Rnn support: DRQN agent + recurrent buffer #258

hnekoeiq · 2022-03-12T19:55:16Z

No description provided.

initial commit recurrent buffer implementation

initial commit DRQN implementation

…pdating

TongTongX · 2022-03-16T16:20:09Z

hive/replays/recurrent_replay.py

+            self.size() + self._max_seq_len + self._n_step - 1
+        )
+        elements = array[full_indices]
+        elements = elements.reshape(indices.shape[0], -1, *elements.shape[2:])


We want to ensure the dimension of observation is (batch_size, seq_length, C, H, W). C=1 according to https://github.com/chandar-lab/RLHive/blame/main/hive/envs/atari/atari.py#L62.

Functions and code resued from CircularReplayBuffer? Maybe just use the inherited functions? Or are there changes across all the functions?

We are using the inherited functions defined in CircularReplayBuffer except for the ones where max_seq_len and stack_size differ.

Can something like super calls be done? For e.g. the sample function in PPOReplayBuffer? That is the input is updated such that it can be passed to the function of the master class.

There will be a new PR that implements a recurrent replay that saves trajectories rather than sequences of transitions. It will be more suitable for recurrent DQN. For now I think it's fine to keep those functions where max_seq_len and stack_size are differerent.

…nto rnn_support

karthiks1701 · 2022-03-16T17:57:10Z

hive/replays/recurrent_replay.py

@@ -200,7 +200,7 @@ def sample(self, batch_size):
            trajectory_lengths = (
                np.argmax(terminals.astype(bool), axis=1) + 1
            ) * is_terminal + self._n_step * (1 - is_terminal)
-            is_terminal = terminals[:,1:self._n_step-1]


Because of the sequence length, is_terminal has to be of shape (B*seq_length). Hence this particular change after calculating trajectory lengths.

karthiks1701 · 2022-03-16T18:04:42Z

hive/replays/recurrent_replay.py

+                    ] + np.arange(self._n_step)
+                    disc_rewards = np.einsum(
+                        "ijk,k->ij", rewards[:, idx], self._discount
+                    )
                    rewards = disc_rewards


Creating a vectorized sliding window to calculate discounted returns for every element in the sequence.

…support

dapatil211

I went through and gave a quick review. I didn't thoroughly check for correctness yet, ideally would want to see experiments that show it's working first before doing that. Also please write updated docstrings.

dapatil211 · 2022-03-18T18:59:28Z

hive/agents/qnets/qnet_heads.py

+    """Implements the standard DQN value computation. Transforms output from
+    :obj:`base_network` with output dimension :obj:`hidden_dim` to dimension
+    :obj:`out_dim`, which should be equal to the number of actions.
+    """


Update docstring

dapatil211 · 2022-03-18T18:59:40Z

hive/agents/qnets/qnet_heads.py

+            base_network (torch.nn.Module): Backbone network that computes the
+                representations that are used to compute action values.


Update docstring

You should say something about the expected output of this base_network

Please update documentation according to previous comment. Specifically that base_network returns two things, and what those two things are.

dapatil211 · 2022-03-18T20:38:15Z

hive/agents/drqn.py

+    """An agent implementing the DQN algorithm. Uses an epsilon greedy
+    exploration policy


dapatil211 · 2022-03-18T20:39:52Z

hive/agents/qnets/rnn.py

+
+        if mlp_layers is not None:
+            # MLP Layers
+            # conv_output_size = calculate_output_dim(self.conv, in_dim)


dapatil211 · 2022-03-18T20:42:27Z

hive/agents/qnets/utils.py

@@ -20,9 +20,12 @@ def calculate_output_dim(net, input_shape):
    """
    if isinstance(input_shape, int):
        input_shape = (input_shape,)
-    placeholder = torch.zeros((0,) + tuple(input_shape))
+    placeholder = torch.zeros((1,) + tuple(input_shape)).to(device)


Device shouldn't need to be passed in right? Like at this point it should all be on the cpu? I am fine with the change, just not sure why it's necessary

@dapatil211 The problem is here we either need to create a dummy hidden_state on CPU and pass it to the network or add an if condition in the forward function of ConvRNNNetwork to take care of hidden_state = None. We did the latter but there might be another case other than calculate_output_dim where hidden_state is None and in that case, our hidden_state inside the forward function should be on the _device. (We had to change the empty array because lstm was not able to process it)

Well don't you only need to do that because you are doing .to(device) here when you don't really need to?: https://github.com/chandar-lab/RLHive/pull/258/files/4874d7a579fbcaf3c26dce7d3fecc01c5c57fac6#diff-324043212c24b1c8e9c8c139a4defa49947886ee8fc9f4f9ab8138d4347f4860R139-R141

dapatil211 · 2022-03-18T20:43:17Z

hive/agents/qnets/utils.py

    output = net(placeholder)
-    return output.size()[1:]
+    if isinstance(output, tuple):


If it's a tuple, you should return the size of each output. It doesn't make sense to only do the first output. That's just specific to your current use case

@dapatil211 This one needs a bit more discussion. Since hidden_state (the second item in the tuple) is a tuple itself, should we check the items inside that and return their size as well?

Probably should be recursive? Keep going until you hit scalars or tensors

dapatil211 · 2022-03-18T20:45:10Z

hive/replays/recurrent_replay.py

+                # mask = np.expand_dims(trajectory_lengths, 1) > np.arange(self._n_step)
+                # rewards = np.sum(rewards * mask, axis=1)


dapatil211 · 2022-03-18T20:45:55Z

hive/replays/recurrent_replay.py

+                    idx = np.arange(rewards.shape[1] - self._n_step + 1)[
+                        :, None
+                    ] + np.arange(self._n_step)
+                    disc_rewards = np.einsum(


Could you give a comment as to what this is doing? einsum is notoriously bad in terms of being interpreted.

Sure will do!

hnekoeiq · 2022-03-22T23:16:36Z

Results:
DRQN vs DQN on Pong, DoubleDunk, Frostbite

DRQN vs DQN on Frostbite and Beam Rider

karthiks1701 · 2022-03-29T07:00:32Z

hive/agents/drqn.py

+            representation_net=representation_net,
+            obs_dim=obs_dim,
+            act_dim=act_dim,
+            id=0,


I think we missed id=id. to pass the id to the base DQN class, because of this in marlgrid all the agents have the same id and that is causing problems.

sriyash421 · 2022-07-11T21:58:39Z

hive/agents/qnets/rnn.py

+        conv_output_size = calculate_output_dim(self.conv, in_dim)
+        if self._rnn_type == "lstm":
+            self.rnn = nn.LSTM(
+                np.prod(conv_output_size),


Changing different types of network to a sequencer class

sriyash421 · 2022-07-11T22:01:33Z

hive/agents/drqn.py

+                "observation", "action", "reward", and "done".
+        """
+        if update_info["done"]:
+            self._state["episode_start"] = True


is episode start used anywhere except the buffer? because the buffer takes care of it and it is redundant.

This is there for stuff in act(). There's probably a better way to do it. It doesn't really make sense for the agent to do it. It might make sense to add it as part of the observation, but need to think about this a bit.

We probably want to fix it in a separate PR as this is what DQN does too.

We created an issue for fixing it in both DQN and DRQN.

I think this part is fine. I don't think it needs fixing.

hnekoeiq · 2022-07-18T18:57:33Z

hive/agents/qnets/sequence_models.py

+            batch_first (bool): If True, then the input and output tensors are provided as (batch, seq, feature) instead of (seq, batch, feature).
+        """
+        super().__init__(
+            rnn_input_size=rnn_input_size,


You can remove rnn_input_size if the base function does not use it.

Done. SImilarly batch_first is also removed.

…or fixes

dapatil211 · 2022-05-27T19:44:45Z

hive/agents/drqn.py

+            self._optimizer.zero_grad()
+            pred_qvals, hidden_state = self._qnet(*current_state_inputs, hidden_state)
+            pred_qvals = pred_qvals.view(
+                self._batch_size, self._replay_buffer._max_seq_len, -1


Should not be accessing internal variable of buffer

dapatil211 · 2022-07-22T15:44:45Z

hive/agents/__init__.py

@@ -16,6 +17,7 @@
        "LegalMovesRainbowAgent": LegalMovesRainbowAgent,
        "RainbowDQNAgent": RainbowDQNAgent,
        "RandomAgent": RandomAgent,
+        "DRQNAgent": DRQNAgent,


Alphabetical order please.

dapatil211 · 2022-07-22T15:45:53Z

hive/agents/drqn.py

+import copy
+import os
+
+import gym
+import numpy as np
+import torch
+
+from hive.agents.agent import Agent
+from hive.agents.qnets.base import FunctionApproximator
+from hive.agents.qnets.qnet_heads import DRQNNetwork
+from hive.agents.qnets.utils import (
+    InitializationFn,
+    calculate_output_dim,
+    create_init_weights_fn,
+)
+from hive.replays import BaseReplayBuffer, CircularReplayBuffer
+from hive.replays.recurrent_replay import RecurrentReplayBuffer
+from hive.utils.loggers import Logger, NullLogger
+from hive.utils.schedule import (
+    LinearSchedule,
+    PeriodicSchedule,
+    Schedule,
+    SwitchSchedule,
+)
+from hive.agents.dqn import DQNAgent
+from hive.utils.utils import LossFn, OptimizerFn, create_folder, seeder


Make sure you run import sorting (isort)

dapatil211 · 2022-07-22T15:47:32Z

hive/agents/drqn.py

+        Args:
+            observation_space (gym.spaces.Box): Observation space for the agent.
+            action_space (gym.spaces.Discrete): Action space for the agent.
+            representation_net (FunctionApproximator): A network that outputs the


I am assuming there are restrictions on the representation_net? eg it needs to be one of your recurrent ones? Please mention this in the documentation.

Can you more explicitly mention the restrictions on representation_net? For example, which methods it should have or that it should follow the structure of ConvRNNNetwork or something?

dapatil211 · 2022-07-22T15:50:22Z

hive/agents/drqn.py

+            stack_size: Number of observations stacked to create the state fed to the
+                DRQN.


What does this mean in the context of DRQN? You aren't using this are you? If not, remove. You may need to keep a dummy or add a kwargs to make it work with the runner.

dapatil211 · 2022-07-22T21:06:26Z

hive/agents/qnets/rnn.py

+    def __init__(
+        self,
+        in_dim,
+        sequence_fn: SequenceModule,


See my comment in the sequence_models.py file about this.

dapatil211 · 2022-07-22T21:07:59Z

hive/replays/recurrent_replay.py

+        capacity: int = 10000,
+        max_seq_len: int = 1,
+        n_step: int = 1,
+        gamma: float = 0.99,
+        observation_shape=(),
+        observation_dtype=np.uint8,
+        action_shape=(),
+        action_dtype=np.int8,
+        reward_shape=(),
+        reward_dtype=np.float32,
+        extra_storage_types=None,
+        num_players_sharing_buffer: int = None,


Why is the spacing inconsistent?

If we specify the datatype black will add whitespaces around the equal sign, otherwise it will remove those whitespaces.

dapatil211 · 2022-07-22T21:08:45Z

hive/replays/recurrent_replay.py

+            capacity (int): Total number of observations that can be stored in the
+                buffer. Note, this is not the same as the number of transitions that
+                can be stored in the buffer.
+            max_seq_len (int): The number of consecutive transitions in a sequence.


Please clarify the description of this parameter.

dapatil211 · 2022-07-22T21:15:46Z

hive/replays/recurrent_replay.py

+        super().__init__(
+            capacity=capacity,
+            stack_size=1,
+            n_step=n_step,
+            gamma=gamma,
+            observation_shape=observation_shape,
+            observation_dtype=observation_dtype,
+            action_shape=action_shape,
+            action_dtype=action_dtype,
+            reward_shape=reward_shape,
+            reward_dtype=reward_dtype,
+            extra_storage_types=extra_storage_types,
+            num_players_sharing_buffer=num_players_sharing_buffer,
+        )
+        self._max_seq_len = max_seq_len
+


Would this class be equivalent to just creating CircularReplayBuffer(stack_size=max_seq_len)? If so, why not just do that? If not, what/where is the change in logic?

There are differences in sample().

dapatil211 · 2022-07-22T21:16:32Z

hive/replays/recurrent_replay.py

+                        self._n_step
+                    )  # (S-N+1) x N
+                    rewards = rewards[:, idx]  # B x (S-N+1) x N
+                    # Creating a vectorized sliding window to calculate discounted returns for every element in the sequence


Please make sure commented lines and lines part of docstrings are still under 88 characters.

dapatil211 · 2022-08-02T19:13:12Z

hive/runners/single_agent_loop.py

@@ -153,7 +153,6 @@ def set_up_experiment(config):
    agent = agent_fn(
        observation_space=env_spec.observation_space[0],
        action_space=env_spec.action_space[0],
-        stack_size=config.get("stack_size", 1),


This shouldn't be removed. It will break other agents.

dapatil211 · 2022-08-16T21:51:47Z

hive/agents/drqn.py

+                "observation", "action", "reward", and "done".
+        """
+        if update_info["done"]:
+            self._state["episode_start"] = True


I think this part is fine. I don't think it needs fixing.

dapatil211 · 2022-08-16T21:52:38Z

hive/agents/drqn.py

+            self._hidden_state = self._qnet.base_network.init_hidden(
+                batch_size=1, device=self._device
+            )


I don't think this is the same thing. episode_start can only be set in update because that's when the agent knows that the episode ended. My comment is about how the resetting of the hidden state should be done in act()

dapatil211 · 2022-08-17T18:22:31Z

hive/agents/qnets/sequence_models.py

+            rnn_input_size (int): The number of expected features in the input x.
+            rnn_hidden_size (int):  The number of features in the hidden state h.
+            num_rnn_layers (int): Number of recurrent layers.
+            batch_first (bool): If True, then the input and output tensors are provided as (batch, seq, feature) instead of (seq, batch, feature).


line length

dapatil211 · 2022-08-17T18:22:39Z

hive/agents/qnets/sequence_models.py

+            rnn_input_size (int): The number of expected features in the input x.
+            rnn_hidden_size (int):  The number of features in the hidden state h.
+            num_rnn_layers (int): Number of recurrent layers.
+            batch_first (bool): If True, then the input and output tensors are provided as (batch, seq, feature) instead of (seq, batch, feature).


line length

dapatil211 · 2022-08-17T18:23:11Z

hive/agents/qnets/sequence_models.py

+            batch_first=batch_first,
+        )
+
+    def init_hidden(self, batch_size, device="cpu"):


can this device not just be passed once in the initializer?

… config

dapatil211 · 2022-09-12T22:01:19Z

hive/agents/drqn.py

+        if replay_buffer is None:
+            replay_buffer = RecurrentReplayBuffer
+        self._replay_buffer = replay_buffer(
+            max_seq_len=max_seq_len,
+            observation_shape=self._observation_space.shape,
+            observation_dtype=self._observation_space.dtype,
+            action_shape=self._action_space.shape,
+            action_dtype=self._action_space.dtype,
+        )
+        self._max_seq_len = max_seq_len


This can be moved above the super constructor call:

if replay_buffer is None: replay_buffer = RecurrentReplayBuffer replay_buffer = partial(replay_buffer, max_seq_len=max_seq_len)

dapatil211 · 2022-09-26T18:58:03Z

hive/agents/drqn.py

+            replay_buffer (BaseReplayBuffer): The replay buffer that the agent will
+                push observations to and sample from during learning. If None,
+                defaults to
+                :py:class:`~hive.replays.circular_replay.CircularReplayBuffer`.


This is incorrect. Please go through all the documentation and make sure it is correct.

dapatil211 · 2022-09-26T18:59:45Z

hive/agents/drqn.py

+        Args:
+            observation_space (gym.spaces.Box): Observation space for the agent.
+            action_space (gym.spaces.Discrete): Action space for the agent.
+            representation_net (FunctionApproximator): A network that outputs the


Can you more explicitly mention the restrictions on representation_net? For example, which methods it should have or that it should follow the structure of ConvRNNNetwork or something?

dapatil211 · 2022-09-26T19:02:25Z

hive/agents/drqn.py

+            hidden_state = self._qnet.base_network.init_hidden(
+                batch_size=self._batch_size, device=self._device
+            )
+            target_hidden_state = self._target_qnet.base_network.init_hidden(
+                batch_size=self._batch_size, device=self._device
+            )


It feels weird that you are accessing an internal module of the qnet. I think instead of self._qnet.base_network.init_hidden, it should be self._qnet.init_hidden

dapatil211 · 2022-09-26T19:03:01Z

hive/agents/qnets/qnet_heads.py

+        self.base_network = base_network
+        self._linear_fn = linear_fn if linear_fn is not None else nn.Linear
+        self.output_layer = self._linear_fn(hidden_dim, out_dim)


should probably make all of these internal variables

maybe in a separate PR? All network modules defined here have base_network and output_layer.

dapatil211 · 2022-09-26T19:07:13Z

hive/replays/recurrent_replay.py

+                    rewards = rewards[:, idx]  # B x (S-N+1) x N
+                    # Creating a vectorized sliding window to calculate
+                    # discounted returns for every element in the sequence.
+                    # equivalent to np.sum(rewards * self._discount[None, None, :], axis=2)


This line is longer than 88 characters.

sriyash421 · 2022-09-27T00:21:52Z

hive/agents/drqn.py

+            )
+            # Compute predicted Q values
+            self._optimizer.zero_grad()
+            pred_qvals, _ = self._qnet(*current_state_inputs, hidden_state)


Just to make sure, the qnet takes in a window of past observations and you take as output the last hidden state and pass it through an MLP to get the Q-values? So, it involves some redundant computation when calculating Q-values for s_t and s_t+1

We have another PR #270 that handles hidden states saving & burn-in frames. In this PR the hidden states are initialized from 0's.

Could you also provide some reference if you have seen more efficient ways of reusing hidden state and computing Q?

we can have a look at the cleanrl/sb3 implementation of recurrent networks. there implementation with jax might have some principles or tricks which we can use for our code base?

I think the current implementation is good enough and is working. We can create new prs to improve its efficiency maybe.

hive/agents/qnets/__init__.py

sriyash421 · 2022-09-27T00:24:56Z

hive/agents/qnets/qnet_heads.py

+        x, hidden_state = self.base_network(x, hidden_state)
+
+        x = x.flatten(start_dim=1)
+        return self.output_layer(x), hidden_state


Maybe it can have an internal function to call during act and update separately?

hive/agents/drqn.py

hive/agents/qnets/qnet_heads.py

hive/agents/qnets/rnn.py

hive/agents/qnets/sequence_models.py

hive/replays/__init__.py

sriyash421 · 2022-09-27T00:33:09Z

hive/replays/recurrent_replay.py

+            self.size() + self._max_seq_len + self._n_step - 1
+        )
+        elements = array[full_indices]
+        elements = elements.reshape(indices.shape[0], -1, *elements.shape[2:])


Functions and code resued from CircularReplayBuffer? Maybe just use the inherited functions? Or are there changes across all the functions?

dapatil211

Assuming everything runs and matches existing benchmarks, looks good. Please add wandb runs to this conversation and also to the benchmarking report. Also when merging, do a squash and merge.

hnekoeiq · 2022-10-07T18:28:23Z

Assuming everything runs and matches existing benchmarks, looks good. Please add wandb runs to this conversation and also to the benchmarking report. Also when merging, do a squash and merge.

We should rerun the experiments for benchmarking, but here are some of the results on atari:
#258 (comment)

And results on Hanabi DRQN: https://wandb.ai/chandar-rl/Hive/reports/DRQN-for-Hanabi--VmlldzoyMzU1NTUy?accessToken=efz63017kwgubdp8oyloepsfpjn3pmf0qka070h6rh90jjrcxcp8jd3eo20ko9sj

hnekoeiq and others added 9 commits March 10, 2022 23:30

initial commit DRQN implementation

981be53

initial commit recurrent buffer implementation

0aabcff

Merge pull request #256 from chandar-lab/recurrent_buffer

9d666a8

initial commit recurrent buffer implementation

Merge branch 'rnn_support' into recurrent_dqn

f557787

Merge pull request #257 from chandar-lab/recurrent_dqn

38af33c

initial commit DRQN implementation

drqn agent working with recurrent buffer

41b6041

fix device

d2f205f

nstep update for RNN

1ae9cf0

handled input obs dimension depending on whether agent is acting or u…

346a46f

…pdating

TongTongX reviewed Mar 16, 2022

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karthiks1701 added 2 commits March 16, 2022 13:32

nstep update for RNN after format

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Merge branch 'rnn_support' of https://github.com/chandar-lab/RLHive i…

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…nto rnn_support

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reformatted code with black

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Merge branch 'rnn_support' of github.com:chandar-lab/RLHive into rnn_…

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…support

fix bugs with buffer size and device

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hnekoeiq added 2 commits March 22, 2022 12:17

update device configuration

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DRQN agent tested + updated doctrings and some cleanups

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soma and others added 6 commits March 30, 2022 21:25

fixing id issue for MARL

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formatted with black

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initial commit DRQN implementation

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initial commit recurrent buffer implementation

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drqn agent working with recurrent buffer

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fix device

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sriyash421 reviewed Jul 11, 2022

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TongTongX added 2 commits July 15, 2022 02:14

merged changes related to callable objects

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added sequence model class

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TongTongX and others added 2 commits July 18, 2022 16:34

removed unused parameter in base sequence module class; docstring min…

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…or fixes

Merge branch 'dev' into rnn_support

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docstring and other minor changes

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TongTongX added 3 commits August 2, 2022 15:20

reverted stack_size for other agents

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sequence registrable not inherit torch nn.Module

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format

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DRQN reset hidden state in act(), set device of sequence model in yml…

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… config

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hnekoeiq added 2 commits September 19, 2022 11:40

clean up

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minors fixes

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alphabetical ordering

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dapatil211 approved these changes Oct 6, 2022

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javin.liu and others added 3 commits October 12, 2022 23:52

fixed device mismatch between rnn hidden state and placeholder image

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add both batch and sequence dim to observation during acting

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Merge branch 'dev' into rnn_support

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hnekoeiq merged commit 8a5c91e into dev Oct 13, 2022

hnekoeiq deleted the rnn_support branch October 13, 2022 21:27

		base_network (torch.nn.Module): Backbone network that computes the
		representations that are used to compute action values.

		"""An agent implementing the DQN algorithm. Uses an epsilon greedy
		exploration policy

		# mask = np.expand_dims(trajectory_lengths, 1) > np.arange(self._n_step)
		# rewards = np.sum(rewards * mask, axis=1)

		stack_size: Number of observations stacked to create the state fed to the
		DRQN.

Rnn support: DRQN agent + recurrent buffer #258

Rnn support: DRQN agent + recurrent buffer #258

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hnekoeiq commented Mar 12, 2022

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hnekoeiq commented Mar 22, 2022

karthiks1701 Mar 29, 2022 • edited Loading

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karthiks1701 Mar 29, 2022 •

edited

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