2BwWDgjTfeaBA/7JpisVgoKCio9Y+9cTGBkkZxCeTkCcIjQhstU5LO3Y2wcUMIHTsYaVUQWo3DbzJSSsrLy+v1HaWnp1NQUEBycjKZmZm1DichhKBz586EhYWxePHiOpl2VJoXl1TuQojWwFdACCCBhVLKd4QQLwKzgayKqs9KKVdXnPMMcAdgBR6UUq51guwNjkajcUqQqfrQpUsXli1bVuOnmLKyMlauXElMTAxdunRxsnTnIqVEURQ0SjrIMjKzNVgVXYPsSL0UQgiEzjlPggaDgSlTptR5jBqNhv/7v/9DURS++eYbpJQsW7asTm15e3szefLkOp2r0ryoyczdAjwmpdwnhPAC9goh1lcce0tK+cbZlYUQXYEbgG5AK2CDEKKTlLLFxxMtKCjg0KFD9OzZs8Fm90KIWiUxyc/PZ968edxwww0NotwVRSE3N5eMjAzy8/M5fPgQ40eGEh4qOJMBfn7+eHl5Nbpyr0RRFA4cOICPj4/DZrf1zajVs2dPXn755XPKnBkpVaVlcEmtIKVMA9Iq3hcKIeKA8GpOmQIskVKWA6eFECeBAcB2B8jbpNm5cycPPfQQn3zyCUOGDGlscaokICCAb7/91umhXRVF4fjx4yxbtozY2Fi8vb0xm80kJpzkwD5fpl4zGrNsT7sOrZqUrbe0tJRHH32UXr168dZbbzW2OIAt/+yXX35p/1xYWEinTp1U04pKtdTK5i6EaAv0BnYCQ4AHhBD/B+zBNrs3YlP8O846LYUqbgZCiLuAuwAiIyPrInuTo1+/frz99tt07979nPLKxBC1nWXXlR07dnD48GFmzZp1QWRCnU7n9Fmf1Wpl48aNfPrpp4wbN46JEyeSnZ2NlJIg3zyOHt3Nf+cvZ8jQMQwfMdqpstQWV1dX5s6d6/SQw7Vh1KhRbN682f5506ZNjSiNSnOhxppGCOEJLAcellIWCCE+BF7GZod/GXgTuL2m7UkpFwILAfr16+fQff6OjoxYU/z8/Bg/fvwF5RaLhRUrVtjND3369CE4OLhOfdRkbBs3buTXX39l+vTpDR52VkrJ33//zYIFC3jkkUfYvXs3b7zxBu3bt0cjLJw+tZeI8AAmTpzM+g2b2bp1KxMmTGhQGatDq9U2uaeuVatWnbOxzmKx8P333zeiRCrNgRo9Dwsh9NgU+7dSyh8BpJQZUkqrlFIBPsFmegFIBVqfdXpERVmDERcXx6pVqygvL2/Ibi+KEILQ0FDy8/N57LHH+OOPP6qsZ7FYiI2NJTs7+6JtHTp0iNtuu41jx45RUFDA4cOHLxjnAw88wNKlS/H19XXkMGpEcXExn376Kffccw8bN27EbDYzZ84cJkyYwNjRA3j6sal4efuzZes27r77br755hvy8/MbXM7mxJgxY9i6dav9tX37doYNG+aUvio9eyonERaLhaysLDIyMigrK3NKnyrOoSbeMgL4DIiTUs4/qzyswh4PMA2o3NnzC7BYCDEf24JqR2CXQ6W+BGazGZPJ5LTAX7VFp9MxfPhwSktLadu2LZ07d66yXnZ2NrNmzeLmm2/mscceq7KO2Wy2uz6uXbuWl156iSVLlhATE2Ov4+Pj0ygZgKSUHD9+HKvViru7O9nZ2TzyyCO8/fbb6LQaNDIdY14+Y8dP48DhHMrLy2nVqhXbt29vwrldGx9fX98Gu1EXFRXxzTffcOedd6LX6zly5AhffvklISEhTJkyRV3IbUbUxCwzBLgFOCSE2F9R9ixwoxCiFzazTAJwN4CU8ogQYikQi83T5v6G9pTp0aMH3bt3bzIeGJW4ublxxRVXXPS4r68vzz333DmK+nz69Oljd3309/fn+eefb1JrFrGxsURFRbFx40YmTJjAb7/9Rvv27dFpJcUFhURFhbNy1e9MnTqVHTt2cMUVV3DkyBEmTJjQ5L6v+iCltEW9VKyABI0OhGN86E+ePInVar3oJKE+5Ofns2XLFmbNmoVerycpKYmRI0cyadKkJrXwrXJpauIt8ydQ1RW5uppzXgFeqYdc9aK+rmeNhaurK9dee221dc5elA0PD2fmzJkNIVqNyc3Nxd/fn/j4eMLCwti2bRvDhg3j/ffmgVLG6DETKSk5Sfv27dmyZQs+Pj4cOnSoscV2KFIqyOx45Kk/ICPWpuQDOiCiRkBIF4SmfovqxcXFWK3OmS9lZGSQnZ1NeXk5Xl5eZGdnEx0d3SCOACqORf3GVBzC6dOnOXHiBF5eXqSlpREZGUlCQgKdOnVi91+buH5KP7LTs0k+sp8Yf8g7sB4/czZZB38n0JqHPPEHUqu3zXC1OtDoQauveK9DaLQgBAgNIP73/uz/qaLsEvWrmgRIKUlJScFqtdKmTZtaTRSklMi0w8idn3H8dBKfbTrO1H6tGdQpB5kRC71vgPbDaj35KCkp4aWXXrLHhJk3b16tzq8pWVlZFBUV2e3uOTk5BAYGOqUvFeeiKncVh1BYWEhGRgZdu3blr7/+4v/+7/9YtGgRTz31FN8lHkGvF5Rm51CUFM+tYzrxzYrvGNwxmK3rjjC9fxssf36KzcJnw6b6zvYM0oDQgqaKV1Xl55RV3hyqOl931g3lf+9nPzAXY0ExW759G72La5V1bO2eVa7VI60m5JFfoNRIhJ8bI7qEkFtksg3CVIT50E8YQruBR+1cLePi4nj//fd59dVXcXFxqVMs95pQ+eRVXl6OoigUFxfj5eXllL5UnIuq3FUcQrdu3ejSpQtmsxkfHx/S0tLo1KkTH3/8MffedScPP3Q/sUeO8Y8Z1/DpodMUe3Yg3SuCBMWCoes4Yl20CKmgQUFz1v8Cq/2zkNL2PwpCkWgUxX6OwIJGmmzHpERQeY6CBnlWvXPf26iYRZ81m57ZUVBS7go7FmHVVD4VVNY8v774339CgMaCANwMWtwMWkrK/2dC0ZYakelH6jR7B5v3ilardZqzgNFopFWrVpSVlWEymRBCoNfrndKXinO5rJV7YWEhJpMJf3//Zmmjb0pURunU6/XcfffdvPDCC9x6660UFBTw+9btHD2ZAno3Sgz+9BjVEy8vL1asWMEd/3yCoK5dURTF/rKc9f5/Lysoyv8WKqVESsX+HqlUfK4oV84tu/C9BMVqU/TSiqbiJiKwfe5+5QA0UiFOWs+64VjPuvFcWC6kgqssw0fmn3OjOBshrWAqrvXf18vLiwEDBrBq1SrAtrBeGdnRkeTk5NC+fXvy8/Mxm81oNBrV3t5Muay/tb/++ovMzExuvPHGZjk7iY2NZfny5dx999113hTlDKKionjiiSd48803mTlzJr/++itjxozBbDYTHx/PuHHj+Oyzz3jwwQcZOXJkjW6slTPVyrjoSoWir+pV22MX3kiqfplrUMe7PJ1uuevRKibiM4v4eW8yFqukW2tf2gZ52tYSXGvvpqrVas9xh6zt9Sql5MyZM1gsFjQaDeHh4Rd4v1itVkpLS4mKiiI3N9c+c1eVe/Pksv7WevToQXFxcfPJO3oeJ06cYNWqVVx33XVNSrlrNBp69+7Nm2++yV9//cWRI0fs4RdcXV05evQob7zxBmFhYTV+YqqsV/m/o76zupo3LnqeuRT5VyKkH6ZdsCev39QPAK22YpxewYjQbrV+UszLy8NkMtGzZ0+AWgems1gsjBs3jujoaDZt2sSnn37Kddddd06dsrIy9Ho9QUFBZGRkYDKZ1Jl7M+aydlxt1aoVHTt2dLr/rqIopKWlYTQaHdruxIkTWb16NZ06dXJou5UoikJqamqddpAKIfD19WXz5s3ceeedgM2Fb/bs2WzduhV3d/cmYQqrdJut7Uuj0VT5EgZ3NN2nglcIGqFBr7O9NEIDbr6IHteCa93y027bto0vv/ySL7/8kpSUlFqdq9Pp2LhxIwsWLCAmJoaNGzdeUKesrAyDwUBQUBCHDx/mzz//VGfuzRj1W2sALBYLa9eupVWrVlXGnqkrBoPBqQGuysvLWbNmDR06dGDUqFG1OldKyc6dOzly5AhZWVl06tSJsrIy1q5dS3p6Ohs3bmT69OlNQsE7EiEEMjAKzbCHURL+gvQjFX7uUYj2QxH+bes05tatW/Piiy/aP3fo0KHWcrm7u3PvvfeSnp7OBx98ANi8Y15++WVSU1O56aabMBgMtGvXjoEDB2Iymbj66qvVzUvNFFW5NwA6nY7Ro0c3aL5XR2AwGBg7dize3rWfaVqtVn788UcOHz7MP/7xD/R6PYWFhQQHB7N161aWLFnClVde2aSyWoHtRvzdd98RERFR6xtaJUJokD6t0PS8HnpcB8gKf/u6B7JLTk7m2WefxWAwYDabad++Pe3atavx+VarlXvuuYfNmzezbNky+83Bz8+P+fPns2HDBvLy8jAYDHh4eDB16tSzxtOybsCXC6pyryHZ2dkUFRURGRlZ65mMRqNpUiECaopWq62zR4ZGo+Hqq6/G19cXvV6PoigYDAYAZs+eTffu3RsttV51mEwmvv76a3r27Fln5Q5nKUQHKsYpU6bw1ltv1XkDU1RUFEFBQaxZs4bc3NwLskNVmmU0GsenGlRpeFTlXkMOHDhAYmIiN998c5NUSk0NjUbDuHHjGDt2LIcPH2blypVotVpGjhzJgAEDmqzycHV1ZdGiRQ0eKvlSeHl5sWXLFkaOHInRaOT666+v1flarfaCbE7nU1hYiKenZ5P9blRqh6rca0i/fv3o2rVrk3SZLCoqwmQy4efn1+R+mCUlJfz000/ccccdBAUFUVxcex/vhkSj0Tht92dd+fPPP/npp5/46aefOH36NK+++qpTrsO8vDxat27d5K4hlbqhrpTUEB8fH8LCwprk4tK8efOYNWsWpaWll6ybnp5OXFyc0wJPnU9xcTF6vZ6AgAD0ej2+vr6XnfKwWq3ExcWRkZFRp/NTUlLQ6/X06dOHa6+9lqFDh5Kenu5gKW3KvTFyAKg4h6anqVoAlS6EBQUFDdLfkCFDuPrqq2vkshYXF8e2bdswm80NIJnNH9tsNpOTk4PZbMZoNDaZOPsNhdlsZtu2bcTFxdW5jbVr13L69GmOHz9epRujIzAajfj5+TmlbZWGRzXLOIGysjJWr15N586dGT58uNP7q02iiwEDBtCjRw/74qazcXNzY+rUqSxatAghBGPGjKF///4N0ndTwWAwMGXKFNzc3Op0/ujRo/n++++5+eabkVLSvXt3Bg0a5GApbTN3Vbm3HFTl7gRcXFwYN25crbIhSSkxm83odLoam37OTryt1WprZO7w8PDAw8OjxnLVFyEEMTEx1SYgaWyklJSVlSGEwMXFxeFmI41GU6+wub6+vvz73/9m5cqVKIrCnXfe6ZT9DapZpmWhmmWcQKULYW1mQdnZ2SxevJjTp0/X+BwpJWvWrHHaY7qjqGqHZ1NCURQeeughnn766cYWpUpWrlzJoEGDiI2N5cCBA6xcudLhpq1KN8jmthdD5eKoM/cGRlEUcnJycHd3P2cG7eLiQlhYWK029QghCA4ObjATS0ul8umirmYTZ2M2mykuLubbb78FbLbxwYMH061bN4f1odfreeihhxzWnkrjoyr3Bqa0tJQVK1bQtWtXhg4dai/39vbmyiuvrFVbQohqc7Kq1AyNRsODDz7Y2GJclCFDhvD666/zn//8ByklAQEBNfKMqg1arZaAgIAm91SlUndU5d7AuLi4MGzYMKekLpNS8ueff1JeXs7o0aObpNumSu1p3bo1N998MyNHjuSvv/6iffv2dQoJoXJ5cUnlLoRwBbYALhX1f5BSviCEaAcsAQKAvcAtUkqTEMIF+AroC+QAM6WUCU6Sv9mh0+no0qWL09r//PPPyc/PZ+TIkapyb0GEhYURFhZGdHQ0Li4uTXIznUrToiYz93JgtJSySAihB/4UQqwBHgXeklIuEUJ8BNwBfFjxv1FKGSWEuAF4DZjpJPlVzuPf//43Vqu12caoV6mephZoTaXpcsmpnbRRVPFRX/GSwGjgh4ryL4GpFe+nVHym4vgYoRryGgQhBK1bt6Zt27qFlb1ckFJSUlJCeXl5Y4uiouI0avTcLoTQCiH2A5nAeuAUkCeltFRUSQHCK96HA8kAFcfzsZluzm/zLiHEHiHEnqysrHoNQkWlNphMJu68805eeeUVp7SfnZ1NQkICiqI4pf26cn56QZWWTY2Uu5TSKqXsBUQAA4Do+nYspVwopewnpewXFBRU3+ZUVGqMRqOhZ8+edO7c2SntHzhwgI0bN2IymZzSfl1QFIWPP/6YHj16MGDAAA4cONDYIqk4mVqtuEkp84DNwCDAVwhRabOPAFIr3qcCrQEqjvtgW1hVqQWKolBcXIzFYrl05WqwWCxkZGRQUlJyybpWq5WMjIyLRm6UUpKdnV1l2j1FUcjKyrogns7F+jeZTKSnp1+gAEtKSsjIyKhy3FJKsrKyqk37ZzabSU9Pp6ys7KJ19Ho9Tz31FDfffPM58hcXFzskoFq/fv2YNGlSk9p/YLVamTdvHtdddx2BgYF8/PHHjS2SipOpibdMEGCWUuYJIdyAcdgWSTcD12HzmLkV+LnilF8qPm+vOL5Jqs+AtcZoNLJ8+XIGDhxoT4pcFxITE5k2bRr33nsv9957b7V109PTufrqq7npppt44oknLjheWlrKjBkziImJ4d133z3nWElJCddddx19+/Zl/vz59vKEhASmT5/OAw88wF133WUv37p1Kw899BALFiw4JynGokWL+Oijj1ixYsUFqeSKi4u5/vrr6dWrF2+//XaVYzh8+DC33HILL7744gUJoKsjLS2NX3/9ldGjRxMVFVXj86rCx8enVqEnGpKhQ4eSmZlpNxnl5+fzwQcfcPz4cdzd3VmzZk29+7BYLA7Lu2q1Wh2WPMSRcjmqrZycHMLDwy9dsQ6IS+ldIUQPbAukWmwz/aVSyn8LIdpjU+z+wN/ALClleYXr5NdAbyAXuEFKGV9dH/369ZN79uyp92BaElarleLiYgwGQ70SR5hMJjIzM/H09Lxk3JDKWba7u3uVoRMURSE9PR29Xs/5prTKYwaD4Rwf/sr+vby8zlF4JSUl5Obm4u/vf86W97y8PIqKiqrceXuxPs6mrKyM7OxsfHx88PLyqna8Z2M2myktLcXV1bVJzbgdhdlspmPHjvzf//0fO3fupH379nz44YdYLBbS09NZuXIlwcHBDBw4sN59zZkzh5deeskhCvnNN9/k1ltvrfe+EJPJxLx585gzZ069ZQLHjfH06dPs3r2bRx99tE7nCyH2Sin7VXXskrceKeVBbIr6/PJ4bPb388vLgNqliVG5AK1W65CNKgaDgYiIiBrV1el01c4iqktkcbFjF+vf3d29yjgmvr6+F70J1SSRhqura43HezZ6vb5F+45rtVqeffZZ3n//fdzc3OxPcTqdjoiICAICAggKCqrT3+58PD09CQ8Pd8g+C29vb1q1akVwcHC92ikvL8fT09Mh4wPHjdHRO43PRt2hqqJyGaDRaJg9ezazZ8+u8njfvn0dFjRs5syZDnPFnTx5cq2ewC6GTqerdWrC6nDUGIODgxk7dqwDJLqQS5plGgLVLKOi0jhIKdm1axd79uwhMDCQGTNm1ElprV+/nuPHjwM2u/5ff/0F2HbWTps2rdYyLV68GCklbdu2tXv2jB07ttYeTqmpqfz0008XlPfo0YNhw4bVSqb169dz4sQJe5kQAiklM2fOrLHZaPny5eTl5XHLLbewbNky8vPz0Wq1DBgwgG3btgHQp0+fGsfrr84sc4Hva2O8+vbtK1VUVBqeM2fOyIiICDl48GAZEhIif/jhhzq1M3fuXDl37lw5YsQIGRYWJj08POTDDz8shw8fXuu2EhISZEREhGzVqpW87rrr5OTJk2VMTIz86quvatWOoijy448/lmFhYXLu3Lmya9euMjo6Wk6bNk0++OCDtWrr77//lj4+PnLMmDHS399fAvLBBx+U3t7ecv/+/TVu59Zbb5UBAQEyPz9fduzYUT711FPS09NT/ve//5Xe3t5yyJAhctasWbKkpKRG7QF75EX0qhp8REXlMqYy9eH3339Pp06dyM3NrVM7zz33HPfddx8eHh6UlZUxa9YsevXqVevFaavVyueff05xcTFnzpzhl19+4aGHHqpzOOb33nuPoqIijh8/jqenJ/fffz/t27evta08ODiYDh06sHHjRnJzc2ndujW33nprrcN8PPDAA/Ynoz59+jB16lT72lrXrl354Ycf+Omnnzhz5kyt2q0KVbmrqKhgMpnqtaM2Ly+Pm266iaSkJL744gsyMjJo27ZtrTM7GY1GPvzwQ3soayFEvRSdVqvl+uuvJywsjF27dpGamkqvXr1q7caYmJjIiRMnePzxx/Hx8bF7ZdWHgoICpJRER9v2hCqKgslkqnFWtUuhLqiqqFzGuLu706FDB4YMGYKiKLRv375O7Tz77LOsX7+eF198kVOnTrF27Vrc3d3JzMysVTt+fn78/fffbN68mdjYWMaPH8+nn35a5yeKOXPm8MQTT9C9e3cAlixZQrt27eyfa4q/vz8BAQEsXryYoqIirFYrX3zxRa13If/999+Ul5ezYsUK9u/fz8cff8yuXbuYMGEC+/fv54orrmDWrFlERkbWqt2qUBdUVVQucxISEkhMTMTT05M+ffrUadYYFxdXpSL38fGhV69etW4vNzeXkydPEhAQQEpKCgDR0dGEhITUqp28vLwqQy2Eh4fXerPaxcbYt2/fGkfr/Pvvvy/Yxa3VamnTpg3x8bbtQK1bt67xTba6BVVVuauoqKg0U6pT7qrNXUVFRaUFotrcVVRUmjz5+fns3bv3nLKOHTvSunXrRpKo6aMqdxUVlSaP1WolLy+PFStWEBcXxx133GGPVXS+l4+fnx9GoxGwhaOorZ2+paAqdxUVlSaPv78/06dP58SJE+Tl5bFy5Ury8vJ45513KCgowGKxMHToUA4dOsSAAQPYunUrgwYNQlEU1q9f39jiNwqqzV1FRaXZYbFYsFqtmM1m3nzzTdq1a8ett96Ku7s7q1evplOnTkRGRjJ06NDGFrXRUJW7iopKs2bgwIFcddVVXHvttQC0atUKk8lE586d6+wf3xJQzTIqKirNhiuvvJKePXtitVoJCwujdevWREZGcv311+Pi4sKrr75Kjx492L59O1JKp0VcbA6ofu4qKioqzRTVz11FRUXlMkNV7ioqKiotEFW5q6ioqLRAVOWuoqKi0gK5pHIXQrgKIXYJIQ4IIY4IIV6qKF8khDgthNhf8epVUS6EEO8KIU4KIQ4KIfo4eQwqKioqKudRE1fIcmC0lLJICKEH/hRCrKk49oSU8ofz6k8EOla8BgIfVvyvoqKiotJAXHLmXpGqr6jio77iVZ3/5BSgMtnhDsBXCBFWf1FVVFRUVGpKjWzuQgitEGI/kAmsl1LurDj0SoXp5S0hhEtFWTiQfNbpKRVl57d5lxBijxBiT1ZWVt1HoKKioqJyATVS7lJKq5SyFxABDBBCxADPANFAf8AfeKo2HUspF0op+0kp+wUFBdVOahUVFRWVaqmVt4yUMg/YDEyQUqZVmF7KgS+AARXVUoGzgyxHVJSpqKioqDQQNfGWCRJC+Fa8dwPGAUcr7ejClnBxKnC44pRfgP+r8Jq5AsiXUqY5QXYVFRUVlYtQE2+ZMOBLIYQW281gqZRylRBikxAiCBDAfuCeivqrgUnASaAE+IfDpVZRUVFRqZZLKncp5UGgdxXloy9SXwL31180FRUVFZW60iSiQgohCoFjjS1HAxEIZDe2EA2AOs6WhTrOpkkbKWWVHilNJZ77sYuFrWxpCCH2XA5jVcfZslDH2fxQY8uoqKiotEBU5a6ioqLSAmkqyn1hYwvQgFwuY1XH2bJQx9nMaBILqioqKioqjqWpzNxVVFRUVBxIoyt3IcQEIcSxivjvTze2PPVBCPG5ECJTCHH4rDJ/IcR6IcSJiv/9Ksqbbdx7IURrIcRmIURsRYz/hyrKW9RYq8ll0E4IsbNiPN8LIQwV5S4Vn09WHG/bqAOoJRUBAv8WQqyq+NziximESBBCHKrIQbGnoqxFXbeVNKpyr9j1+j62GPBdgRuFEF0bU6Z6sgiYcF7Z08BGKWVHYGPFZzg37v1d2OLeNxcswGNSyq7AFcD9Fd9bSxtrZS6DnkAvYEJFSI3XgLeklFGAEbijov4dgLGi/K2Kes2Jh4C4sz631HGOklL2OsvlsaVdtzaklI32AgYBa8/6/AzwTGPK5IAxtQUOn/X5GBBW8T4Mm08/wMfAjVXVa24v4GdsMYda7FgBd2AftsQz2YCuotx+DQNrgUEV73UV9URjy17D8UVgU2yjgVXYwoq0xHEmAIHnlbXI67axzTI1iv3ezAmR/wuclg6EVLxvEWOveCTvDeykBY71/FwGwCkgT0ppqahy9ljs46w4ng8ENKjAdedt4ElAqfgcQMscpwTWCSH2CiHuqihrcdctNJ0dqpcFUkophGgx7klCCE9gOfCwlLLAFiDURksZq5TSCvSqiIy6AlsOgxaFEOJqIFNKuVcIMbKRxXE2Q6WUqUKIYGC9EOLo2QdbynULjb+gejnEfs84KzxyGLYZIDTzsQtbPt3lwLdSyh8rilvkWOGcXAaDsKWOrJwYnT0W+zgrjvsAOQ0raZ0YAlwjhEgAlmAzzbxDyxsnUsrUiv8zsd2sB9BCr9vGVu67gY4Vq/IG4AZs8eBbEr8At1a8vxWbfbqyvFnGvRe2KfpnQJyUcv5Zh1rUWEXVuQzisCn56yqqnT/OyvFfB2ySFcbapoyU8hkpZYSUsi223+AmKeXNtLBxCiE8hBBele+B8djyULSo69ZOYxv9scV+P47NlvlcY8tTz7F8B6QBZmz2uTuw2SI3AieADYB/RV2BzVPoFHAI6NfY8tdinEOx2S4PYovlv7/ie2xRYwV6AH9XjPMwMKeivD2wC1vOgmWAS0W5a8XnkxXH2zf2GOow5pHAqpY4zorxHKh4HanUNy3tuq18qTtUVVRUVFogjW2WUVFRUVFxAqpyV1FRUWmBqMpdRUVFpQWiKncVFRWVFoiq3FVUVFRaIKpyV1FRUWmBqMpdRUVFpQWiKncVFRWVFsj/A1TIVjoNFPyhAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -180,16 +194,15 @@ " plt.imshow(env.render(mode='rgb_array'))\n", " display.display(plt.gcf())\n", " display.clear_output(wait=True)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "The rendered environment shows the three cells as cell towers with circles indicating their range.\n", "The five moving users are shown as small circles, where the number indicates the user ID and\n", @@ -203,29 +216,52 @@ "\n", "\n", "\n", - "### Step 2: Configure `mobile-env`\n", + "### Step 2: Configure `mobile-env` (Optional)\n", "\n", "#### Predefined Scenarios\n", "\n", - "We can choose between one of the [three predefined scenarios](https://mobile-env.readthedocs.io/en/latest/components.html#scenarios): Small, medium, and large\n", + "Configuration is optional.\n", + "We can also choose between one of the [three predefined scenarios](https://mobile-env.readthedocs.io/en/latest/components.html#scenarios) (small, medium, and large) without having to configure anything.\n", "\n", "By default, these are available for either a single, centralized agent (e.g., `\"mobile-small-central-v0\"`)\n", "or multiple agents (e.g., `\"mobile-small-ma-v0\"`), which affects the observations, actions, and reward." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "code", "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "{'width': 200,\n 'height': 200,\n 'EP_MAX_TIME': 100,\n 'seed': 0,\n 'reset_rng_episode': False,\n 'arrival': mobile_env.core.arrival.NoDeparture,\n 'channel': mobile_env.core.channels.OkumuraHata,\n 'scheduler': mobile_env.core.schedules.ResourceFair,\n 'movement': mobile_env.core.movement.RandomWaypointMovement,\n 'utility': mobile_env.core.utilities.BoundedLogUtility,\n 'handler': mobile_env.handlers.central.MComCentralHandler,\n 'bs': {'bw': 9000000.0, 'freq': 2500, 'tx': 30, 'height': 50},\n 'ue': {'velocity': 1.5, 'snr_tr': 2e-08, 'noise': 1e-09, 'height': 1.5},\n 'arrival_params': {'ep_time': 100, 'reset_rng_episode': False},\n 'channel_params': {},\n 'scheduler_params': {},\n 'movement_params': {'width': 200, 'height': 200, 'reset_rng_episode': False},\n 'utility_params': {'lower': -20, 'upper': 20, 'coeffs': (10, 0, 10)}}" + "text/plain": [ + "{'width': 200,\n", + " 'height': 200,\n", + " 'EP_MAX_TIME': 100,\n", + " 'seed': 0,\n", + " 'reset_rng_episode': False,\n", + " 'arrival': mobile_env.core.arrival.NoDeparture,\n", + " 'channel': mobile_env.core.channels.OkumuraHata,\n", + " 'scheduler': mobile_env.core.schedules.ResourceFair,\n", + " 'movement': mobile_env.core.movement.RandomWaypointMovement,\n", + " 'utility': mobile_env.core.utilities.BoundedLogUtility,\n", + " 'handler': mobile_env.handlers.central.MComCentralHandler,\n", + " 'bs': {'bw': 9000000.0, 'freq': 2500, 'tx': 30, 'height': 50},\n", + " 'ue': {'velocity': 1.5, 'snr_tr': 2e-08, 'noise': 1e-09, 'height': 1.5},\n", + " 'arrival_params': {'ep_time': 100, 'reset_rng_episode': False},\n", + " 'channel_params': {},\n", + " 'scheduler_params': {},\n", + " 'movement_params': {'width': 200, 'height': 200, 'reset_rng_episode': False},\n", + " 'utility_params': {'lower': -20, 'upper': 20, 'coeffs': (10, 0, 10)}}" + ] }, "execution_count": 4, "metadata": {}, @@ -238,22 +274,27 @@ "\n", "# easy access to the default configuration\n", "MComSmall.default_config()" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 11, + }, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -272,22 +313,27 @@ "\n", "# plot small env from earlier\n", "plot_env(\"mobile-small-central-v0\")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 12, + }, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n", 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -322,16 +373,15 @@ ], "source": [ "plot_env(\"mobile-large-central-v0\")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "#### Custom Scenario\n", "\n", @@ -341,22 +391,27 @@ "\n", "We also configure the environment to simulate each episode with identical user positions and movement. By default, users appear and move randomly in each episode.\n", "We also set the episode length to 10 (instead of default 100 steps)." - ], + ] + }, + { + "cell_type": "code", + "execution_count": 8, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 16, + }, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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5RwjhC/wMhAAJlHWFrLAKvqotoyiKUnUV1ZapdmOblDIO6HSL5deAQdXdr6IoilJzaoSqhV1/JVTZ7mV/vHqy1cS61Ym9JtspimI9tu+MWcfk5ubyyy+/cOnSpSptt2vXLqKjo60UVeUUFRWxbt26CiccvpXDhw+zZcsWhy3cpCh1kTpzr0D5yLPyLk6VYTQaKS4urnKiKy0trdGNrT+SUlJcXIwQAmdn50qdUZdvU9XKgCUlJRQXF1c3VOUPEhIS+OKLL2p1UmUPDw/0er3ddOOrCldXV3Q6nU0mobaEUaNGmQdMWZKarKMCxcXF/Pzzz4SEhNCvX79KbSOlREqJEKJKzTLXzzZjiaYNg8HAqlWrcHd3Z9iwYZVO7lWNHTB/KFkq9rud0Wg0D3OvLUKIWi8BYEmOHL9WqzV3Va0qq9xQvRtotVpatWpVpdFm1Ulw1dmmvCZGYGDgLd8YQghCQ0NxcXGxahxAlYdaSylJTU3F2dmZBg0aVPn16jqNRmMXw9cVx6beQRXQ6XR0796dli1b2jqUmxw7doxff/31tpfRGo2Gzp07065dO7s7mzYYDGzdupU9e/bYOhRFqbPuqjP38kpwOp3O4c6MymMvv4Tr0KEDwcHBuLq62jq0KtNqtfTv37/S9zHuZqWlpfz9739nxowZBAYG8vrrr+Pi4sKTTz5J27Ztzet9/fXXeHl5MW7cuFvup6ioyDxc3lqMRiPbt2/nxIkTdOnShV69eqHVaklJSWHlypVIKRkzZgyurq6sXLkSIQRjxozBz8/Ppj3EEhISiIqKwsnJiTFjxuDv748Qgt9++40TJ06QmZnJsGHD0Ov1HDp0iKysLPr27UtoaChLliyhY8eODB8+3CbxV+SuSu4ZGRmsW7eOe++9l1atWtk6nCrJz89n9erVdOzYkYiICHx8fPDx8bF1WNUihCA4ONjWYTgEg8HADz/8wMiRI/Hy8uKbb75h2rRpFBQUMH/+fKAsQX333Xd4eXmRkpJyQ9tznz59iI6OZufOnYSHh/PYY48RFxfHqVOneOihh/D19bVYrPv27ePHH3/khRdeYPbs2Tz//PMMGTKEL774goSEBIQQXLx4kcDAQJycnGjZsiXvvfce//d//2fVD52K5OTk8NJLLzFo0CDi4+P57bffmDRpEgD33nsvnTp1YtasWQwfPpzIyEg6dOjA888/T48ePfD09MTPz48tW7ao5G5rzs7ONGrUCA8PD1uHUmU6nY6goCC8vLxsHYpiAx9//DE+Pj4UFRUxb948mjVrxquvvsqQIUPQarUEBATQsGFDjEYjr7zyCl27dmXv3r188MEHvPTSS7zxxhts3ryZX3/9FT8/P1xdXXnyySctGuOFCxeIiorCz8+PjRs30qRJE4YMGcKxY8dYsWIFAG+99Rbnz59nxYoV1K9fHykl7777rs2SO5RNMThv3jyysrKYOXMmRqMRrVaLq6sr8+bNY9++fXh7e+Pi4sI333zDpk2b+Mtf/oKXlxelpaUcPnzYZrFX5K5K7p6engwePNjWYVSLq6sr9913X7W3NxgMFBUVUa9ePYdrkjIajRQWFuLq6mrTJGBLzz//PK1bt2bp0qWUlpbi6+vLAw88wI4dOxg3bhxFRUX4+fmZC2AtWLCAPn36ANCwYUNmz55NZGQkY8aMQavVsmnTJos36U2ZMgWtVsvZs2fRaDQ3FNB68MEHqV+/PitWrGDu3Lncc889nDhxgl9+sU3pqfIPldjYWH7//Xd++OEHLl68yAcffMArr7yCu7s7paWlnD9/nvvvv59WrVqh1+u5cOEC3bp1q9VaONV1VyX3u1l8fDzbtm1j5MiRBAUF2TqcKklJSSEqKor777+f5s2b2zqcWlc+TqF8zIKzszNZWVkUFxfTr18/Tp8+TatWrdi/fz8pKSkkJibSvXt39Ho9Wq3WfG9j8ODB9OzZk+PHj9OhQweLx2k0Gvn555+JjY3Fx8eH+++/n++//x4nJydSU1PZuXMnTZo0Ye3atRw7doz4+HhGjBhhs5ON2bNnk5mZSXx8PHPnzuXatWsMGzaMw4cPk5SURJcuXVi5ciULFiwA4OrVq3z55Zf85z//AWDTpk1s2rSJ5ORkPvnkE55++mm7OvlQ/dzvEhkZGZw8eZLOnTvj6elp63CqJCcnh6NHj9KxY0eHvc9QXVJKUlJSaNCgATqdztym7uXlZa6B7uzsjE6no6Cg4Kbtyyfs8Pf3Z8OGDbz66qtMmTKFV1991eJJtTxWg8GAEMJcF12j0VBYWAiAk5MTzs7O5OfnA+Dr62uuA28r2dnZ5ikHfXx8zAMRvby8SEtLM3c3NhgMpKSkEBAQgE6nIzMz03wcWq2WwMDAWr8xXFE/d5XcFeUuYDQaOXDgAEVFRYSGhtK4cWNbh6RYQJ0exJSfn8+pU6do1aqV3RTJr6w/dm9UFGvRaDR0797d1mEotcix7qzdQk5ODocOHcIRp+rLyclh6dKlxMTE2DoURVHqGIc/c/f392fKlCl2M/tJVTg5OREUFET9+vVtHYpiR3Jycjh+/DglJSW2DkWpBaGhoYSEhFh8vw6f3LVarcP2/XZzc3PYrpmK9ej1evOE0krdZ61KnA6f3BXLyszMJDExkVatWjlceYDc3Fzi4+MJDQ21eQ+MmmjQoAEPPPCArcNQHJzDt7krlhUfH090dLRD1sZOTk4mOjqazMxMW4eiKDanztytwGAwkJiYSP369R2uB0+bNm3QaDRVnrDDHjRr1owJEyaoMsKKQiXO3IUQXwkh0oQQJ69b1kAIsUUIEWv638e0XAgh/iuEOC+EOC6EsP8xulaQlZXFQw89ZC7sdCsGg4G8vDy7m5pOCMGrr77KnDlzbruO0WgkLy/P7j4AnJ2d8ff3R6dT5yyKUplmmW+AoX9Y9iqwTUoZBmwzPQYYBoSZvmYCCywTpv2QUnL8+HF2795922nx3N3dmTJlCi1atLhtj4cjR47wwAMPsH///gpf6/pZmiwhNjaW7du33zYxOzk58fjjjzNmzJjb7uPChQuMHDmSjRs33nYda8SuKErl3TG5Syl3Ahl/WDwa+Nb0/bfAmOuWfyfL7AW8hRA1KmQipeTq1at2dZb46aef8vbbb982JldXVwYMGEBGRgZZWVm3XKdBgwZ07dq1wpKra9asYdasWeZh5pbw/fff8/rrr5uHW/+RVqvloYceqnBOR09PT7p27VphjZrff/+dJ554gqSkpBrHrChK1VX3+jVASpls+j4FCDB93xi4fN16V0zLkqmBDRs2MGjQIBo1alST3VSKlJKsrCy0Wu1t+5+/8sor5omzb6dly5Z4e3vfthZKixYt+PDDDyuMpaioiJycnEpPnC2lJDc3F6PRiJeX1y3rXMyYMYMxY8bUqL5MYGAgH3zwQYXrFBcXk5OTY3fNTopyt6hUbRkhRDMgSkrZwfQ4S0rpfd3zmVJKHyFEFPCulHKXafk24BUp5U2FY4QQMylruiEkJKRrQkLCLV9bSsnZs2dp1qxZrcw6VFJSwpQpU2jcuDHz5s2z6RR1RqPRXFu6shNcP/300+Tk5PDtt9/atO25qrErlmcwGMxXl1qtFqPRaG4mK680qTg2a9SWSRVCBEkpk03NLmmm5YlAk+vWCzYtu4mUchGwCMoKh93uhYQQtGnTppphVp1Wq2XYsGF20eOioomS9Xo9R44cITAwkCZN/v+PfODAgRQUFFitjKqUkmvXrmEwGMzTkVU1dijrk378+HHCw8NrtUqlwWAgOTkZLy8vh6uOWVXLly/n3Xffxc3NjdDQUI4ePUrTpk25cOECe/bscdjBf0rlVDcDrAWmmr6fCvxy3fLHTL1megDZ1zXfOAStVsu0adMYO3asXZ/Z5OTk8Oc//5mvv/7avEwIwYMPPsjUqVOtWiN7165d/Pbbb5VuLrqVgwcPMmPGjFqfxSY/P5+oqChOnjx555UdXL9+/fjmm2947rnnWLp0KQkJCYwdO5akpCR1o/sucMczdyHEUqA/0FAIcQX4B/Au8LMQYhqQAEw0rb4eGA6cBwqAJ6wQc43p9XqKiopwc3NzuFmJynl6evLee+9ZpSbFnfTs2RO9Xl+jn13nzp2ZN28eERERlgusEspLPlhy7lB7FRgYSHp6Om+88QYPPPAAXl5eDBs2jJ9//tm8TlZWFu+//z4lJSX4+/s75ITrjqykpISOHTtapQzJXVnPPSoqirlz57J48WJCQ0Nr7XUtITMzk71793Lvvfeqy2qlQqdPn2bMmDGEh4ezaNEiunXrxuzZs3nzzTc5d+4c3t7eGI1GcnNz2b59O35+fnTq1MnWYd9VLly4wLZt23jhhReqtX2drudeHUFBQXTt2tUhJ8o+dOgQL774IosWLaJ37962DkexY/Hx8QQHB+Pu7s4//vEPWrZsSXR0NN27dzffbNdoNHh5eeHu7k69evUc8m/Ckbm5uVlt33dlcu/atStdu3at0T6klObukLVZYKt79+589dVXFc6BaTQaycjIwM3NzapvHmuQUpKZmYmTk1Odv+FpbcOHD2f48OG2DkOxEcdscLYDxcXFLF++vMIRptbg6elJjx49KjzDysjIYNy4cXz++ee1GJllFBUV8fjjj/P222/bOhRFcWh35Zm7Jeh0Olq3bk1AQMCdV65lbm5ujB49msjIWzbF2TWdTsfw4cMJDg62dSiK4tDqbHKXUpKamopGo8HPz8/i3Rp1Oh09evSw6D4txc3NjRdffNHWYVSLk5MTf/rTn2wdhqI4vDrbLGM0Gnn22Wd55ZVXbB1KtWRnZ/PZZ59x5swZW4eiKIoDsvszdykl8fHxNGrUCBcXl0pvJ4RgypQpVdrGnqSnp7N48WLq169fqyN0a+KP3WrteRDYrVwfv73Fnp6eTnp6Om3btgXsLz7F/th9coeyEZFVLRym0WgqLFtr75o1a8batWstOtimqKiI8+fP07RpU6v1RNm9ezd6vZ6+fftafN8ZGRkkJyfTqlUrnJycLL7/I0eOkJ6ezqBBg+yuJvylS5cYPXo0DRo04PXXX+e+++6jYcOGFW6TlJTEmTNnaN++PYWFhcTFxQHQqlUrdU/jLuAQzTKDBg3Cz8/P1mHUKp1OR0hICO7u7hbb59mzZ5k0aRJbtmyx2D7/qKSkhJKSEqsMb1+2bBlTpkwhOdk6FS1KS0spLi62y6H5wcHBvPjii7Rs2ZLJkyczatQo1qxZU+E20dHRDB06lO3bt/PDDz/w2muv8dxzz/HLL79UuJ1SN9jX6cktCCFqpdTv3aBZs2a88cYbVr0R3K9fP8A6zQZDhgzB39//jmes1XXPPfcA9tnkkZiYyN/+9je6devGP/7xDzIzM7l48WKF20yYMIF3330XKLuSnTZtGitWrLhhHSklJSUllJaWWi12xTbsPrkrluPl5cVDDz1ktf0LIayaGFu2bEnLli2ttn97rjMkpeStt95i+vTpHDt2jKKiIu69994Kt9FoNObfhxACf39/Ro4cecPvKCsri3feeYcrV65Uewi8Yp/qTHLftWsXq1ev5rXXXrPamZ21SClJSyurmlxRGV3l7rRt2zZefvll8vLyWLduHZmZmbzzzjt3vG+SkZFBYWEhCQkJJCYmsnXrVnJzc5k6dap5HR8fH95//322bNmCVqu19qEotajOJPfk5GSOHz9OYWGhVfav1+sxGo04OTlZPPlKKZk9ezZ6vZ4lS5ao5K7cICgoiPvuu48LFy6YB6ZVpgdVeno6zz//PADe3t507NgRQNUkukvUmeQ+duxYHnjgAauVLN23bx+XL19m3LhxFq8lI4TgoYceQkqpErtyk5iYGJo2bcqnn37K1q1bgbKSyXeqaNqmTRuH6UarWF6dSe46nc6q3dcaNGhAaWmpVdplhRCMGDHC4vu9k5KSEs6dO0dwcDDe3t61/vo1lZ2dzeXLlwkLC3PY8QyV0bFjR4xGI//5z3/My1q1amXDiBRHYL93kOxM27Zt6d+/v931f66JS5cuMXnyZFavXm3rUKrl119/ZfLkyeb+23WVlJKCggIMBoP5yx67ayr2xe4zlZSSM2fO0KxZM+rVq2frcOqUoKAg3njjDbp162brUKqlT58+6HS6Oj8g58SJExQWFvLmm2+ak3rbtm1p0aKFjSNT7JndJ3com2/Ty8tLJXcLc3d3Z+LEiXde0U41adLkhsnB66rhw4dz7do12rdvT/PmzUlJSbGLCdwV++YQzTLDhg3D39//hmWbN2/m5ZdfJicnx0ZRVZ/BYOD999/nq6++qrOX10ajkf/9738cOXLEYY/x3LlzbNu2jZKSEpvGsWfPHsaMGcP06dN5+OGHefLJJzl9+rRNY1Lsn92fuQshbtlv/fLly5w4cYLi4mIbRFUzRqORmJgYu6wFbylSSq5everQ07ZlZWWRmpqK0Wi0aRxBQUHce++9JCcnExkZiZOTk7lbY1WUlJSQl5eHEAIvLy+7HrSlWICUssIv4CsgDTh53bJ/AonAUdPX8Oueew04D5wFhtxp/1JKunbtKquqpKRE5ufnS6PRWOVtbc1oNMrCwkJZVFTkkPFXhtFolKWlpbK0tNRhj1Gv18uSkhKbx79ixQrp4eEh3dzcpJeXl+zatau8ePFilfZhMBjkK6+8Ir28vKS/v7/csGHDDc9v3rxZHjp0yIJRK5Vx7tw5OXfu3GpvDxyUt8mrlfno/gYYeovlH0kpI0xf6wGEEO2ASUB70zbzhRBWGfbm5OSEm5ub3fYLl6Y5Vi9fvsyFCxdISUlBr9cDZVcjrq6uuLi42GX8UkqKioq4cuUKFy5cICkpqcrFwIQQ5u6p9niMlaHVaq0yaK2qBg0axO7du9m7dy87d+6kQYMGnDx5skr7MBgMLFu2jLlz5zJgwABVPOwucMdmGSnlTiFEs0rubzSwTEpZDFwUQpwHugF7qh+iY5FSkp2dzebNm9m8eTNFRUW4uLhQUFBAcHAwY8eOJTIyslYn1a6M5ORkFi9ezMiRI0lMTOTXX38lJycHV1dXCgsLadiwIaNGjaJXr152eWN7z549bN++nT//+c/Ur1/f1uFYlJOTk/mYpJQ1aupq1qwZvr6+5qamzMxM5syZw5UrVxxq9i4pJXq9nvz8fAoLCzEajWi1WjQazQ3/X/+9Tqe7q5qiatLm/mchxGPAQeBFKWUm0BjYe906V0zLHJKUEqPReEMBpjutf+XKFebMmYOHhwePPfYYLi4uFBYWUr9+feLj41m4cCG7d+/mmWeesaskmZaWxrJly9i4cSOdO3dm/PjxeHl5kZeXh6enp/n5nTt38uKLL+Ll5WXrkG9w/Phxfv31V5544ok6l9w3bNjA448/bn4cEhJSrUFMWq2Wb7/9lpiYGLp37w6UlSV455132LZtm13Wlim/WjQajRiNRnJzczl37hynT58mKSnJPKq7PGnf6uqyfJmXlxeDBg0iLCzMoa8oK6u6yX0B8G9Amv6fCzxZlR0IIWYCM6HszWqPTp48SUxMDCNGjMDNze2O62dnZ/PWW2/RqVMngoOD+fbbb9FqtXh5eZGSkkJAQADTpk3jxx9/ZPHixTz11FNWmXSiOpo1a0aPHj1o0aIFkZGRrFixgtLSUnx9fUlLS8PT05NHHnmE9evXM2/ePF599VW7GhU6depUHnzwQYccaXsnw4cPv2G6RRcXlyrPb6DT6ViwYAFffPEFbdu25fXXXwfKms+cnZ3tcnBefn4+p06dIiEhgdTUVK5evUppaSmhoaG0bduWIUOG4OHhQb169RBCmD8ADAbDLb9PTEwkKiqKoqIiwsPD6d69O4GBgXU2yVfrNyqlTC3/XgixGIgyPUwEru94HGxadqt9LAIWAURGRt62MVdKSU5ODh4eHmi1WoqLi0lOTiYoKMjqycXZ2bnS7fpGo5Gff/6ZkJAQGjVqxIoVK3jqqafw8vKiuLgYV1dXTpw4weLFi5k6dSpff/01/fr1Izw83OZvLikl27Ztw9vbm27duvHNN9/w5JNPEhgYaI794sWLLFq0iKeffpoffviB3bt3079/f5vHXs7V1dVqdYVszc3NrVInFxURQjB48GAGDx5soaisw2g0kp6ezv79+9m3bx9NmzalRYsWtG/fHn9/f3x8fKp9hREQEECnTp24cuUKR44cYcGCBfj7+9OnTx9atGiBh4eH3byfLaFayV0IESSlLJ8OZyxQfndnLfCjEOJDoBEQBuyvaZDr1q1j4MCBNGrUiH379vHMM8/w2WefWWUqt+u1atWq0pe/hYWF7Nixgz/96U8sXLiQ119/nW3btrFnzx48PDzIyclh5syZODk58cILL/DEE0+wYcMGwsPDrXoMlWE0Gtm0aRNjx47lxx9/5C9/+QuxsbEsXrwYLy8vcnJymDhxIo888ghLly5lyJAhREVF0bdvX7u8lK/rTp06hcFgsIv3jiVIKTEYDKSmprJ+/XoSEhIIDw/nr3/9q3mwlqWSrlarJSQkhJCQEIYPH87x48fZsWMHy5cvp1OnTvTv3x9fX9860TZ/x+QuhFgK9AcaCiGuAP8A+gshIihrlokHZgFIKU8JIX4GTgN64BkppaGmQXbo0MF8uR0WFsbTTz9NWFhYTXd7R1V5Q6WmpiKE4Nq1awQGBpKVlcXRo0cZP348hw4dYsSIEXzzzTeMHz+e2NhYOnXqxLJly6wYfeVlZGSQnZ2Ns7MzQghcXFzYvHkzjzzyCNHR0TzwwAMsW7aMf//73yxbtgxvb2/S0tIwGAwqudeSP7YlCyEcuoro9W3pp0+fZsuWLWRnZzNgwACmTJli1Z5w5ft1dnama9euREREkJ2dzf/+9z/ee+89xo8fT48ePRz2Z1uuMr1lJt9i8ZcVrD8HmFOToK4nhCAiIsL8OCgoiKeeespSu7eYa9eu4enpSWZmJoGBgVy4cIGIiAi2bt3KxYsXadWqFS4uLgQGBtK0aVM8PDzIy8vDYDDYvL0zKysLJycn8vPzadCgAcnJybRs2ZI9e/Zw7Ngx/P39CQgIIDExEXd3d4QQFBcXo9fr7a7XT10VExNzwyxaDRs25Ouvv6ZZs2a2C6qaynuUHT9+nP/973/odDruv/9+WrVqZW4/ry3lXXZ9fX0ZOXIk99xzD/Pnz0ej0dCtWzeHTvD2dxfFQZX3LGnQoAExMTH06NGD7777jvvvv599+/bRsGFD9Ho9np6e5n7k7u7udnHm6+npSWlpKR4eHly9ehUpJefPn+ehhx6ioKCAVq1acezYMQIDAyksLERKWembcIWFhRgMBvOHgrUYjUbS0tJwdXW12E1Vo9FIfn4+Tk5ONm/PL59Raf78+bzyyiskJiayfPlyXnrpJZvGdSdSSkpLS8nNzSUzM5OYmBhOnjxJTk4OoaGhjBs3jrCwMLtoBhFCEBgYyDPPPMP8+fMRQnDPPfc4bIJXyd1CgoOD0Wq1+Pj4kJiYiL+/P6GhoRiNRgICAoiNjWXSpEmcOXOGxo0bExcXVytNS5Xh5+dHw4YNKS0tJS0tjWnTpjFlyhSysrLw9/cnKSmJ4cOHk5ycTL169cjNzSUoKKhSH0zr16/n6tWrPPnkk1btGZSbm8t9991Hz549Wbx4sUX2WVBQwNdff02bNm1sfiNSo9EgpeTEiROUlpbSs2dPm1/x3Up5+3lhYSHnzp3j2LFjJCYmIqXE1dWV1q1bM2nSJBo3bmzujmhPyVMIQUBAALNmzeKzzz7DycmJiIgIu4qxsuzv3eGgXFxc6NmzJ9HR0YwZM4Y5c+Ywe/ZsNm3axIcffsjKlSu5du0aq1at4i9/+QufffaZ3UxIrNFo6N+/P5s2bWLy5MkkJCRw//33k5aWxgcffMC3336Lp6cn8+fP589//jNLly7lwQcfrNTZVvmgGWufmTk7OzNq1CiLzjzk5OREmzZt7KKkcKNGjWjXrh2rV69m1qxZzJo1y+7GGkgpOXXqFOvWrSM7O5vmzZsTHh7OsGHD8PDwwM3NzS6uVO9ECEGjRo146qmnzGfwnTp1crgEr5K7hWi1WiZNmsSrr75K06ZNGTlypHlgjYuLC8uXL6dNmzY8+eSTrFmzhnvuuYcOHTrYxRtGCMHQoUPZu3cveXl5vPfee6SlpbF69Wo8PDz4+eefGTBgANOnTyc6OpqgoCB69epVqdi7du1aC0cA9erV45133rHoPl1cXGx+xl4uIyOD/Px8ioqK2LlzJ8OHD6/wQ6egoIBJkyaRnZ3Nm2++Sdu2bXnmmWfIyMhg0qRJzJw506KJVq/XEx0dzY4dO5gwYQKtW7e2i9IN1SWEIDg42Nz7TQhhF92Wq8L2DV13IKWkpKTkhsp8RqORvLw8c60We+Hl5cVrr73Gzp07ycjIICMjg+bNm9OzZ08uXLhAWFgYa9aswcvLi8mTJ9vVWYy7uzsvv/wyZ86cISYmBp1Oh6enJ927dycvLw9fX19+++03CgsLee655+xqANPdICwsjFmzZpGcnMzu3bvvWOraycmJ8ePHc/HiRdLT0/nqq6+Ij4+nf//+vPbaa+Tm5lokrvIaSsuXL2fPnj08++yzdOjQwdzzypEJIQgJCWHWrFn89NNPnD592qHKV9t9cgf45ZdfSE9PNz++dOkSo0aNsrviR+Vvhvfff58OHTqwZs0azp07x5kzZ0hJSWHt2rXMmDGDsLAwJk6cSHx8vK1DNhNC4Ofnx3/+8x/69u3LihUruHjxImfPniUpKYlVq1bx0EMP8fe//73ODe93BCdPnuTvf/87U6dOpXPnzrRs2fKG5wsKCli0aBHz589n/vz5fPPNN0ycOPGGctnNmjXjwQcfvGG73NxcPv/8czZs2FDl0sblAwwXLFhAQUGBuV+6oyf165X/TU+bNo3vvvuOc+fOOUyCd4jk7u/vf0NvBXd3d7p06ULjxtYtW1NQUEB6enqV3vRCCOrXr8+5c+fo3r07LVu2JDY2lscff5zU1FQ0Gg1Nmzala9euuLu7WzH6qhNC4ObmRnp6OsHBwXTr1o2zZ88yatQooOznYa+VLOuy1NRUkpKS6NixI3v27MHV1fWGkx0ou+fQq1cv+vTpQ58+fbj33ntvuoF99epV9u+/cUyhi4sLvXv3pl27dlX6vUopSUhI4KOPPqJdu3Y89thjtd6NsbYIIWjRogXTp0/n66+/5uzZsw6R4O0+uQsh6Nev3w03j/z8/Pjggw/o0aOHVV/72LFjrFq1ivz8/Cptl5GRwapVq7hy5QoFBQVERESwbds29Ho9P//8M126dOGDDz6wy8k6CgsL+emnn8jKyuLKlSt06dKFgwcPotfr+e677xxychRHt337dh544AGCg4Np3bo1ixYtomfPnjeso9PpaN++PR07dqRjx46Ehobyz3/+E4PBwNatWxk5ciR5eXn861//YtasWeZyBs7OznTo0IGmTZtWOjEbDAaOHDnC559/zujRoxk8eLDd1EiyFiEEoaGhPPnkk3z33XfExsbafYK3++RuS2FhYfTp06fK1Rujo6P5/fffKSgoYMSIEQQEBDBq1ChiY2NZvnw5KSkpVoq4+rKysvjxxx+Jiopi06ZNpKWlMX78eBo2bMiYMWOIi4sjKiqKuLg4W4d6S+fOnePnn3+msLDQ1qFYXIcOHXjqqadYu3Yt69ev55NPPuHs2bMVbuPs7Mw//vEPDhw4wIIFCwgPD2f//v3ExsbyzjvvVGvwWXk3xw0bNrBmzRr+8pe/EBERYRd91GuDEIKwsDAeffRRvvrqKy5evGjXCf7u+K1UU8OGDWnXrl2V+xO3bNmSxx9/nObNmxMTE0NISAhnz56lf//+TJw40S6nnouLi+Odd94hOTmZxx57jI4dO3L06FFatGhhHpT18MMP4+vra+tQb2njxo385z//4dq1a7YOxeI6dOjAvHnzOHfuHF999RXp6el3vJoUQuDk5ISzs7O510r54+reyC8oKODLL7/k4sWL/PWvf6VRo0Z1shmmIkII2rRpw6OPPsrChQvtOsELewgsMjJSHjx40NZhWFxSUhLz58/n+eefv+U8sPakqKiICxcu0LRpU9zd3Xnttdfw8vLCz8+Phx9+2K5qz99KRkYGqamp5lrdStVs2bIFX19funTpctNz5fMU/PDDDzRv3pzRo0fb/fvB2sr79P/4449MmzbtphvclRUbG8u6deuqPeZFCHFIShl5q+fUmbuVSCk5duwYxcXF7Nu3z24/3cu5urrSvn1781VFUVER06ZNIzk5+Y7d7uxBgwYNaNu2rUrsFlbevr5gwQLzlaetSzHYAyEE7du3Z/LkyXz11VckJCTY3d+4wyb3+Ph4tmzZQklJia1DuSWj0ci5c+cYMWIE58+fv6FPvtFoNBflsrc3RLmUlBS+++47cnNzq90+e/nyZVJSUuz2GO8kIyODuLg4DIYaFzZ1OFJKiouLWblyJevWrePpp5+mR48elZ6V7G4ghKBDhw5MnDiRzz//3FxmwV44bHJfsWIFL7/8st22sV69epU9e/awceNGdu/eTVZWlvk5vV7PnDlz+OSTT2wX4B0EBwfzwgsv4Ofnd1O3u8owGo3s3LmTAwcOWCG62nHy5Em2bdt21/UQklKSnp7O/Pnzyc3N5fnnnyc4OFgl9VsQQtCxY0cmTpzIggULuHz5sq1DMrP7a1gpJXv37qVDhw54enqalz/88MP069fPbtuyjx8/zsyZMxk4cCAbNmwgJibGPDWak5MT7777rl1f3ubm5vLBBx+g1+vx9/ev8vYajYb777/foZtJIiIiCA0Ntevfk6UZjUbOnDnD0qVL6du3LwMHDrSrkdT2SKPR0KlTJ4xGI4sXL2bmzJl28WHoEH958fHxNG/e/IbkHhQURFBQkA2jqtigQYPM3w8ZMuSG58ov52ytfAb58hniywkh+PTTT83fV+ePWwhRrQ+FqiifI9Nakx3Xr1/fYUfjFhcX89FHH1FSUoKbmxtPP/00CxYsIDs7m6FDh9KzZ8+bfmalpaVs3LiRXbt2MW3aNFq0aGGj6B2PRqOhS5cuGI1GFi5cyJ///GcCAgJsmuAdollmzJgxVk8UlqbRaMwJ8/rv7UliYiIPPvgg69evv+k5JycnnJyc7HqW+K1btzJhwgQuXbpk61DsjtFoJDY2Fh8fHz766CM+/fRT/vvf/7J//34mTZp0001yg8HADz/8QFxcHC+//DItWrSwu3K89k4IQdeuXRk1ahSffPKJzcez2P2ZuxDCLrpdFRYWUlpaiqenZ515w5fPQGNvZRAqy83NjYYNGzp004+llJaWEhMTY76hp9FoWLhwIStXrgTKmtm6dOnCv//9b3r37m1er6SkxFwszt3dne7du5OQkGCz46gLnJycaNq0KW+//TZTp06tsMieNU9M1F9FJe3Zs4eEhASmTJli8YqI5TMzoTeg0RswlurRubqgc68HVuydEBAQwBdffGGVfdeGXr160atXL1uHYRf0ej0HDhww9+zR6XQkJCTw/PPP88gjj6DT6SgtLb3pjL2kpIT9+/cTExODl5cXR44cqXEsP/30ExMnTrTI+3bdunX079//hibZ6tDr9axevfqmwmnVVZljDA8P5+jRoxXuJy0tzWonr3UmuScmJnLy5En69u1rlR9WWFgYvr6+VjlLlFLy4ezX8dh3liZJORgKi3Bv2pjmU8fQ8onxOHlZ52rB0a9AHD1+S6pXrx7Tpk0zP87Ly6Nt27bm2Y8CAgL473//y9ChQ7n33nvN3Vs9PDyYPn069evXJzAwkL59+9Y4lpMnTzJjxgyLNEUmJiby6KOP1rhZtri4mPPnzzNz5swaxwSWO8byQUzWcMdMJYRoAnwHBAASWCSl/FgI0QD4CWgGxAMTpZSZouwv7mNgOFAAPC6lPGyV6K+zfv16/vvf/7JmzZpqjxarSJMmTWjSpInF9yulJOdMHCHrDnDtciI/kYsEemVlkPPqBbJPxtL1o9dxqm9/JQsU+1WvXj02b96MXq/HycmJsLAwdu/eTUlJCY0aNTIXDivn7+9vsblnw8LCLPbB27x5c4sUJSsv/GUpljrGevXqWSWvQCXKDwghgoAgKeVhIYQncAgYAzwOZEgp3xVCvAr4SClfEUIMB/5CWXLvDnwspexe0WtYovxAeno6cXFxREREONREEoaSEg6/+C6xn/1IqTQggZMUoAE64Y7WxYXeP88jeNSgO+1KUaolKyuL7OxsnJ2dCQwMrHbSklKSlJSEXq9Ho9HQqFGjGnWjLC0tJSkpCXd39xp3eS4/RgBfX99q13eyxDEaDAYSExNxdXXFx8eHpKQkoKz8cmBgYJX2VaPyA1LK5PIzbyllLhADNAZGA9+aVvuWsoSPafl3ssxewNv0AWFVfn5+dO/e3aESO4A+t4DLKzaDlOgQSOAKxQRSdtlsLC4hYdl6i4x8K5+JvqqTMlR23waDwWoj9A4ePEhUVJTdzb7l6LKyspgwYQLt27ene/fuHD5c/Ytsg8HAkCFD+Otf/0rHjh1ZsWJFjWKLi4ujTZs2vPzyyzXaj5SSuXPnMnbsWIYNG2a+yVwdO3bsICIigmeeeYbOnTtXqzRHZmYmXbp0Ydq0aSQkJNCxY0eeeeYZi90PKFelBiMhRDOgM7APCJBSJpueSqGs2QbKEv/1w7SumJYpt2AoKUWfXwBAKZKdZBNKPfxxQlB2BlWak4u0QEI+f/4848ePZ+fOnTXe1x+dPn2aFStWVLn2fWUtXbqUDz/80CqjRaWUpKWlcfXqVbsaPl4bcnJy2Lt3L9u3bycoKKhGyV2r1bJlyxY++eQTOnbsyLZt22oU21tvvUVRUVGNyz8kJiYyf/58XF1deeaZZ25ZHK2yMjIyKCgoYNeuXbRp06ZaTUY+Pj4888wz6PV6pJS0b9+eIUOGWPzEpdLJXQjhAawEnpdS3vBxJcv+Iqr0VyGEmCmEOCiEOFjR8HYpJTt27DBfUtU1unouuAWXXYqlUMpZCjlHIQkUI5EgBO7NghEWuDnl7OxMw4YNrXLD2dnZGTc3N6v153/ppZdYtGiRVWKXUrJ9+3arfOg5iqCgoBpf9ZbP5PXyyy+TnJzM008/Xe197du3j4MHD9K/f38yMzMpKCio9r4MBgN5eXm0aNGChQsXsm/fvmrvKz8/Hw8PDwICAsjLy6vWyYBWq71h8qG+ffvSv39/QkJCqh3XrVSq64cQwomyxL5ESrnKtDhVCBEkpUw2NbukmZYnAtffIQg2LbuBlHIRsAjK2twrev2rV69W+YytvHuhEMKup4bTebjT7NFRnHjzvzTRO/MMN7ZgOdX3oPljoy0Sf0hICF9++WWN93MroaGhFr1h9UdVbYusCiEEffr0scuBZtam0WhwcnLi1VdfJS4urkYJ3mAw8PTTT7N161aWL19OWFhYtffVqFEjXnjhBVauXEl6ejp5eXk33QSurAYNGjBs2DDCw8O5cOFCtWOCsnt7Hh4ehIeHs3nzZkpLS6u8D6PRaB43k52dzfz583F3d7d4n/fK9JYRwJdAjJTyw+ueWgtMBd41/f/Ldcv/LIRYRtkN1ezrmm+qZfTo0VXugmg0Gnnuuedwd3fnww8/vPMGNiK0GsJmPETm0RiurN6KLC27NJOAwVlHo2en4BPR1jKvZcUPOHv98KwMIQSNGjWydRg20ahRI7744gt+++03/vSnPzFp0qQa7a9ly5Y0aNCAjRs3cu3aNcaOHVut/TRp0oRZs2bh7+9Pbm5ujbpCenp6Mn36dLZu3Uq3bt1o165dtff11FNPkZmZSWZmJtOnT69W3aHCwkLy8vJo3749K1asYOrUqWRnZzN+/Phqx3Urlekt0xuIBk4A5Q2/r1PW7v4zEAIkUNYVMsP0YfApMJSyrpBPSCkr7Apjjck6jEYjn376KfXq1WP69Ol2nXyklJRkZpO0MZqEZb+iz83Ho2UITR8ZhVeXdrjVr9kADkVR6qaKesuomZhqoHxCDq1WS4cOHSzyASKlRBqMCG31R6YajUYyMjJwc3Or9qWsrUgpyczMxMnJqcajEhWlrlMzMVmJlJKLFy8SHx9vsX0KIdDotDX6oMjIyGDcuHF8/vnnFourthQVFfH444/z9ttv2zoURXFodab8gC0IIRg+fHitvqaUktjYWIqLi297teDm5sbo0aOJjLzlB7pd0+l0DB8+nODgYFuHoigO7a5sljl48CBLlizhlVdesWovDGuQUjJjxgxSUlJYvXq1RYZmK4rimCpqlrkrz9xTU1M5fvy41QbcWNtzzz1HdnY2xcXFdl1v/XaklBQWFqLVah1uRLGiOIq7ss19yJAhrFu3jubNm9s6lCorn7PxxIkTPPjgg2RmZtZ6DFJK81d1lJaWMnPmTJu0q9c0dkVxFHflmbtOp7ttv3kppXkwR+PGje32rLh58+Z06dLFXLq1XHFxMbGxsYSEhFhlirjyOW2Lioro169ftX4+Go2G8PBwm/QtLyoqYsuWLbRu3ZrWrVvX+uvbitFoNP/edDodPXv2VE16ddxdeeZekdLSUp599ln+/e9/2zqUCg0dOpQ5c+bcVN0uNjaWSZMmsXHjRqu9dklJCSUlJdXeXqfT8fLLL/PII49YMKrKkVJSXFxcrZGFjsxgMPDSSy+xcuVK7rvvPhYuXGjrkBQrs/sz9/JL6Nqaz1Gn0/H888/bvI91Xl4eUVFR9OjRg2bNmlV6u5CQEN544w2rzlDUp08f4PajUouLi1m3bh3t2rWr0WhAa6hXrx7jx4+32ysya9HpdCxcuJCFCxei0WjMIz6llOYP6rvtZ2IvtFptjUoj347dJ3com2qrV69eNa7pXBkajYb777//ts9LKTl//jyFhYV06NDBavVIkpOTmTNnDrNnz65Scq9fvz4PPfTQbZ+/dOkSaWlpREREVGtWqcp8yGZnZ/Puu+8yceJEu0vud+ukz0IIWrZsydSpU9m0aROHDx9m4sSJZGVl8c4775CSkkLjxo0dbiJ6R5eVlUXjxo2ZNWuWxfftEMnd1dXVKp9s1fX+++9z5coV1qxZc1ObN5RdAsfFxXHhwgV69ep1y6uACxcu8Mknn/DUU0/dsu23adOm/PTTTzRubNlqyYsXL2br1q2sX78eHx+fm56XUpKcnIyLiwu+vr633EdycjL/93//x8MPP3zLvvQNGjTghx9+qJUPY6Vy9Ho9jz/+OL169aKoqMg8ctnHx4f333+fLVu24OvrW6NyuErV2XSaPXswePBgW4dwg2effZb8/PzbnvlmZWXx97//na5du9KtW7fbrnP8+PHb9nZxdna2ylnv1KlTue+++27b7FRcXMy0adNo1aoVH3/88S3Xyc/P5/jx49x33323fF6n09GmTRuLxazUnEajYfTo0eTm5vKvf/2LKVOm2DokxcrsPrnb2yW0EIIOHTpUuI67u7u5xOitzo4BIiIiWLduXbWqytXEnUrzOjk58fjjj1d4ed6iRQvWrVt3y6sWxT5pNBqV0O8ydp/cHZGrqyuPP/54hetotVrc3d1rJ6Aq0Gq1FbbZQ1micLSCZIpyt1FdIZUbJCcns3PnTgoLC20dSpVdu3aN33//ndzcXFuHoig2p5K7coP09HTOnTtnlblKrS0rK4tz5845bFkJRbEk1Syj3KBdu3aEhobW+r0AS2jWrBkPP/ywQ8Z+J5mZmfz++++0atWKkydP0rt377t29iilchz+zN1gMJCVleWQIw6NRiNZWVk1Gu1paTqdzqoTXVuTVqt12NjvJC4ujm3btjFhwgT++te/cvjwYVuHpNg5h/8rSEtLY+nSpVy8eNHWoVRZdnY2y5Yt49SpU7YORXEABw4c4PLly7Ro0cLWoSgOwOGTu5eXF5GRkQQEBNg6lCpzc3MjMjJSTUyhVCg2NhaDwUBJSQmffPIJYWFhtg5JcQAO3+bu5ubGPffcY+swqsXFxcUhZ0tSatehQ4do0qQJ9913H+PHjycoKMghmyGV2uXwZ+7KnRkMBvbu3UtsbKytQ6mU62uuSyk5cuQIJ0+evKtrsKekpJCTk8Ply5dJS0uzdTiKA7jjmbsQognwHRAASGCRlPJjIcQ/gRlAumnV16WU603bvAZMAwzAs1LKTVaIXakkg8HA+fPnKSoqsutLeiklhqIS0nYe4OreI2CUeHdqQ2xeGu6+PrRv397WIdqERqPhsccew2g08sMPPyClZPny5VXah5SS0tJS8weks7Oz3Y3+ViyrMs0yeuBFKeVhIYQncEgIscX03EdSyg+uX1kI0Q6YBLQHGgFbhRCtpJQGSwZeHaWlpaSkpODr6+twIywNBgPJycl4e3vfVMP9TpycnBg/frxdFV+7lYLEVA49N4fU7XuJyUqjSBppXr8BwRHtuefTN20dns106tTppvkFqlq7R6/X07dvX/z8/Dhz5gxff/01vXv3tmSYip25Y3KXUiYDyabvc4UQMUBFpQpHA8uklMXARSHEeaAbsMcC8dZIZmYm69ato2/fvnesD2Nv8vPziYqKIiIigh49elRpWyEE9erVs1JklmEoLuHEPz/hyuotSGnEGShEEp2TzH07Czn1t3n0+mkeunp1rw/7nVy8eJFvv/3W/Dg3N5dWrVpVqdeMVqvl448/pmHDhjzxxBMsWbLEKsldr9dz6dIlioqKLL7v2uDi4kKzZs3s/kSoMqp0Q1UI0QzoDOwDegF/FkI8Bhyk7Ow+k7LEv/e6za5wiw8DIcRMYCaUTTBRG7y9vRkyZAiBgYG18nqW5ObmxuDBg29bhteRSSnJPhlL8sadYGo28EDLPvLwxwkNkP6/w6TvOkTgfffedc0JAwYMYPv27ebHv/32W5X3odFo6Ny5M//4xz84deoUb7zxBlA2qvf999/n0qVLPPfcczWKU0rJwYMHWbNmDU2aNKnRvmwlLi6OKVOm0KVLF4d/n1U6uQshPICVwPNSyhwhxALg35S1w/8bmAs8Wdn9SSkXAYsAIiMja+VOmbOzMy1btqyNl7I4nU53w5laZmYmmZmZNG3a1OHOMnJzc0lNTaVp06bmeTzzLydRmJRuXscbHX2pz2Hy0CMpycgm51w8gffda6uwbSYqKopp06aZH+v1en766acq7cNoNPLmm2/y1VdfsWTJEgYNGgSUTe7y8ssvs2PHjhoP/ioqKmL9+vVMnz7dYfviX7p0icWLF9O6desqN3/am0oldyGEE2WJfYmUchWAlDL1uucXA1Gmh4nA9R/bwaZligWdPHmSmJgYHn74YbusLlmR8+fPs2fPHiZNmkSDBg0AEDodQiOQBkkJkisUU4AREAgAjUDj5PA9d6tl0KBBREdH37CsadOmVdqH0WgkISGBrl27sn79enJycpg4cSIajQYvL68a34MyGo1s2LCBNm3a0KJFC4cdJdykSRPCwsLYsWMHDzzwgEOfvVemt4wAvgRipJQfXrc8yNQeDzAWOGn6fi3woxDiQ8puqIYB+y0adS3R6/UcOHAAPz+/Cmug20JERAQtW7a8bVu60Wjk6NGjuLi40K5dO7t6k7Zu3Ro/Pz/q168PlN0T8GrdHM9WzcmJuYAWgRHQIOiFJ05ocGscgG9kB7s6jtri7e2Nt7d3jfah0+lYunSpZQK6hfT0dA4cOMBLL73ksIkdyu5NjB07lvfff58uXboQFBTksO+5yvwWegGPAgOFEEdNX8OB/xNCnBBCHAcGAH8FkFKeAn4GTgMbgWfsoadMdZR3IUxKSqr0NsXFxZw4cYLs7OxKb5Obm8uJEyeqVGbX09OTRo0a3fYPSUpJXFwcly5dqvQ+S0tLOXXqFFevXq30NgCJiYmcO3cOo9FYqfXd3NwIDg6+YSYrj5YhNHtkFMJJhw5BGPVojxv10SG0WoLHDcank5rdCcqufM6ePWvrMMwMBgMrV67k/vvvv+3kNI6kfv369O3bl6ioKIceW1GZ3jK7gFt9dK2vYJs5wJwaxGUXnJ2dmTBhQpUmks7JySE6Opq+ffvi5eVVqW2Sk5OJjo7G19fXYr1aNBoNI0aMqNJZR1FREbt27SI8PLxK85+eOHGCtLQ0mjdvXu2zNqHR0Povj6LRabn4w1pyTp9HSolnaFNCHhxKmxeeBAc+I7Sk/Px8DAb7OF+SUnL69GmysrLo1auXw57lXk8IQd++fdm/fz9nzpyhbdu2Dnlcd2cjZiVVpwuhj48PDz74oLnJoTKaNWvGhAkTzO3PliCEqHLpW3d3d8aNG1flG0m9evWitLS0Sh+Ct+Lk6U7bF6cR8uAw8i8lIY1G3BoH4tEiGE0N9+3oCgoK+Ne//sXevWUd0d59910bR1SmpKSEqKgoRo4cWaemXXR1dWXs2LGsXLmS2bNn231X4lu5u/9irECn0+Hn51elbZydnSucs7S2aDSaKscO3Hay7eoQWg0ezYNxb/b/e8864lmTpcXExPDZZ5/xzjvv4OLiYhe13KWU/P777/j6+tKhQ927H9K6dWt8fHzYt28f/fr1c7jjU9e5il0SQpi/lP9Pr9ej1+vtoi04JyeH7du3V7n5z1HodDoeeughNm7cSEZGhq3DqTKV3BXFAXh6etKtWzeioqJYuXIlycnJd97IigwGA2vWrOHee+91yEGBleXr60v37t3ZtGmTXXygVoVK7oriALRarblLpLe3t3nwl61cunSJ8+fPM3DgQIfu+ngnGo2GIUOGcO7cOeLi4hwqwdfd34qi1CHl0zG2bduWtm3b2nT0ZGlpKStWrGD06NEOV4CvOurVq8fw4cNZvXq13fRSqgyV3BXFQezevZtvv/2Wb7/9litXrtgkBiklBw4cwNnZmc6dO9fJtvY/EkIQERGBEILDhw87zNm76i2jKA6gSZMm/POf/zQ/rk6NpOzsbOLj42ncuHGVxjFcr7CwkM2bN/Poo4/W6eaYPyof8/LFF1/Qpk2bKnV1tpW757ejKA7s8uXLvP766/zzn//kb3/7W7UmVY+OjiYyMrLKRcfKGY1Gfv31V3P9mLvhrP16wcHBhIaGsmPHDoc4e1fJXVEcxOjRozlz5gwzZsyo1vZDhgzh3ntvrKp5/XSGd5KWlsbhw4cZPHjwXZfYoeym9rhx49i7dy9JSUl2n+BVclcUB+Dp6cnOnTvp378/S5curVZdfycnp5tGEWdmZvLCCy/wxRdfVHizUK/Xs2rVKoYMGVIn6sdUl4eHB4MGDWLdunUquSuKUjO7du1i0aJFrFmzhrfeeougoKBqdYUsKCigqKiIjIwMrl27BkCDBg346KOPmDFjRoXzApw+fZrMzEzuvffumyzlekIIevfuTWZmJqdPn7brBK+Su6LYuStXruDk5ESXLl0YP348vXv3JiUlpcr7uXz5MkOHDkWj0XDgwIFKb1dcXMyaNWsYN26czfvX2wMXFxfGjBnDqlWrKC4utnU4t6V6yyiKA9i0aRMzZsygtLSUbdu2MXjw4Crvo3Xr1ubp9SpLSsn27dsJDg6mdevWd/VZ+/XCwsLw8/Nj165dDBo0yC5/LurMXVHs3MCBA2natCkPP/wwU6dOpWPHjvTs2bNWXjs7O5vo6GiGDh1qlwnMVnQ6HRMmTOC3334jPT39zhvYgEruimLnvL29eeuttxg5ciQjRoxg3rx5tTJRevkkHL169XLoGYmsxdfXl27durF58+ZKT1RTm1RyVxQ7t27dOnr27Mnp06c5duxYrfTUkFKSkJBAfHw8ffv2VYn9FjQaDUOHDiU2NtYu686o5K4odq60tJT8/HyWLFnCihUr+Omnnzh9+rTVX3PVqlWMHj3a4SZgr00uLi6MGDGCNWvW2F3dGZXcFcUKpJSUZOWQd/EKuXGXKb6WhazmpXuvXr14//33adCgAT4+Pvj6+lZpvt2qKq8f4+TkdNfUj6mu8rozOp2OgwcP2tXZu+otoygWZiwp5fLabcQu/Im03YcRRiMNOrWhxZMTaP7IKLT1XKuUMJs0acLDDz9M//79+d///keLFi2sWtuksLCQjRs3Mn369Luqfkx1OTk5MX78eBYsWED79u0tOjNZTdwxuQshXIGdgItp/RVSyn8IIZoDywBf4BDwqJSyRAjhAnwHdAWuAQ9JKeOtFL+i2BUpJRd/XMeh5+aQkJPBFjKJxIMO+46TdfwcxWnXaP/6n6CKZ8NBQUEEBQXRpk0bXFxcrNbf3Gg0EhUVRXh4OCEhIeqsvZIaNWpE+/bt2bp1K2PGjLGLn1tlPpaLgYFSyk5ABDBUCNEDeA/4SEoZCmQC00zrTwMyTcs/Mq2nKHeFwqQ0Yj9bgj4njwCc6IIHJZRdqhsKi7jw5Qpyzl6s9v49PDysOpAoNTWVo0eP2m3fbXul1WoZOXIkR44c4fLly3bRPHPH5C7L5JkeOpm+JDAQWGFa/i0wxvT9aNNjTM8PEupdotwFpJRkn4ol89gZALSAlhvf+gWXk0n7/YBd/PH/kV6v56effmL48OF3df2Y6vL09KR///6sX7/eLn6/lWpQE0JohRBHgTRgC3AByJJS6k2rXAHKp6tvDFwGMD2fTVnTzR/3OVMIcVAIcdBeBwEoSlUZioqRev11S6TpX9P/BiP6AuvdDK3I9RUgb5V8Tp8+TWFhIffcc486a6+G6+vOnDx50uYJvlLJXUppkFJGAMFAN6BNTV9YSrlIShkppYz08/Or6e4UxS64BjTExbfsrDeVUo5RwGkKuEpZwtd5uOHerHFFu7AKo9HIwoULCQ8Pp1u3bhw7duym5zdu3MjYsWNxdnau9fjqCmdnZ8aOHcuaNWsoKiqyaSxVuhUupcwCtgM9AW8hRPkN2WAg0fR9ItAEwPS8F2U3VhWlThNC4N2xFf4DugMQiDNT8OMR/PGjrJ3cu2NrAgf2qPUzY4PBwLvvvsuECRNo2LAhCxcuvOH5goICunXrpurHWEBoaCiBgYHs2rXLpmfvlekt4weUSimzhBD1gPspu0m6HZhAWY+ZqcAvpk3Wmh7vMT3/m7T19Ymi1BJtPVfavTSNpF9//0PzDKARtHnxCZy8bNdVrnfv3qSlpZmHy2dnZzN//nxOnjyJu7s777zzTo1fQ6/X31Q3vroMBgMajcYiHziWjOtO+8rOzmbp0qXs2bOnwlLK165do3Fj61zJVeZIg4BvhRBays70f5ZSRgkhTgPLhBBvA0eAL03rfwl8L4Q4D2QAk6wQt6LYJSEEPhHtGHl24y2fd2noY9Mz499//53z58/TokULANzd3Xn00UdZt24d/v7+dO/evcav8eabb/Kvf/3LIsc5d+5cpk6dWu05X8uVlJTw7rvv8uabb9Y4JrDcMV68eLFK5Zer4o7JXUp5HOh8i+VxlLW//3F5EfCgRaJTFAekcdLhFhxo6zBuoNVqef311/nss8+oV68eTz31FFBW3TA4OBhfX1/8/PwIDg6u8Wt5eHjQuHFjiwyAql+/Po0aNcLf379G+ykuLsbDw8MixweWO0ZrjjRWI1QV5S6g0WiYMWPGbedf7dq1K25ubhZ5rYceeshiVycjR460yIhPnU7Hgw9a7pzTUsfo7+/PfffdZ4GIbqbGFivKXUIIccMXlHWP3LdvHxs3bmTnzp01ugFoNBr5/vvvOXbsGF988UWNe4tkZ2ezf/9+/ve//9VoPwAHDx7k8OHDfPbZZzUqumYwGPjuu+9qdIyFhYUsWrTIXN1z165dzJ8/nxUrVtx54ypQyV1R7mIpKSlMmDCBH3/8keeee45Vq1ZVe19Go5ENGzYgpeTtt99mzpw5NYpt9+7d/OUvf+H777+v0X6KioqYP38+69at48iRI8THx1d7X1988QXTp09n27ZtzJ49m4KCgirvo6SkhE8++YT58+eTnp7Oyy+/zIULF5g7d26147oVldwV5S5WWlpKZmYmP/30E61atSIjI6Pa+9LpdPz444/06dMHFxeXGs0vWlhYyLx58yxSbjg1NZWlS5dy8eJFCgoKatSPPzw8HA8PD1atWsXw4cOr1ZTl5eXFE088YX48efJkunTpYvHxBSq5K4pCSUlJjWcTklJy8uRJJkyYQHh4eI16puzatYvjx4/Tvn17jh8/TlJSUrX3JYTA2dmZF154gUuXLtXozH3Hjh34+/szc+ZMNm3aZJEboikpKYSEhFh8di11Q1VR7mJubm60bNmSXr16YTQazV0kq8NgMPDoo4+Sk5PDsGHDOHPmDJGRkdXaV58+fThw4ABvv/02165dq1FXyEaNGjFjxgzWrFnDtWs1G0/ZqlUrrl69yi+//ILBYKjWPYri4mJOnjxJSkoKGzZsYOvWrXh7e5OcnFyj2P5I2MP4osjISHnw4EFbh6Eod6X4+HgSEhLw8PCgS5cu1e4FYjQaOXDggPkmY1BQEK1atapRbHFxcZSUlNCmTc0qnpQfI5Ql6KCgoGrt5/pj1Ol0dOvWrcpVOktLS9m3b99NMzfVr1+fzp1v6nVeISHEISnlLT9BVXJXFEVxUBUld9XmriiKUgepNndFUexednY2hw4dumFZWFgYTZo0sVFE9k8ld0VR7J7BYCArK4vVq1cTExPDtGnT8PLyAripl4+Pjw+ZmZkAuLq6EhAQUOvx2gOV3BVFsXsNGjRg3LhxxMbGkpWVxbp168jKyuLjjz8mJycHvV5P7969OXHiBN26dSM6OpqePXtiNBrZsmWLrcO3CdXmriiKw9Hr9RgMBkpLS5k7dy7Nmzdn6tSpuLm5sX79elq1akVISAi9e/e2dag2o5K7oigOrXv37jzwwAOMHz8eKOvXXlJSQuvWrWs04tbRqWYZRVEcxpAhQ+jUqRMGg4GgoCCaNGlCSEgIDz74IC4uLrzzzjuEh4ezZ88epJRWq7joCFQ/d0VRFAel+rkriqLcZVRyVxRFqYNUclcURamDVHJXFEWpg+6Y3IUQrkKI/UKIY0KIU0KIf5mWfyOEuCiEOGr6ijAtF0KI/wohzgshjgshulj5GBRFUZQ/qExXyGJgoJQyTwjhBOwSQmwwPfeSlPKPE/8NA8JMX92BBab/FUVRlFpyxzN3WSbP9NDJ9FVR/8nRwHem7fYC3kKI6hVPVhRFUaqlUm3uQgitEOIokAZskVLuMz01x9T08pEQwsW0rDFw+brNr5iW/XGfM4UQB4UQB9PT06t/BIqiKMpNKpXcpZQGKWUEEAx0E0J0AF4D2gD3AA2AV6rywlLKRVLKSCllpJ+fX9WiVhRFUSpUpd4yUsosYDswVEqZbGp6KQa+BrqZVksEri+yHGxapiiKotSSyvSW8RNCeJu+rwfcD5wpb0cXZRMujgFOmjZZCzxm6jXTA8iWUlp25ldFURSlQpXpLRMEfCuE0FL2YfCzlDJKCPGbEMIPEMBR4E+m9dcDw4HzQAHwhMWjVhRFUSp0x+QupTwO3DQlt5Ry4G3Wl8AzNQ9NURRFqS67qAophMgFzto6jlrSELhq6yBqgTrOukUdp31qKqW8ZY8Ue6nnfvZ2ZSvrGiHEwbvhWNVx1i3qOB2Pqi2jKIpSB6nkriiKUgfZS3JfZOsAatHdcqzqOOsWdZwOxi5uqCqKoiiWZS9n7oqiKIoF2Ty5CyGGCiHOmuq/v2rreGpCCPGVECJNCHHyumUNhBBbhBCxpv99TMsdtu69EKKJEGK7EOK0qcb/c6bldepYK5jLoLkQYp/peH4SQjiblruYHp83Pd/MpgdQRaYCgUeEEFGmx3XuOIUQ8UKIE6Y5KA6altWp9205myZ306jXzyirAd8OmCyEaGfLmGroG2DoH5a9CmyTUoYB20yP4ca69zMpq3vvKPTAi1LKdkAP4BnT762uHWv5XAadgAhgqKmkxnvAR1LKUCATmGZafxqQaVr+kWk9R/IcEHPd47p6nAOklBHXdXmsa+/bMlJKm30BPYFN1z1+DXjNljFZ4JiaASeve3wWCDJ9H0RZn36AhcDkW63naF/AL5TVHKqzxwq4AYcpm3jmKqAzLTe/h4FNQE/T9zrTesLWsVfy+IIpS2wDgSjKyorUxeOMBxr+YVmdfN/aulmmUrXfHVyA/P+F01KAANP3deLYTZfknYF91MFj/eNcBsAFIEtKqTetcv2xmI/T9Hw24FurAVffPOBlwGh67EvdPE4JbBZCHBJCzDQtq3PvW7CfEap3BSmlFELUme5JQggPYCXwvJQyp6xAaJm6cqxSSgMQYaqMupqyOQzqFCHECCBNSnlICNHfxuFYW28pZaIQwh/YIoQ4c/2TdeV9C7a/oXo31H5Pva48chBlZ4Dg4McuyubTXQkskVKuMi2uk8cKN8xl0JOyqSPLT4yuPxbzcZqe9wKu1W6k1dILGCWEiAeWUdY08zF17ziRUiaa/k+j7MO6G3X0fWvr5H4ACDPdlXcGJlFWD74uWQtMNX0/lbL26fLlDln3XpSdon8JxEgpP7zuqTp1rOLWcxnEUJbkJ5hW++Nxlh//BOA3aWqstWdSyteklMFSymaU/Q3+JqV8mDp2nEIIdyGEZ/n3wGDK5qGoU+9bM1s3+lNW+/0cZW2Zf7N1PDU8lqVAMlBKWfvcNMraIrcBscBWoIFpXUFZT6ELwAkg0tbxV+E4e1PWdnmcslr+R02/xzp1rEA4cMR0nCeBN03LWwD7KZuzYDngYlruanp83vR8C1sfQzWOuT8QVReP03Q8x0xfp8rzTV1735Z/qRGqiqIodZCtm2UURVEUK1DJXVEUpQ5SyV1RFKUOUsldURSlDlLJXVEUpQ5SyV1RFKUOUsldURSlDlLJXVEUpQ76fy23F2n1CWwxAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -424,16 +479,11 @@ " plt.imshow(env.render(mode='rgb_array'))\n", " display.display(plt.gcf())\n", " display.clear_output(wait=True)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "#### Extending `mobile-env`: Adding a Handler for a Custom Observation Space\n", "\n", @@ -441,56 +491,65 @@ "Hence, when designing a new Markov Decision Process for a reinforcement learning approach, you can quickly validate it by implementing a new handler in `mobile-env`.\n", "\n", "Let's first look at the default handler (for single-agent RL) and then add a new handler with a custom observation space." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "['connections', 'snrs', 'utility']" + "text/plain": [ + "['connections', 'snrs', 'utility']" + ] }, - "execution_count": 17, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "env.handler.features" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "By default, observed features are:\n", "* The current connections between users and cells (binary)\n", "* The signal-to-noise-ratio (SNR) between all users and cells (normalized to [0,1])\n", "* The current utility (i.e., QoE) of each user (normalized to [-1,1])" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 10, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Raw observations: [ 0. 0. 0.38170958 1. -1. 1.\n", - " 0. 1. 0.2665389 0.47082826 0. 0.\n", + "Raw observations: [ 0. 0. 0.38170958 1. -1. 0.\n", + " 1. 1. 0.2665389 -0.08450864 0. 0.\n", " 0.34874913 1. -1. ]\n", "\n", "Observations for user 1:\n", @@ -499,9 +558,9 @@ "Current utility: -1.0\n", "\n", "Observations for user 2:\n", - "Current connections to the 2 cells: [1. 0.]\n", + "Current connections to the 2 cells: [0. 1.]\n", "SNR to the 2 cells: [1. 0.2665389]\n", - "Current utility: 0.47082826495170593\n", + "Current utility: -0.08450863510370255\n", "\n", "Observations for user 3:\n", "Current connections to the 2 cells: [0. 0.]\n", @@ -525,28 +584,29 @@ " print(f\"Current connections to the {env.NUM_STATIONS} cells: {obs[offset:offset+env.NUM_STATIONS]}\")\n", " print(f\"SNR to the {env.NUM_STATIONS} cells: {obs[offset+env.NUM_STATIONS:offset+2*env.NUM_STATIONS]}\")\n", " print(f\"Current utility: {obs[offset+2*env.NUM_STATIONS]}\")\n" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Now, let's extend the observation space by adding an observation that indicates whether a user is connected to any cell at all, i.e., a single binary entry per user.\n", "\n", "For that, we create a new handler that inherits from the existing central handler and simply overwrite the relevant parts: The available features and the observations." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 11, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "from mobile_env.handlers.central import MComCentralHandler\n", @@ -586,23 +646,26 @@ " flattened_obs.extend(ue_obs[\"utility\"])\n", "\n", " return flattened_obs" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 90, + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "New, raw observations: [0.0, 0.0, 0, 0.42142308, 1.0, -1.0, 0.0, 0.0, 0, 0.08438771, 1.0, -1.0, 1.0, 0.0, 1, 1.0, 0.059552114, 0.65321183]\n", + "New, raw observations: [0.0, 0.0, 0, 0.42142308, 1.0, -1.0, 0.0, 0.0, 0, 0.08438771, 1.0, -1.0, 0.0, 0.0, 0, 1.0, 0.059552114, -1.0]\n", "\n", "Observations for user 1:\n", "Current connections to the 2 cells: [0.0, 0.0]\n", @@ -617,10 +680,10 @@ "Current utility: -1.0\n", "\n", "Observations for user 3:\n", - "Current connections to the 2 cells: [1.0, 0.0]\n", - "NEW: Any connection?: 1\n", + "Current connections to the 2 cells: [0.0, 0.0]\n", + "NEW: Any connection?: 0\n", "SNR to the 2 cells: [1.0, 0.059552114]\n", - "Current utility: 0.6532118320465088\n" + "Current utility: -1.0\n" ] } ], @@ -641,80 +704,777 @@ " print(f\"NEW: Any connection?: {obs[offset+env.NUM_STATIONS]}\")\n", " print(f\"SNR to the {env.NUM_STATIONS} cells: {obs[offset+env.NUM_STATIONS+1:offset+2*env.NUM_STATIONS+1]}\")\n", " print(f\"Current utility: {obs[offset+2*env.NUM_STATIONS+1]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the observations include an additional entry per user that indicates whether the user is connected to any cell at all. Maybe that's a useful observation for an RL approach?\n", + "\n", + "Let's try to train an RL agent with the custom observations on our custom environment.\n", + "\n", + "\n", + "## Step 3: Single-Agent RL with `stable-baselines3`\n", + "\n", + "In a single-agent approach, the single RL agent controls cell selection for all users simultaneously.\n", + "We can train such a single-agent approach with many different RL frameworks.\n", + "A popular example is `stable-baselines3`, using the PPO algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: stable-baselines3 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (1.3.0)\n", + "Requirement already satisfied: gym<0.20,>=0.17 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (0.19.0)\n", + "Requirement already satisfied: matplotlib in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (3.5.0)\n", + "Requirement already satisfied: torch>=1.8.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (1.10.0)\n", + "Requirement already satisfied: numpy in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (1.21.4)\n", + "Requirement already satisfied: cloudpickle in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (1.6.0)\n", + "Requirement already satisfied: pandas in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (1.2.3)\n", + "Requirement already satisfied: typing-extensions in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from torch>=1.8.1->stable-baselines3) (4.0.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (1.3.1)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (2.4.7)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (21.3)\n", + "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (8.4.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) 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|\n", + "| iterations | 1 |\n", + "| time_elapsed | 14 |\n", + "| total_timesteps | 2048 |\n", + "---------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -5.55 |\n", + "| time/ | |\n", + "| fps | 156 |\n", + "| iterations | 2 |\n", + "| time_elapsed | 26 |\n", + "| total_timesteps | 4096 |\n", + "| train/ | |\n", + "| approx_kl | 0.011195142 |\n", + "| clip_fraction | 0.121 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -3.29 |\n", + "| explained_variance | 0.023 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.979 |\n", + "| n_updates | 10 |\n", + "| policy_gradient_loss | -0.0161 |\n", + "| value_loss | 2.42 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -5.14 |\n", + "| time/ | |\n", + "| fps | 156 |\n", + "| iterations | 3 |\n", + "| time_elapsed | 39 |\n", + "| total_timesteps | 6144 |\n", + "| train/ | |\n", + "| approx_kl | 0.010791814 |\n", + "| clip_fraction | 0.11 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -3.27 |\n", + "| explained_variance | 0.296 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 1.53 |\n", + "| n_updates | 20 |\n", + "| policy_gradient_loss | -0.0168 |\n", + "| value_loss | 2.6 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -5.18 |\n", + "| time/ | |\n", + "| fps | 159 |\n", + "| iterations | 4 |\n", + "| time_elapsed | 51 |\n", + "| total_timesteps | 8192 |\n", + "| train/ | |\n", + "| approx_kl | 0.012837088 |\n", + "| clip_fraction | 0.142 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -3.24 |\n", + "| explained_variance | 0.33 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 1.15 |\n", + "| n_updates | 30 |\n", + "| policy_gradient_loss | -0.0207 |\n", + "| value_loss | 2.4 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -5.07 |\n", + "| time/ | |\n", + "| fps | 158 |\n", + "| iterations | 5 |\n", + "| time_elapsed | 64 |\n", + "| total_timesteps | 10240 |\n", + "| train/ | |\n", + "| approx_kl | 0.011881544 |\n", + "| clip_fraction | 0.144 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -3.21 |\n", + "| explained_variance | 0.372 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 1.35 |\n", + "| n_updates | 40 |\n", + "| policy_gradient_loss | -0.0195 |\n", + "| value_loss | 2.45 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -4.56 |\n", + "| time/ | |\n", + "| fps | 158 |\n", + "| iterations | 6 |\n", + "| time_elapsed | 77 |\n", + "| total_timesteps | 12288 |\n", + "| train/ | |\n", + "| approx_kl | 0.012489447 |\n", + "| clip_fraction | 0.14 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -3.17 |\n", + "| explained_variance | 0.434 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.963 |\n", + "| n_updates | 50 |\n", + "| policy_gradient_loss | -0.019 |\n", + "| value_loss | 2.16 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -4.86 |\n", + "| time/ | |\n", + "| fps | 142 |\n", + "| iterations | 7 |\n", + "| time_elapsed | 100 |\n", + "| total_timesteps | 14336 |\n", + "| train/ | |\n", + "| approx_kl | 0.010966829 |\n", + "| clip_fraction | 0.124 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -3.11 |\n", + "| explained_variance | 0.418 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 1.42 |\n", + "| n_updates | 60 |\n", + "| policy_gradient_loss | -0.0156 |\n", + "| value_loss | 2.29 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -4.47 |\n", + "| time/ | |\n", + "| fps | 129 |\n", + "| iterations | 8 |\n", + "| time_elapsed | 126 |\n", + "| total_timesteps | 16384 |\n", + "| train/ | |\n", + "| approx_kl | 0.014730865 |\n", + "| clip_fraction | 0.186 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -3.04 |\n", + "| explained_variance | 0.419 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 1.14 |\n", + "| n_updates | 70 |\n", + "| policy_gradient_loss | -0.0233 |\n", + "| value_loss | 2.11 |\n", + "-----------------------------------------\n", + "----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -4.18 |\n", + "| time/ | |\n", + "| fps | 119 |\n", + "| iterations | 9 |\n", + "| time_elapsed | 153 |\n", + "| total_timesteps | 18432 |\n", + "| train/ | |\n", + "| approx_kl | 0.01256018 |\n", + "| clip_fraction | 0.157 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -2.97 |\n", + "| explained_variance | 0.446 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.58 |\n", + "| n_updates | 80 |\n", + "| policy_gradient_loss | -0.0194 |\n", + "| value_loss | 1.93 |\n", + "----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -4.39 |\n", + "| time/ | |\n", + "| fps | 123 |\n", + "| iterations | 10 |\n", + "| time_elapsed | 165 |\n", + "| total_timesteps | 20480 |\n", + "| train/ | |\n", + "| approx_kl | 0.013073035 |\n", + "| clip_fraction | 0.147 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -2.91 |\n", + "| explained_variance | 0.491 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 1.04 |\n", + "| n_updates | 90 |\n", + "| policy_gradient_loss | -0.0176 |\n", + "| value_loss | 1.74 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -4.09 |\n", + "| time/ | |\n", + "| fps | 121 |\n", + "| iterations | 11 |\n", + "| time_elapsed | 184 |\n", + "| total_timesteps | 22528 |\n", + "| train/ | |\n", + "| approx_kl | 0.013191431 |\n", + "| clip_fraction | 0.155 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -2.84 |\n", + "| explained_variance | 0.501 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.98 |\n", + "| n_updates | 100 |\n", + "| policy_gradient_loss | -0.0172 |\n", + "| value_loss | 1.77 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -3.68 |\n", + "| time/ | |\n", + "| fps | 120 |\n", + "| iterations | 12 |\n", + "| time_elapsed | 204 |\n", + "| total_timesteps | 24576 |\n", + "| train/ | |\n", + "| approx_kl | 0.012647969 |\n", + "| clip_fraction | 0.144 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -2.78 |\n", + "| explained_variance | 0.463 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.848 |\n", + "| n_updates | 110 |\n", + "| policy_gradient_loss | -0.0164 |\n", + "| value_loss | 1.79 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -3.3 |\n", + "| time/ | |\n", + "| fps | 119 |\n", + "| iterations | 13 |\n", + "| time_elapsed | 223 |\n", + "| total_timesteps | 26624 |\n", + "| train/ | |\n", + "| approx_kl | 0.011612153 |\n", + "| clip_fraction | 0.134 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -2.71 |\n", + "| explained_variance | 0.523 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.832 |\n", + "| n_updates | 120 |\n", + "| policy_gradient_loss | -0.0164 |\n", + "| value_loss | 1.65 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -3.7 |\n", + "| time/ | |\n", + "| fps | 119 |\n", + "| iterations | 14 |\n", + "| time_elapsed | 239 |\n", + "| total_timesteps | 28672 |\n", + "| train/ | |\n", + "| approx_kl | 0.011285038 |\n", + "| clip_fraction | 0.111 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -2.63 |\n", + "| explained_variance | 0.502 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.433 |\n", + "| n_updates | 130 |\n", + "| policy_gradient_loss | -0.0131 |\n", + "| value_loss | 1.42 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -3.57 |\n", + "| time/ | |\n", + "| fps | 120 |\n", + "| iterations | 15 |\n", + "| time_elapsed | 254 |\n", + "| total_timesteps | 30720 |\n", + "| train/ | |\n", + "| approx_kl | 0.012549748 |\n", + "| clip_fraction | 0.14 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -2.58 |\n", + "| explained_variance | 0.55 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.344 |\n", + "| n_updates | 140 |\n", + "| policy_gradient_loss | -0.015 |\n", + "| value_loss | 1.45 |\n", + "-----------------------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from stable_baselines3 import PPO\n", + "from stable_baselines3.ppo import MlpPolicy\n", + "\n", + "# create the custom env with the custom handler (obs space) from step 2\n", + "env = CustomEnv(config={\"handler\": CustomHandler})\n", + "\n", + "# train PPO agent on environment. this takes a while\n", + "model = PPO(MlpPolicy, env, tensorboard_log='results_sb', verbose=1)\n", + "model.learn(total_timesteps=30000)" + ] }, { "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ - "Now, the observations include an additional entry per user that indicates whether the user is connected to any cell at all. Maybe that's useful for the RL approach?\n", + "To visualize training progress, we can use TensorBoard using `tensorboard --logdir results_sb`, i.e., pointing to the TensorBoard log directory that we defined above.\n", "\n", - "Let's try to train an RL agent with the custom observations on our custom environment.\n", - "\n", - "## Step 3: Single-Agent RL with `stable-baselines3`" - ], + "If we run it in another terminal, we can monitor training progress live. After training finishes, we can also show TensorBoard inside the notebook (works on Google CoLab):" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": { - "collapsed": false - } + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: tensorboard in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (2.7.0)\n", + "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (3.3.6)\n", + "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (2.3.3)\n", + "Requirement already satisfied: grpcio>=1.24.3 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (1.42.0)\n", + 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c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard) (3.1.1)\n" + ] + } + ], + "source": [ + "# install and load tensorboard\n", + "!pip install tensorboard\n", + "%load_ext tensorboard" + ] }, { "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], + "execution_count": 16, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Reusing TensorBoard on port 6006 (pid 19344), started 0:50:01 ago. (Use '!kill 19344' to kill it.)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# show training progress on tensorboard\n", + "%tensorboard --logdir results_sb" + ] }, { "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ - "## Step 4: Multi-Agent RL with Ray RLlib" + "Finally, we can also easily visualize the learned policy with `mobile-env`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } ], + "source": [ + "env = CustomEnv(config={\"handler\": CustomHandler})\n", + "obs = env.reset()\n", + "done = False\n", + "\n", + "# run one episode with the trained model\n", + "while not done:\n", + " action, _ = model.predict(obs)\n", + "\n", + " # perform step on simulation environment\n", + " obs, reward, done, info = env.step(action)\n", + "\n", + " # render environment as RGB\n", + " plt.imshow(env.render(mode='rgb_array'))\n", + " display.display(plt.gcf())\n", + " display.clear_output(wait=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4: Multi-Agent RL with Ray RLlib\n", + "\n", + "As alternative to controlling cell selection centrally for all users from a single RL agent, we can also use multi-agent RL, i.e., delegating control to multiple agents that act in parallel.\n", + "As an example, we could have each RL agent responsible for the cell selection of a single user. Then we would need as many agents as we have users.\n", + "That's what happens in the predefined multi-agent scenarios, e.g., `mobile-small-ma-v0`.\n", + "\n", + "To train a multi-agent approach, we can use Ray RLlib, which supports multi-agent RL out of the box. RLlib uses its own registry for RL environments, but `mobile-env` provides a wrapper that makes it easy to register an environment with RLlib." + ] + }, + { + "cell_type": "code", + "execution_count": 18, "metadata": { - "collapsed": false - } + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: ray[rllib] in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (1.8.0)\n", + "Requirement already satisfied: click>=7.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from ray[rllib]) (8.0.3)\n", + "Requirement already satisfied: grpcio>=1.28.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from ray[rllib]) (1.42.0)\n", + "Requirement already satisfied: attrs in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from 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setuptools-scm>=4->matplotlib!=3.4.3->ray[rllib]) (46.1.3)\n", + "Requirement already satisfied: tomli>=1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib!=3.4.3->ray[rllib]) (1.2.2)\n", + "Requirement already satisfied: wrapt<2,>=1.10 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from deprecated->redis>=3.5.0->ray[rllib]) (1.13.3)\n" + ] + } + ], + "source": [ + "# install ray RLlib\n", + "!pip install ray[rllib]" + ] }, { "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], + "execution_count": 21, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "from ray.tune.registry import register_env\n", + "\n", + "# use the mobile-env RLlib wrapper for RLlib\n", + "def register(config):\n", + " import mobile_env\n", + " from mobile_env.wrappers.multi_agent import RLlibMAWrapper\n", + "\n", + " env = gym.make(\"mobile-small-ma-v0\")\n", + " return RLlibMAWrapper(env)\n", + "\n", + "# register the predefined scenario with RLlib\n", + "register_env(\"mobile-small-ma-v0\", register)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, that the predefined scenario is registered with RLlib, we can configure and train a multi-agent PPO approach on the scenario with RLlib." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ray\n", + "from ray.rllib.agents import ppo\n", + "\n", + "stop = {\"timesteps_total\": 10000}\n", + "\n", + "config = {\n", + " # environment configuration:\n", + " \"env\": \"mobile-small-ma-v0\",\n", + "\n", + " # multi-agent configuration (here with shared policy)\n", + " \"multiagent\": {\n", + " \"policies\": {\"shared_policy\"},\n", + " \"policy_mapping_fn\": (\n", + " lambda agent_id, **kwargs: \"shared_policy\"),\n", + " },\n", + "}\n", + "save_dir = \".\"\n", + "\n", + "# set available CPUs (and GPUs) and init ray\n", + "ray.init(\n", + " num_cpus=3,\n", + " include_dashboard=False,\n", + " ignore_reinit_error=True,\n", + " log_to_driver=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "analysis = ray.tune.run(ppo.PPOTrainer, config=config, local_dir=save_dir, stop=stop, checkpoint_at_end=True)" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.2" } }, "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "nbformat_minor": 4 +} diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb index b6f03d1..00b87e9 100644 --- a/examples/tutorial.ipynb +++ b/examples/tutorial.ipynb @@ -2,6 +2,8 @@ "cells": [ { "cell_type": "markdown", + "id": "7c6a211c", + "metadata": {}, "source": [ "# mobile-env: An Open Environment for Autonomous Coordination in Mobile Networks\n", "\n", @@ -13,10 +15,7 @@ "First, we train a multi-agent policy with **RLlib**.\n", "\n", "Second, we train a central policy with **stable-baselines3**." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", @@ -299,80 +298,190 @@ "outputs": [ { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:32:13 (running for 00:00:00.14)
Memory usage on this node: 9.8/11.9 GiB
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Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
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Current time: 2021-11-24 12:32:13 (running for 00:00:00.14)
Memory usage on this node: 9.8/11.9 GiB
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Resources requested: 0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
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Current time: 2021-11-24 12:32:43 (running for 00:00:30.03)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
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Current time: 2021-11-24 12:32:43 (running for 00:00:30.03)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
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Using FIFO scheduling algorithm.
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Current time: 2021-11-24 12:32:44 (running for 00:00:31.07)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
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Using FIFO scheduling algorithm.
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Current time: 2021-11-24 12:32:49 (running for 00:00:36.09)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
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Current time: 2021-11-24 12:32:54 (running for 00:00:41.10)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Current time: 2021-11-24 12:32:59 (running for 00:00:46.12)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
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Current time: 2021-11-24 12:32:59 (running for 00:00:46.12)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
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Current time: 2021-11-24 12:33:04 (running for 00:00:51.21)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
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Current time: 2021-11-24 12:33:04 (running for 00:00:51.21)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
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Current time: 2021-11-24 12:33:09 (running for 00:00:56.26)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
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Current time: 2021-11-24 12:33:09 (running for 00:00:56.26)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
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Using FIFO scheduling algorithm.
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Current time: 2021-11-24 12:33:14 (running for 00:01:01.36)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
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Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
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Current time: 2021-11-24 12:33:20 (running for 00:01:06.40)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -456,56 +565,133 @@ }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:33:25 (running for 00:01:12.07)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:33:25 (running for 00:01:12.07)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:33:30 (running for 00:01:17.11)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:33:30 (running for 00:01:17.11)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:33:36 (running for 00:01:23.13)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:33:36 (running for 00:01:23.13)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:33:41 (running for 00:01:28.14)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:33:41 (running for 00:01:28.14)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:33:46 (running for 00:01:33.21)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:33:46 (running for 00:01:33.21)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:33:51 (running for 00:01:38.26)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:33:51 (running for 00:01:38.26)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:33:56 (running for 00:01:43.28)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:33:56 (running for 00:01:43.28)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 1 36.92784000 -197.17 -82.5853 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -590,56 +776,133 @@ }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:34:02 (running for 00:01:49.27)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:02 (running for 00:01:49.27)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:34:07 (running for 00:01:54.34)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:07 (running for 00:01:54.34)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:34:13 (running for 00:01:59.41)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:13 (running for 00:01:59.41)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:34:18 (running for 00:02:04.48)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:18 (running for 00:02:04.48)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:34:23 (running for 00:02:09.57)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:23 (running for 00:02:09.57)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:34:28 (running for 00:02:14.65)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:28 (running for 00:02:14.65)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


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Current time: 2021-11-24 12:34:33 (running for 00:02:19.69)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:33 (running for 00:02:19.69)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 2 74.06458000-156.007 26.011 -324.75 100


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Current time: 2021-11-24 12:34:38 (running for 00:02:24.82)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:38 (running for 00:02:24.82)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


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Current time: 2021-11-24 12:34:43 (running for 00:02:29.84)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:43 (running for 00:02:29.84)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


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Current time: 2021-11-24 12:34:49 (running for 00:02:35.89)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:49 (running for 00:02:35.89)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


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Current time: 2021-11-24 12:34:54 (running for 00:02:40.90)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:54 (running for 00:02:40.90)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


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Current time: 2021-11-24 12:34:59 (running for 00:02:45.97)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:34:59 (running for 00:02:45.97)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:35:04 (running for 00:02:51.02)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:35:04 (running for 00:02:51.02)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


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Current time: 2021-11-24 12:35:09 (running for 00:02:56.10)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:35:09 (running for 00:02:56.10)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


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Current time: 2021-11-24 12:35:14 (running for 00:03:01.15)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:35:14 (running for 00:03:01.15)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 3 113.56212000-123.123 26.011 -324.75 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -866,56 +1217,133 @@ }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:35:19 (running for 00:03:06.31)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:35:19 (running for 00:03:06.31)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:35:24 (running for 00:03:11.33)
Memory usage on this node: 10.4/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:35:24 (running for 00:03:11.33)
Memory usage on this node: 10.4/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:35:29 (running for 00:03:16.35)
Memory usage on this node: 10.4/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:35:29 (running for 00:03:16.35)
Memory usage on this node: 10.4/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:35:34 (running for 00:03:21.36)
Memory usage on this node: 10.4/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


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Current time: 2021-11-24 12:35:34 (running for 00:03:21.36)
Memory usage on this node: 10.4/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


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Current time: 2021-11-24 12:35:40 (running for 00:03:26.44)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:35:40 (running for 00:03:26.44)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


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Current time: 2021-11-24 12:35:45 (running for 00:03:31.67)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:35:45 (running for 00:03:31.67)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


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Current time: 2021-11-24 12:35:50 (running for 00:03:36.79)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:35:50 (running for 00:03:36.79)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 4 150.94116000-87.9173 26.011 -308.379 100


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Current time: 2021-11-24 12:35:56 (running for 00:03:42.62)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


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Current time: 2021-11-24 12:35:56 (running for 00:03:42.62)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


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Current time: 2021-11-24 12:36:02 (running for 00:03:48.64)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:36:02 (running for 00:03:48.64)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


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Current time: 2021-11-24 12:36:07 (running for 00:03:53.68)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:36:07 (running for 00:03:53.68)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


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Current time: 2021-11-24 12:36:12 (running for 00:03:58.98)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:36:12 (running for 00:03:58.98)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


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Current time: 2021-11-24 12:36:17 (running for 00:04:04.20)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:36:17 (running for 00:04:04.20)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


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Current time: 2021-11-24 12:36:22 (running for 00:04:09.33)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:36:22 (running for 00:04:09.33)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


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Current time: 2021-11-24 12:36:27 (running for 00:04:14.38)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:36:27 (running for 00:04:14.38)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


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Current time: 2021-11-24 12:36:33 (running for 00:04:19.54)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:36:33 (running for 00:04:19.54)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


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Current time: 2021-11-24 12:36:38 (running for 00:04:24.57)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:36:38 (running for 00:04:24.57)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 5 190.25120000-74.0203 27.924 -308.379 100


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Current time: 2021-11-24 12:36:43 (running for 00:04:29.98)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:36:43 (running for 00:04:29.98)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:36:48 (running for 00:04:35.01)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:36:48 (running for 00:04:35.01)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:36:53 (running for 00:04:40.05)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:36:53 (running for 00:04:40.05)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:36:58 (running for 00:04:45.08)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:36:58 (running for 00:04:45.08)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:37:03 (running for 00:04:50.16)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:37:03 (running for 00:04:50.16)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:37:08 (running for 00:04:55.22)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:37:08 (running for 00:04:55.22)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 6 234.50924000-63.0578 32.0051 -166.787 100


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Current time: 2021-11-24 12:37:14 (running for 00:05:00.86)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


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Current time: 2021-11-24 12:37:14 (running for 00:05:00.86)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


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Current time: 2021-11-24 12:37:19 (running for 00:05:05.90)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


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Current time: 2021-11-24 12:37:19 (running for 00:05:05.90)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


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Current time: 2021-11-24 12:37:24 (running for 00:05:10.97)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:37:24 (running for 00:05:10.97)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


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Current time: 2021-11-24 12:37:29 (running for 00:05:16.02)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:37:29 (running for 00:05:16.02)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
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Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


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Current time: 2021-11-24 12:37:34 (running for 00:05:21.09)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:37:34 (running for 00:05:21.09)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


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Current time: 2021-11-24 12:37:39 (running for 00:05:26.15)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:37:39 (running for 00:05:26.15)
Memory usage on this node: 10.5/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


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Current time: 2021-11-24 12:37:44 (running for 00:05:31.29)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:37:44 (running for 00:05:31.29)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


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Current time: 2021-11-24 12:37:49 (running for 00:05:36.36)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:37:49 (running for 00:05:36.36)
Memory usage on this node: 10.6/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 7 268.33228000-56.8994 37.5174 -159.397 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -1418,56 +2099,133 @@ }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:37:55 (running for 00:05:41.65)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:37:55 (running for 00:05:41.65)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:00 (running for 00:05:46.68)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:00 (running for 00:05:46.68)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:05 (running for 00:05:51.74)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:05 (running for 00:05:51.74)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:10 (running for 00:05:56.78)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:10 (running for 00:05:56.78)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:15 (running for 00:06:01.87)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:15 (running for 00:06:01.87)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:20 (running for 00:06:06.96)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:20 (running for 00:06:06.96)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:25 (running for 00:06:12.04)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:25 (running for 00:06:12.04)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 8 306.96832000-44.2855 37.5174 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -1552,56 +2310,133 @@ }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:31 (running for 00:06:17.60)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:31 (running for 00:06:17.60)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:36 (running for 00:06:22.66)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:36 (running for 00:06:22.66)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:41 (running for 00:06:27.71)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:41 (running for 00:06:27.71)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:46 (running for 00:06:32.77)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:46 (running for 00:06:32.77)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:51 (running for 00:06:37.81)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:51 (running for 00:06:37.81)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:38:56 (running for 00:06:42.89)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:38:56 (running for 00:06:42.89)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:01 (running for 00:06:47.94)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:01 (running for 00:06:47.94)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 9 345.89736000-36.2633 28.0141 -307.404 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -1686,64 +2521,152 @@ }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:06 (running for 00:06:53.05)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:06 (running for 00:06:53.05)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:11 (running for 00:06:58.08)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:11 (running for 00:06:58.08)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:16 (running for 00:07:03.14)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:16 (running for 00:07:03.14)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:21 (running for 00:07:08.20)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:21 (running for 00:07:08.20)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:26 (running for 00:07:13.25)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:26 (running for 00:07:13.25)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:31 (running for 00:07:18.26)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:31 (running for 00:07:18.26)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:37 (running for 00:07:23.41)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:37 (running for 00:07:23.41)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:42 (running for 00:07:28.49)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:42 (running for 00:07:28.49)
Memory usage on this node: 10.7/11.9 GiB
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 10 381.27540000-32.0991 33.7207 -301.054 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -1828,64 +2751,152 @@ }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:47 (running for 00:07:33.95)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:47 (running for 00:07:33.95)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:52 (running for 00:07:39.04)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:52 (running for 00:07:39.04)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:39:57 (running for 00:07:44.15)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:39:57 (running for 00:07:44.15)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:40:02 (running for 00:07:49.21)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:40:02 (running for 00:07:49.21)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:40:07 (running for 00:07:54.27)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:40:07 (running for 00:07:54.27)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:40:12 (running for 00:07:59.33)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:40:12 (running for 00:07:59.33)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:40:18 (running for 00:08:04.52)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:40:18 (running for 00:08:04.52)
Memory usage on this node: 10.7/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:40:23 (running for 00:08:09.59)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:40:23 (running for 00:08:09.59)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 11 418.42344000-25.0044 76.6098 -156.68 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -1970,88 +2981,209 @@ }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:40:29 (running for 00:08:15.51)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:40:29 (running for 00:08:15.51)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:40:34 (running for 00:08:20.55)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:40:34 (running for 00:08:20.55)
Memory usage on this node: 10.8/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:40:39 (running for 00:08:25.62)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:40:39 (running for 00:08:25.62)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:40:44 (running for 00:08:30.74)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:40:44 (running for 00:08:30.74)
Memory usage on this node: 10.9/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:40:50 (running for 00:08:36.40)
Memory usage on this node: 11.0/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:40:50 (running for 00:08:36.40)
Memory usage on this node: 11.0/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:40:55 (running for 00:08:41.53)
Memory usage on this node: 11.2/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:40:55 (running for 00:08:41.53)
Memory usage on this node: 11.2/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:41:00 (running for 00:08:46.72)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:41:00 (running for 00:08:46.72)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:41:05 (running for 00:08:51.87)
Memory usage on this node: 11.2/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:41:05 (running for 00:08:51.87)
Memory usage on this node: 11.2/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:41:10 (running for 00:08:56.95)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:41:10 (running for 00:08:56.95)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:41:15 (running for 00:09:02.01)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:41:15 (running for 00:09:02.01)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:41:20 (running for 00:09:07.10)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:41:20 (running for 00:09:07.10)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 3.0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 RUNNING)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000RUNNING 127.0.0.1:7956 12 461.52548000-25.1392 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -2136,8 +3268,19 @@ }, { "data": { - "text/plain": "", - "text/html": "== Status ==
Current time: 2021-11-24 12:41:23 (running for 00:09:09.61)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 TERMINATED)
\n\n\n\n\n\n\n
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000TERMINATED127.0.0.1:7956 13 518.47552000-35.4615 76.6098 -158.713 100


" + "text/html": [ + "== Status ==
Current time: 2021-11-24 12:41:23 (running for 00:09:09.61)
Memory usage on this node: 11.3/11.9 GiB: ***LOW MEMORY*** less than 10% of the memory on this node is available for use. This can cause unexpected crashes. Consider reducing the memory used by your application or reducing the Ray object store size by setting `object_store_memory` when calling `ray.init`.
Using FIFO scheduling algorithm.
Resources requested: 0/3 CPUs, 0/0 GPUs, 0.0/1.51 GiB heap, 0.0/0.75 GiB objects
Result logdir: C:\\Users\\Stefan\\git-repos\\work\\mobile-env\\examples\\PPO_2021-11-24_12-32-13
Number of trials: 1/1 (1 TERMINATED)
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
Trial name status loc iter total time (s) ts reward episode_reward_max episode_reward_min episode_len_mean
PPO_mobile-small-ma-v0_2b82d_00000TERMINATED127.0.0.1:7956 13 518.47552000-35.4615 76.6098 -158.713 100


" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -2170,15 +3313,35 @@ "outputs": [ { "data": { - "text/plain": "Launching TensorBoard..." + "text/plain": [ + "Launching TensorBoard..." + ] }, "metadata": {}, "output_type": "display_data" }, { "data": { - "text/plain": "", - "text/html": "\n \n \n " + "text/html": [ + "\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -2212,23 +3375,23 @@ }, { "ename": "RayActorError", - "evalue": "The actor died because of an error raised in its creation task, \u001B[36mray::RolloutWorker.__init__()\u001B[39m (pid=18304, ip=127.0.0.1)\n File \"python\\ray\\_raylet.pyx\", line 565, in ray._raylet.execute_task\n File \"python\\ray\\_raylet.pyx\", line 569, in ray._raylet.execute_task\n File \"python\\ray\\_raylet.pyx\", line 519, in ray._raylet.execute_task.function_executor\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\_private\\function_manager.py\", line 576, in actor_method_executor\n return method(__ray_actor, *args, **kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 584, in __init__\n self._build_policy_map(\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 1384, in _build_policy_map\n self.policy_map.create_policy(name, orig_cls, obs_space, act_space,\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\policy\\policy_map.py\", line 123, in create_policy\n sess = self.session_creator()\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\worker_set.py\", line 323, in session_creator\n return tf1.Session(\nAttributeError: 'NoneType' object has no attribute 'Session'", + "evalue": "The actor died because of an error raised in its creation task, \u001b[36mray::RolloutWorker.__init__()\u001b[39m (pid=18304, ip=127.0.0.1)\n File \"python\\ray\\_raylet.pyx\", line 565, in ray._raylet.execute_task\n File \"python\\ray\\_raylet.pyx\", line 569, in ray._raylet.execute_task\n File \"python\\ray\\_raylet.pyx\", line 519, in ray._raylet.execute_task.function_executor\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\_private\\function_manager.py\", line 576, in actor_method_executor\n return method(__ray_actor, *args, **kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 584, in __init__\n self._build_policy_map(\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 1384, in _build_policy_map\n self.policy_map.create_policy(name, orig_cls, obs_space, act_space,\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\policy\\policy_map.py\", line 123, in create_policy\n sess = self.session_creator()\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\worker_set.py\", line 323, in session_creator\n return tf1.Session(\nAttributeError: 'NoneType' object has no attribute 'Session'", "output_type": "error", "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mRayActorError\u001B[0m Traceback (most recent call last)", - "\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_1344/335420695.py\u001B[0m in \u001B[0;36m\u001B[1;34m\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[0mcheckpoint\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0manalysis\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_last_checkpoint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 2\u001B[1;33m \u001B[0mmodel\u001B[0m \u001B[1;33m=\u001B[0m 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"\u001B[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\agents\\trainer_template.py\u001B[0m in \u001B[0;36msetup\u001B[1;34m(self, config)\u001B[0m\n\u001B[0;32m 145\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_override_all_subkeys_if_type_changes\u001B[0m \u001B[1;33m+=\u001B[0m\u001B[0;31m \u001B[0m\u001B[0;31m\\\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 146\u001B[0m \u001B[0moverride_all_subkeys_if_type_changes\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 147\u001B[1;33m \u001B[0msuper\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msetup\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mconfig\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 148\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 149\u001B[0m def _init(self, config: TrainerConfigDict,\n", - "\u001B[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\agents\\trainer.py\u001B[0m in \u001B[0;36msetup\u001B[1;34m(self, config)\u001B[0m\n\u001B[0;32m 774\u001B[0m \u001B[0mlogging\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mgetLogger\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"ray.rllib\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msetLevel\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mconfig\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"log_level\"\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 775\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 776\u001B[1;33m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_init\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mconfig\u001B[0m\u001B[1;33m,\u001B[0m 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"\u001B[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\worker.py\u001B[0m in \u001B[0;36mget\u001B[1;34m(object_refs, timeout)\u001B[0m\n\u001B[0;32m 1625\u001B[0m \u001B[1;32mraise\u001B[0m \u001B[0mvalue\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mas_instanceof_cause\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 1626\u001B[0m \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1627\u001B[1;33m \u001B[1;32mraise\u001B[0m \u001B[0mvalue\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 1628\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 1629\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mis_individual_id\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n", - "\u001B[1;31mRayActorError\u001B[0m: The actor died because of an error raised in its creation task, \u001B[36mray::RolloutWorker.__init__()\u001B[39m (pid=18304, ip=127.0.0.1)\n File \"python\\ray\\_raylet.pyx\", line 565, in ray._raylet.execute_task\n File \"python\\ray\\_raylet.pyx\", line 569, in ray._raylet.execute_task\n File \"python\\ray\\_raylet.pyx\", line 519, in ray._raylet.execute_task.function_executor\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\_private\\function_manager.py\", line 576, in actor_method_executor\n return method(__ray_actor, *args, **kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 584, in __init__\n self._build_policy_map(\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 1384, in _build_policy_map\n self.policy_map.create_policy(name, orig_cls, obs_space, act_space,\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\policy\\policy_map.py\", line 123, in create_policy\n sess = self.session_creator()\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\worker_set.py\", line 323, in session_creator\n return tf1.Session(\nAttributeError: 'NoneType' object has no attribute 'Session'" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mRayActorError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_1344/335420695.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mcheckpoint\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0manalysis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_last_checkpoint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mppo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPPOTrainer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'mobile-small-ma-v0'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m 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logger_creator)\u001b[0m\n\u001b[0;32m 621\u001b[0m \u001b[0mlogger_creator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefault_logger_creator\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 622\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 623\u001b[1;33m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogger_creator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 624\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 625\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\tune\\trainable.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, config, logger_creator)\u001b[0m\n\u001b[0;32m 105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 107\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 108\u001b[0m \u001b[0msetup_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 109\u001b[0m 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setup.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\agents\\trainer_template.py\u001b[0m in \u001b[0;36m_init\u001b[1;34m(self, config, env_creator)\u001b[0m\n\u001b[0;32m 169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 170\u001b[0m \u001b[1;31m# Creating all workers (excluding evaluation workers).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 171\u001b[1;33m self.workers = self._make_workers(\n\u001b[0m\u001b[0;32m 172\u001b[0m \u001b[0menv_creator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0menv_creator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 173\u001b[0m \u001b[0mvalidate_env\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidate_env\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\agents\\trainer.py\u001b[0m in \u001b[0;36m_make_workers\u001b[1;34m(self, env_creator, validate_env, policy_class, config, num_workers)\u001b[0m\n\u001b[0;32m 856\u001b[0m \u001b[0mWorkerSet\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mcreated\u001b[0m \u001b[0mWorkerSet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 857\u001b[0m \"\"\"\n\u001b[1;32m--> 858\u001b[1;33m return WorkerSet(\n\u001b[0m\u001b[0;32m 859\u001b[0m \u001b[0menv_creator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0menv_creator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 860\u001b[0m \u001b[0mvalidate_env\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidate_env\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\worker_set.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, env_creator, validate_env, policy_class, trainer_config, num_workers, logdir, _setup)\u001b[0m\n\u001b[0;32m 85\u001b[0m (not trainer_config.get(\"observation_space\") or\n\u001b[0;32m 86\u001b[0m not trainer_config.get(\"action_space\")):\n\u001b[1;32m---> 87\u001b[1;33m remote_spaces = ray.get(self.remote_workers(\n\u001b[0m\u001b[0;32m 88\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforeach_policy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremote\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 89\u001b[0m lambda p, pid: (pid, p.observation_space, p.action_space)))\n", + "\u001b[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\_private\\client_mode_hook.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"init\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_client_mode_enabled_by_default\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 105\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 107\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\worker.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(object_refs, timeout)\u001b[0m\n\u001b[0;32m 1625\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_instanceof_cause\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1626\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1627\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1628\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1629\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_individual_id\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mRayActorError\u001b[0m: The actor died because of an error raised in its creation task, \u001b[36mray::RolloutWorker.__init__()\u001b[39m (pid=18304, ip=127.0.0.1)\n File \"python\\ray\\_raylet.pyx\", line 565, in ray._raylet.execute_task\n File \"python\\ray\\_raylet.pyx\", line 569, in ray._raylet.execute_task\n File \"python\\ray\\_raylet.pyx\", line 519, in ray._raylet.execute_task.function_executor\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\_private\\function_manager.py\", line 576, in actor_method_executor\n return method(__ray_actor, *args, **kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 584, in __init__\n self._build_policy_map(\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\util\\tracing\\tracing_helper.py\", line 451, in _resume_span\n return method(self, *_args, **_kwargs)\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\rollout_worker.py\", line 1384, in _build_policy_map\n self.policy_map.create_policy(name, orig_cls, obs_space, act_space,\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\policy\\policy_map.py\", line 123, in create_policy\n sess = self.session_creator()\n File \"c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages\\ray\\rllib\\evaluation\\worker_set.py\", line 323, in session_creator\n return tf1.Session(\nAttributeError: 'NoneType' object has no attribute 'Session'" ] } ], @@ -2402,9 +3565,9 @@ ], "metadata": { "kernelspec": { - "name": "python3", + "display_name": "Python 3 (ipykernel)", "language": "python", - "display_name": "Python 3 (ipykernel)" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2416,9 +3579,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From da74bedec87c9085939925608f163b267a4969df Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Mon, 13 Dec 2021 16:53:16 +0100 Subject: [PATCH 13/14] complete first draft of demo --- examples/demo.ipynb | 1980 +++++++++++++++++++++++++++++++++++++------ 1 file changed, 1736 insertions(+), 244 deletions(-) diff --git a/examples/demo.ipynb b/examples/demo.ipynb index c183791..1260a43 100644 --- a/examples/demo.ipynb +++ b/examples/demo.ipynb @@ -24,7 +24,7 @@ "\n", "\n", "\n", - "## Demonstration Steps\n", + "**Demonstration Steps:**\n", "\n", "This demonstration consists of the following steps:\n", "\n", @@ -34,7 +34,7 @@ "4. Training a multi-agent reinforcement learning approach with [Ray RLlib](https://docs.ray.io/en/latest/rllib.html)\n", "\n", "\n", - "### Step 1: Install and Test `mobile-env` With Dummy Actions\n", + "## Step 1: Install and Test `mobile-env` With Dummy Actions\n", "\n", "Installing `mobile_env` via PyPI is very simple:" ] @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false, "jupyter": { @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": false, "jupyter": { @@ -168,7 +168,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -216,9 +216,9 @@ "\n", "\n", "\n", - "### Step 2: Configure `mobile-env` (Optional)\n", + "## Step 2: Configure `mobile-env` (Optional)\n", "\n", - "#### Predefined Scenarios\n", + "### Predefined Scenarios\n", "\n", "Configuration is optional.\n", "We can also choose between one of the [three predefined scenarios](https://mobile-env.readthedocs.io/en/latest/components.html#scenarios) (small, medium, and large) without having to configure anything.\n", @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { @@ -263,7 +263,7 @@ " 'utility_params': {'lower': -20, 'upper': 20, 'coeffs': (10, 0, 10)}}" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -278,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": false, "jupyter": { @@ -330,7 +330,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -347,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { @@ -360,7 +360,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -383,7 +383,7 @@ } }, "source": [ - "#### Custom Scenario\n", + "### Custom Scenario\n", "\n", "It is also easy to define a custom scenario by subclassing `mobile-env`'s `MComCore` base class.\n", "\n", @@ -408,7 +408,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -456,12 +456,12 @@ "\n", " # users\n", " users = [\n", - " # stationary user --> set velocity to 0\n", - " UserEquipment(ue_id=1, velocity=0, snr_tr=env_config[\"ue\"][\"snr_tr\"], noise=env_config[\"ue\"][\"noise\"],\n", - " height=env_config[\"ue\"][\"height\"]),\n", " # two fast moving users with config defaults\n", + " UserEquipment(ue_id=1, **env_config[\"ue\"]),\n", " UserEquipment(ue_id=2, **env_config[\"ue\"]),\n", - " UserEquipment(ue_id=3, **env_config[\"ue\"]),\n", + " # stationary user --> set velocity to 0\n", + " UserEquipment(ue_id=3, velocity=0, snr_tr=env_config[\"ue\"][\"snr_tr\"], noise=env_config[\"ue\"][\"noise\"],\n", + " height=env_config[\"ue\"][\"height\"]),\n", " ]\n", "\n", " super().__init__(stations, users, config)\n", @@ -485,7 +485,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Extending `mobile-env`: Adding a Handler for a Custom Observation Space\n", + "### Extending `mobile-env`: Adding a Handler for a Custom Observation Space\n", "\n", "Handlers define the observation and action spaces as well as the reward for an environment.\n", "Hence, when designing a new Markov Decision Process for a reinforcement learning approach, you can quickly validate it by implementing a new handler in `mobile-env`.\n", @@ -548,23 +548,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Raw observations: [ 0. 0. 0.38170958 1. -1. 0.\n", - " 1. 1. 0.2665389 -0.08450864 0. 0.\n", - " 0.34874913 1. -1. ]\n", + "Raw observations: [ 0. 0. 0.3470676 1. -1. 0.\n", + " 0. 1. 0.2665389 -1. 0. 0.\n", + " 0.38201445 1. -1. ]\n", "\n", "Observations for user 1:\n", "Current connections to the 2 cells: [0. 0.]\n", - "SNR to the 2 cells: [0.38170958 1. ]\n", + "SNR to the 2 cells: [0.3470676 1. ]\n", "Current utility: -1.0\n", "\n", "Observations for user 2:\n", - "Current connections to the 2 cells: [0. 1.]\n", + "Current connections to the 2 cells: [0. 0.]\n", "SNR to the 2 cells: [1. 0.2665389]\n", - "Current utility: -0.08450863510370255\n", + "Current utility: -1.0\n", "\n", "Observations for user 3:\n", "Current connections to the 2 cells: [0. 0.]\n", - "SNR to the 2 cells: [0.34874913 1. ]\n", + "SNR to the 2 cells: [0.38201445 1. ]\n", "Current utility: -1.0\n" ] } @@ -665,25 +665,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "New, raw observations: [0.0, 0.0, 0, 0.42142308, 1.0, -1.0, 0.0, 0.0, 0, 0.08438771, 1.0, -1.0, 0.0, 0.0, 0, 1.0, 0.059552114, -1.0]\n", + "New, raw observations: [0.0, 0.0, 0, 0.39157578, 1.0, -1.0, 0.0, 1.0, 1, 0.08438771, 1.0, -0.14720577, 1.0, 0.0, 1, 1.0, 0.021902867, 0.65321183]\n", "\n", "Observations for user 1:\n", "Current connections to the 2 cells: [0.0, 0.0]\n", "NEW: Any connection?: 0\n", - "SNR to the 2 cells: [0.42142308, 1.0]\n", + "SNR to the 2 cells: [0.39157578, 1.0]\n", "Current utility: -1.0\n", "\n", "Observations for user 2:\n", - "Current connections to the 2 cells: [0.0, 0.0]\n", - "NEW: Any connection?: 0\n", + "Current connections to the 2 cells: [0.0, 1.0]\n", + "NEW: Any connection?: 1\n", "SNR to the 2 cells: [0.08438771, 1.0]\n", - "Current utility: -1.0\n", + "Current utility: -0.14720577001571655\n", "\n", "Observations for user 3:\n", - "Current connections to the 2 cells: [0.0, 0.0]\n", - "NEW: Any connection?: 0\n", - "SNR to the 2 cells: [1.0, 0.059552114]\n", - "Current utility: -1.0\n" + "Current connections to the 2 cells: [1.0, 0.0]\n", + "NEW: Any connection?: 1\n", + "SNR to the 2 cells: [1.0, 0.021902867]\n", + "Current utility: 0.6532118320465088\n" ] } ], @@ -719,7 +719,9 @@ "\n", "In a single-agent approach, the single RL agent controls cell selection for all users simultaneously.\n", "We can train such a single-agent approach with many different RL frameworks.\n", - "A popular example is `stable-baselines3`, using the PPO algorithm." + "A popular example is `stable-baselines3`, using the PPO algorithm.\n", + "\n", + "### Set Up `stable-baselines3`" ] }, { @@ -740,21 +742,21 @@ "output_type": "stream", "text": [ "Requirement already satisfied: stable-baselines3 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (1.3.0)\n", - "Requirement already satisfied: gym<0.20,>=0.17 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (0.19.0)\n", - "Requirement already satisfied: matplotlib in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (3.5.0)\n", - "Requirement already satisfied: torch>=1.8.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (1.10.0)\n", "Requirement already satisfied: numpy in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (1.21.4)\n", - "Requirement already satisfied: cloudpickle in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (1.6.0)\n", "Requirement already satisfied: pandas in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (1.2.3)\n", + "Requirement already satisfied: cloudpickle in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (1.6.0)\n", + "Requirement already satisfied: torch>=1.8.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (1.10.0)\n", + "Requirement already satisfied: matplotlib in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (3.5.0)\n", + "Requirement already satisfied: gym<0.20,>=0.17 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from stable-baselines3) (0.19.0)\n", "Requirement already satisfied: typing-extensions in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from torch>=1.8.1->stable-baselines3) (4.0.0)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (1.3.1)\n", - "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (2.4.7)\n", - "Requirement already satisfied: packaging>=20.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (21.3)\n", "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (8.4.0)\n", - "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (2.8.1)\n", "Requirement already satisfied: setuptools-scm>=4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (6.3.2)\n", - "Requirement already satisfied: cycler>=0.10 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (0.10.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (2.4.7)\n", + "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (2.8.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (4.28.1)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (21.3)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (1.3.1)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from matplotlib->stable-baselines3) (0.10.0)\n", "Requirement already satisfied: pytz>=2017.3 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from pandas->stable-baselines3) (2021.1)\n", "Requirement already satisfied: six in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from cycler>=0.10->matplotlib->stable-baselines3) (1.15.0)\n", "Requirement already satisfied: tomli>=1.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from setuptools-scm>=4->matplotlib->stable-baselines3) (1.2.2)\n", @@ -767,9 +769,16 @@ "!pip install stable-baselines3" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train a Single PPO Agent" + ] + }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 14, "metadata": { "collapsed": false, "jupyter": { @@ -787,320 +796,320 @@ "Using cpu device\n", "Wrapping the env with a `Monitor` wrapper\n", "Wrapping the env in a DummyVecEnv.\n", - "Logging to results_sb\\PPO_3\n", + "Logging to results_sb\\PPO_1\n", "---------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -5.52 |\n", + "| ep_rew_mean | -5.47 |\n", "| time/ | |\n", - "| fps | 141 |\n", + "| fps | 168 |\n", "| iterations | 1 |\n", - "| time_elapsed | 14 |\n", + "| time_elapsed | 12 |\n", "| total_timesteps | 2048 |\n", "---------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -5.55 |\n", + "| ep_rew_mean | -5.83 |\n", "| time/ | |\n", - "| fps | 156 |\n", + "| fps | 187 |\n", "| iterations | 2 |\n", - "| time_elapsed | 26 |\n", + "| time_elapsed | 21 |\n", "| total_timesteps | 4096 |\n", "| train/ | |\n", - "| approx_kl | 0.011195142 |\n", - "| clip_fraction | 0.121 |\n", + "| approx_kl | 0.011307748 |\n", + "| clip_fraction | 0.119 |\n", "| clip_range | 0.2 |\n", "| entropy_loss | -3.29 |\n", - "| explained_variance | 0.023 |\n", + "| explained_variance | -0.0886 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 0.979 |\n", + "| loss | 0.845 |\n", "| n_updates | 10 |\n", - "| policy_gradient_loss | -0.0161 |\n", - "| value_loss | 2.42 |\n", + "| policy_gradient_loss | -0.015 |\n", + "| value_loss | 2.43 |\n", "-----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -5.14 |\n", + "| ep_rew_mean | -5.18 |\n", "| time/ | |\n", - "| fps | 156 |\n", + "| fps | 197 |\n", "| iterations | 3 |\n", - "| time_elapsed | 39 |\n", + "| time_elapsed | 31 |\n", "| total_timesteps | 6144 |\n", "| train/ | |\n", - "| approx_kl | 0.010791814 |\n", - "| clip_fraction | 0.11 |\n", + "| approx_kl | 0.010906098 |\n", + "| clip_fraction | 0.0975 |\n", "| clip_range | 0.2 |\n", "| entropy_loss | -3.27 |\n", - "| explained_variance | 0.296 |\n", + "| explained_variance | 0.283 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 1.53 |\n", + "| loss | 1.11 |\n", "| n_updates | 20 |\n", - "| policy_gradient_loss | -0.0168 |\n", - "| value_loss | 2.6 |\n", + "| policy_gradient_loss | -0.0166 |\n", + "| value_loss | 2.71 |\n", "-----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -5.18 |\n", + "| ep_rew_mean | -5.6 |\n", "| time/ | |\n", - "| fps | 159 |\n", + "| fps | 201 |\n", "| iterations | 4 |\n", - "| time_elapsed | 51 |\n", + "| time_elapsed | 40 |\n", "| total_timesteps | 8192 |\n", "| train/ | |\n", - "| approx_kl | 0.012837088 |\n", - "| clip_fraction | 0.142 |\n", + "| approx_kl | 0.012331916 |\n", + "| clip_fraction | 0.132 |\n", "| clip_range | 0.2 |\n", - "| entropy_loss | -3.24 |\n", - "| explained_variance | 0.33 |\n", + "| entropy_loss | -3.25 |\n", + "| explained_variance | 0.327 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 1.15 |\n", + "| loss | 1.3 |\n", "| n_updates | 30 |\n", - "| policy_gradient_loss | -0.0207 |\n", - "| value_loss | 2.4 |\n", + "| policy_gradient_loss | -0.0193 |\n", + "| value_loss | 2.51 |\n", "-----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -5.07 |\n", + "| ep_rew_mean | -5.09 |\n", "| time/ | |\n", - "| fps | 158 |\n", + "| fps | 202 |\n", "| iterations | 5 |\n", - "| time_elapsed | 64 |\n", + "| time_elapsed | 50 |\n", "| total_timesteps | 10240 |\n", "| train/ | |\n", - "| approx_kl | 0.011881544 |\n", - "| clip_fraction | 0.144 |\n", + "| approx_kl | 0.014774322 |\n", + "| clip_fraction | 0.156 |\n", "| clip_range | 0.2 |\n", - "| entropy_loss | -3.21 |\n", - "| explained_variance | 0.372 |\n", + "| entropy_loss | -3.2 |\n", + "| explained_variance | 0.348 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 1.35 |\n", + "| loss | 1.38 |\n", "| n_updates | 40 |\n", - "| policy_gradient_loss | -0.0195 |\n", - "| value_loss | 2.45 |\n", + "| policy_gradient_loss | -0.0223 |\n", + "| value_loss | 2.62 |\n", "-----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -4.56 |\n", + "| ep_rew_mean | -4.83 |\n", "| time/ | |\n", - "| fps | 158 |\n", + "| fps | 203 |\n", "| iterations | 6 |\n", - "| time_elapsed | 77 |\n", + "| time_elapsed | 60 |\n", "| total_timesteps | 12288 |\n", "| train/ | |\n", - "| approx_kl | 0.012489447 |\n", - "| clip_fraction | 0.14 |\n", + "| approx_kl | 0.013777858 |\n", + "| clip_fraction | 0.16 |\n", "| clip_range | 0.2 |\n", - "| entropy_loss | -3.17 |\n", + "| entropy_loss | -3.14 |\n", "| explained_variance | 0.434 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 0.963 |\n", + "| loss | 1.04 |\n", "| n_updates | 50 |\n", - "| policy_gradient_loss | -0.019 |\n", - "| value_loss | 2.16 |\n", + "| policy_gradient_loss | -0.0227 |\n", + "| value_loss | 2.2 |\n", "-----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -4.86 |\n", + "| ep_rew_mean | -4.67 |\n", "| time/ | |\n", - "| fps | 142 |\n", + "| fps | 205 |\n", "| iterations | 7 |\n", - "| time_elapsed | 100 |\n", + "| time_elapsed | 69 |\n", "| total_timesteps | 14336 |\n", "| train/ | |\n", - "| approx_kl | 0.010966829 |\n", - "| clip_fraction | 0.124 |\n", + "| approx_kl | 0.012769257 |\n", + "| clip_fraction | 0.166 |\n", "| clip_range | 0.2 |\n", - "| entropy_loss | -3.11 |\n", - "| explained_variance | 0.418 |\n", + "| entropy_loss | -3.08 |\n", + "| explained_variance | 0.434 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 1.42 |\n", + "| loss | 1.19 |\n", "| n_updates | 60 |\n", - "| policy_gradient_loss | -0.0156 |\n", - "| value_loss | 2.29 |\n", + "| policy_gradient_loss | -0.0197 |\n", + "| value_loss | 2.09 |\n", "-----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -4.47 |\n", + "| ep_rew_mean | -4.16 |\n", "| time/ | |\n", - "| fps | 129 |\n", + "| fps | 206 |\n", "| iterations | 8 |\n", - "| time_elapsed | 126 |\n", + "| time_elapsed | 79 |\n", "| total_timesteps | 16384 |\n", "| train/ | |\n", - "| approx_kl | 0.014730865 |\n", - "| clip_fraction | 0.186 |\n", + "| approx_kl | 0.012809217 |\n", + "| clip_fraction | 0.147 |\n", "| clip_range | 0.2 |\n", - "| entropy_loss | -3.04 |\n", - "| explained_variance | 0.419 |\n", + "| entropy_loss | -3.02 |\n", + "| explained_variance | 0.42 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 1.14 |\n", + "| loss | 0.857 |\n", "| n_updates | 70 |\n", - "| policy_gradient_loss | -0.0233 |\n", - "| value_loss | 2.11 |\n", + "| policy_gradient_loss | -0.0186 |\n", + "| value_loss | 2.09 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 10 |\n", + "| ep_rew_mean | -4.15 |\n", + "| time/ | |\n", + "| fps | 206 |\n", + "| iterations | 9 |\n", + "| time_elapsed | 89 |\n", + "| total_timesteps | 18432 |\n", + "| train/ | |\n", + "| approx_kl | 0.014097567 |\n", + "| clip_fraction | 0.149 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -2.95 |\n", + "| explained_variance | 0.473 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.967 |\n", + "| n_updates | 80 |\n", + "| policy_gradient_loss | -0.0178 |\n", + "| value_loss | 1.87 |\n", "-----------------------------------------\n", - "----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -4.18 |\n", - "| time/ | |\n", - "| fps | 119 |\n", - "| iterations | 9 |\n", - "| time_elapsed | 153 |\n", - "| total_timesteps | 18432 |\n", - "| train/ | |\n", - "| approx_kl | 0.01256018 |\n", - "| clip_fraction | 0.157 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -2.97 |\n", - "| explained_variance | 0.446 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.58 |\n", - "| n_updates | 80 |\n", - "| policy_gradient_loss | -0.0194 |\n", - "| value_loss | 1.93 |\n", - "----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -4.39 |\n", + "| ep_rew_mean | -4.18 |\n", "| time/ | |\n", - "| fps | 123 |\n", + "| fps | 206 |\n", "| iterations | 10 |\n", - "| time_elapsed | 165 |\n", + "| time_elapsed | 99 |\n", "| total_timesteps | 20480 |\n", "| train/ | |\n", - "| approx_kl | 0.013073035 |\n", - "| clip_fraction | 0.147 |\n", + "| approx_kl | 0.013525041 |\n", + "| clip_fraction | 0.143 |\n", "| clip_range | 0.2 |\n", - "| entropy_loss | -2.91 |\n", - "| explained_variance | 0.491 |\n", + "| entropy_loss | -2.87 |\n", + "| explained_variance | 0.498 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 1.04 |\n", + "| loss | 0.716 |\n", "| n_updates | 90 |\n", - "| policy_gradient_loss | -0.0176 |\n", - "| value_loss | 1.74 |\n", + "| policy_gradient_loss | -0.0184 |\n", + "| value_loss | 1.58 |\n", "-----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -4.09 |\n", + "| ep_rew_mean | -3.8 |\n", "| time/ | |\n", - "| fps | 121 |\n", + "| fps | 205 |\n", "| iterations | 11 |\n", - "| time_elapsed | 184 |\n", + "| time_elapsed | 109 |\n", "| total_timesteps | 22528 |\n", "| train/ | |\n", - "| approx_kl | 0.013191431 |\n", - "| clip_fraction | 0.155 |\n", + "| approx_kl | 0.011971119 |\n", + "| clip_fraction | 0.147 |\n", "| clip_range | 0.2 |\n", - "| entropy_loss | -2.84 |\n", - "| explained_variance | 0.501 |\n", + "| entropy_loss | -2.8 |\n", + "| explained_variance | 0.503 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 0.98 |\n", + "| loss | 1.13 |\n", "| n_updates | 100 |\n", - "| policy_gradient_loss | -0.0172 |\n", - "| value_loss | 1.77 |\n", + "| policy_gradient_loss | -0.0191 |\n", + "| value_loss | 1.75 |\n", "-----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -3.68 |\n", + "| ep_rew_mean | -4.04 |\n", "| time/ | |\n", - "| fps | 120 |\n", + "| fps | 205 |\n", "| iterations | 12 |\n", - "| time_elapsed | 204 |\n", + "| time_elapsed | 119 |\n", "| total_timesteps | 24576 |\n", "| train/ | |\n", - "| approx_kl | 0.012647969 |\n", - "| clip_fraction | 0.144 |\n", + "| approx_kl | 0.011888548 |\n", + "| clip_fraction | 0.153 |\n", "| clip_range | 0.2 |\n", - "| entropy_loss | -2.78 |\n", - "| explained_variance | 0.463 |\n", + "| entropy_loss | -2.71 |\n", + "| explained_variance | 0.508 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 0.848 |\n", + "| loss | 0.697 |\n", "| n_updates | 110 |\n", - "| policy_gradient_loss | -0.0164 |\n", - "| value_loss | 1.79 |\n", + "| policy_gradient_loss | -0.0171 |\n", + "| value_loss | 1.42 |\n", "-----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -3.3 |\n", + "| ep_rew_mean | -3.2 |\n", "| time/ | |\n", - "| fps | 119 |\n", + "| fps | 206 |\n", "| iterations | 13 |\n", - "| time_elapsed | 223 |\n", + "| time_elapsed | 128 |\n", "| total_timesteps | 26624 |\n", "| train/ | |\n", - "| approx_kl | 0.011612153 |\n", - "| clip_fraction | 0.134 |\n", + "| approx_kl | 0.011168966 |\n", + "| clip_fraction | 0.16 |\n", "| clip_range | 0.2 |\n", - "| entropy_loss | -2.71 |\n", - "| explained_variance | 0.523 |\n", + "| entropy_loss | -2.66 |\n", + "| explained_variance | 0.513 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 0.832 |\n", + "| loss | 0.735 |\n", "| n_updates | 120 |\n", - "| policy_gradient_loss | -0.0164 |\n", - "| value_loss | 1.65 |\n", + "| policy_gradient_loss | -0.0208 |\n", + "| value_loss | 1.55 |\n", "-----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -3.7 |\n", + "| ep_rew_mean | -3.76 |\n", "| time/ | |\n", - "| fps | 119 |\n", + "| fps | 206 |\n", "| iterations | 14 |\n", - "| time_elapsed | 239 |\n", + "| time_elapsed | 138 |\n", "| total_timesteps | 28672 |\n", "| train/ | |\n", - "| approx_kl | 0.011285038 |\n", - "| clip_fraction | 0.111 |\n", + "| approx_kl | 0.013959848 |\n", + "| clip_fraction | 0.174 |\n", "| clip_range | 0.2 |\n", - "| entropy_loss | -2.63 |\n", - "| explained_variance | 0.502 |\n", + "| entropy_loss | -2.6 |\n", + "| explained_variance | 0.503 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 0.433 |\n", + "| loss | 0.876 |\n", "| n_updates | 130 |\n", - "| policy_gradient_loss | -0.0131 |\n", - "| value_loss | 1.42 |\n", + "| policy_gradient_loss | -0.0216 |\n", + "| value_loss | 1.6 |\n", "-----------------------------------------\n", "-----------------------------------------\n", "| rollout/ | |\n", "| ep_len_mean | 10 |\n", - "| ep_rew_mean | -3.57 |\n", + "| ep_rew_mean | -3.54 |\n", "| time/ | |\n", - "| fps | 120 |\n", + "| fps | 207 |\n", "| iterations | 15 |\n", - "| time_elapsed | 254 |\n", + "| time_elapsed | 148 |\n", "| total_timesteps | 30720 |\n", "| train/ | |\n", - "| approx_kl | 0.012549748 |\n", - "| clip_fraction | 0.14 |\n", + "| approx_kl | 0.010908822 |\n", + "| clip_fraction | 0.133 |\n", "| clip_range | 0.2 |\n", - "| entropy_loss | -2.58 |\n", - "| explained_variance | 0.55 |\n", + "| entropy_loss | -2.52 |\n", + "| explained_variance | 0.548 |\n", "| learning_rate | 0.0003 |\n", - "| loss | 0.344 |\n", + "| loss | 1.02 |\n", "| n_updates | 140 |\n", - "| policy_gradient_loss | -0.015 |\n", - "| value_loss | 1.45 |\n", + "| policy_gradient_loss | -0.0144 |\n", + "| value_loss | 1.54 |\n", "-----------------------------------------\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1148,29 +1157,29 @@ "output_type": "stream", "text": [ "Requirement already satisfied: tensorboard in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (2.7.0)\n", - "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (3.3.6)\n", + "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (1.8.0)\n", "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (2.3.3)\n", + "Requirement already satisfied: werkzeug>=0.11.15 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (2.0.2)\n", "Requirement already satisfied: grpcio>=1.24.3 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (1.42.0)\n", + "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (3.3.6)\n", + "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (2.26.0)\n", + "Requirement already satisfied: absl-py>=0.4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (1.0.0)\n", "Requirement already satisfied: numpy>=1.12.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (1.21.4)\n", - "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (0.4.6)\n", - "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (1.8.0)\n", "Requirement already satisfied: protobuf>=3.6.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (3.19.1)\n", - "Requirement already satisfied: werkzeug>=0.11.15 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (2.0.2)\n", - "Requirement already satisfied: absl-py>=0.4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (1.0.0)\n", - "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (0.6.1)\n", "Requirement already satisfied: wheel>=0.26 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (0.34.2)\n", + "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (0.4.6)\n", "Requirement already satisfied: setuptools>=41.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (46.1.3)\n", - "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (2.26.0)\n", + "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from tensorboard) (0.6.1)\n", "Requirement already satisfied: six in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from absl-py>=0.4->tensorboard) (1.15.0)\n", - "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard) (0.2.8)\n", "Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard) (4.2.4)\n", "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard) (4.8)\n", + "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard) (0.2.8)\n", "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard) (1.3.0)\n", "Requirement already satisfied: importlib-metadata>=4.4 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from markdown>=2.6.8->tensorboard) (4.8.2)\n", + "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard) (2.0.7)\n", "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard) (3.3)\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard) (1.26.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard) (2021.10.8)\n", - "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard) (2.0.7)\n", "Requirement already satisfied: zipp>=0.5 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard) (3.6.0)\n", "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard) (0.4.8)\n", "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\stefan\\git-repos\\work\\mobile-env\\venv\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard) (3.1.1)\n" @@ -1185,7 +1194,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": { "collapsed": false, "jupyter": { @@ -1199,7 +1208,7 @@ { "data": { "text/plain": [ - "Reusing TensorBoard on port 6006 (pid 19344), started 0:50:01 ago. (Use '!kill 19344' to kill it.)" + "Reusing TensorBoard on port 6006 (pid 19344), started 22:35:08 ago. (Use '!kill 19344' to kill it.)" ] }, "metadata": {}, @@ -1209,11 +1218,11 @@ "data": { "text/html": [ "\n", - " \n", " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%tensorboard --logdir results_rllib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load and Test the Trained Multi-Agent Policy\n", + "\n", + "Let's load the trained multi-agent model and visualize the learned multi-agent policy:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from ray.rllib.agents.ppo import PPOTrainer\n", + "\n", + "\n", + "# load the trained agent\n", + "checkpoint = analysis.get_last_checkpoint()\n", + "model = PPOTrainer(config=config, env='mobile-small-ma-v0')\n", + "model.restore(checkpoint)\n", + "\n", + "# create the env for testing\n", + "env = gym.make(\"mobile-small-ma-v0\")\n", + "obs = env.reset()\n", + "done = False\n", + "\n", + "# run one episode with the trained model\n", + "while not done:\n", + " # gather action from each actor (for each UE)\n", + " action = {}\n", + " for agent_id, agent_obs in obs.items():\n", + " policy_id = config['multiagent']['policy_mapping_fn'](agent_id)\n", + " action[agent_id] = model.compute_action(agent_obs, policy_id=policy_id)\n", + "\n", + " # apply actions and perform step on simulation environment\n", + " obs, reward, done, info = env.step(action)\n", + "\n", + " # render environment as RGB\n", + " plt.imshow(env.render(mode='rgb_array'))\n", + " display.display(plt.gcf())\n", + " display.clear_output(wait=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 0d14a3623a25ee56796a4c86af9dac098168819c Mon Sep 17 00:00:00 2001 From: Stefan Schneider Date: Mon, 13 Dec 2021 17:02:04 +0100 Subject: [PATCH 14/14] fix flake error --- mobile_env/handlers/multi_agent.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/mobile_env/handlers/multi_agent.py b/mobile_env/handlers/multi_agent.py index e50d720..8d78b71 100644 --- a/mobile_env/handlers/multi_agent.py +++ b/mobile_env/handlers/multi_agent.py @@ -8,7 +8,9 @@ class MComMAHandler(Handler): - features = ["connections", "snrs", "utility", "bcast", "stations_connected"] + features = [ + "connections", "snrs", "utility", "bcast", "stations_connected" + ] @classmethod def ue_obs_size(cls, env) -> int: