Project 3 - Custom Environment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://sourceforge.net/projects/swig/files/swigwin/swigwin-4.0.2/swigwin-4.0.2.zip/download?use_mirror=ixpeering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym \n",
    "from gym import Env\n",
    "from gym.spaces import Discrete, Box, Dict, Tuple, MultiBinary, MultiDiscrete \n",
    "import numpy as np\n",
    "import random\n",
    "import os\n",
    "from stable_baselines3 import PPO\n",
    "from stable_baselines3.common.vec_env import VecFrameStack\n",
    "from stable_baselines3.common.evaluation import evaluate_policy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Types of Spaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Discrete(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Box(0,1,shape=(3,3)).sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Box(0,255,shape=(3,3), dtype=int).sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tuple((Discrete(2), Box(0,100, shape=(1,)))).sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dict({'height':Discrete(2), \"speed\":Box(0,100, shape=(1,))}).sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MultiBinary(4).sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MultiDiscrete([5,2,2]).sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Building an Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ShowerEnv(Env):\n",
    "    def __init__(self):\n",
    "        # Actions we can take, down, stay, up\n",
    "        self.action_space = Discrete(3)\n",
    "        # Temperature array\n",
    "        self.observation_space = Box(low=np.array([0]), high=np.array([100]))\n",
    "        # Set start temp\n",
    "        self.state = 38 + random.randint(-3,3)\n",
    "        # Set shower length\n",
    "        self.shower_length = 60\n",
    "        \n",
    "    def step(self, action):\n",
    "        # Apply action\n",
    "        # 0 -1 = -1 temperature\n",
    "        # 1 -1 = 0 \n",
    "        # 2 -1 = 1 temperature \n",
    "        self.state += action -1 \n",
    "        # Reduce shower length by 1 second\n",
    "        self.shower_length -= 1 \n",
    "        \n",
    "        # Calculate reward\n",
    "        if self.state >=37 and self.state <=39: \n",
    "            reward =1 \n",
    "        else: \n",
    "            reward = -1 \n",
    "        \n",
    "        # Check if shower is done\n",
    "        if self.shower_length <= 0: \n",
    "            done = True\n",
    "        else:\n",
    "            done = False\n",
    "        \n",
    "        # Apply temperature noise\n",
    "        #self.state += random.randint(-1,1)\n",
    "        # Set placeholder for info\n",
    "        info = {}\n",
    "        \n",
    "        # Return step information\n",
    "        return self.state, reward, done, info\n",
    "\n",
    "    def render(self):\n",
    "        # Implement viz\n",
    "        pass\n",
    "    \n",
    "    def reset(self):\n",
    "        # Reset shower temperature\n",
    "        self.state = np.array([38 + random.randint(-3,3)]).astype(float)\n",
    "        # Reset shower time\n",
    "        self.shower_length = 60 \n",
    "        return self.state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env=ShowerEnv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.observation_space.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stable_baselines3.common.env_checker import check_env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "check_env(env, warn=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Test Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "episodes = 5\n",
    "for episode in range(1, episodes+1):\n",
    "    state = env.reset()\n",
    "    done = False\n",
    "    score = 0 \n",
    "    \n",
    "    while not done:\n",
    "        env.render()\n",
    "        action = env.action_space.sample()\n",
    "        n_state, reward, done, info = env.step(action)\n",
    "        score+=reward\n",
    "    print('Episode:{} Score:{}'.format(episode, score))\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_path = os.path.join('Training', 'Logs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = PPO(\"MlpPolicy\", env, verbose=1, tensorboard_log=log_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.learn(total_timesteps=400000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('PPO')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluate_policy(model, env, n_eval_episodes=10, render=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "rlcourse",
   "language": "python",
   "name": "rlcourse"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}