Deep-Q-Network/DQN-NN/Deep_Q_Network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Q-Network (DQN)\n",
    "---\n",
    "In this notebook, you will implement a DQN agent with OpenAI Gym's LunarLander-v2 environment.\n",
    "\n",
    "### 1. Import the Necessary Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: box2d in /opt/conda/lib/python3.6/site-packages (2.3.2)\n",
      "Collecting pyvirtualdisplay\n",
      "  Downloading https://files.pythonhosted.org/packages/cf/ad/b15f252bfb0f1693ad3150b55a44a674f3cba711cacdbb9ae2f03f143d19/PyVirtualDisplay-0.2.4-py2.py3-none-any.whl\n",
      "Collecting EasyProcess (from pyvirtualdisplay)\n",
      "  Downloading https://files.pythonhosted.org/packages/fa/29/40040d1d64a224a5e44df9572794a66494618ffe5c77199214aeceedb8a7/EasyProcess-0.2.7-py2.py3-none-any.whl\n",
      "Installing collected packages: EasyProcess, pyvirtualdisplay\n",
      "Successfully installed EasyProcess-0.2.7 pyvirtualdisplay-0.2.4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "xdpyinfo was not found, X start can not be checked! Please install xdpyinfo!\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "!pip3 install box2d\n",
    "import random\n",
    "import torch\n",
    "import numpy as np\n",
    "from collections import deque\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "!python -m pip install pyvirtualdisplay\n",
    "from pyvirtualdisplay import Display\n",
    "display = Display(visible=0, size=(1400, 900))\n",
    "display.start()\n",
    "\n",
    "is_ipython = 'inline' in plt.get_backend()\n",
    "if is_ipython:\n",
    "    from IPython import display\n",
    "\n",
    "plt.ion()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Instantiate the Environment and Agent\n",
    "\n",
    "Initialize the environment in the code cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "State shape:  (8,)\n",
      "Number of actions:  4\n"
     ]
    }
   ],
   "source": [
    "env = gym.make('LunarLander-v2')\n",
    "env.seed(0)\n",
    "print('State shape: ', env.observation_space.shape)\n",
    "print('Number of actions: ', env.action_space.n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before running the next code cell, familiarize yourself with the code in **Step 2** and **Step 3** of this notebook, along with the code in `dqn_agent.py` and `model.py`.  Once you have an understanding of how the different files work together, \n",
    "- Define a neural network architecture in `model.py` that maps states to action values.  This file is mostly empty - it's up to you to define your own deep Q-network!\n",
    "- Finish the `learn` method in the `Agent` class in `dqn_agent.py`.  The sampled batch of experience tuples is already provided for you; you need only use the local and target Q-networks to compute the loss, before taking a step towards minimizing the loss.\n",
    "\n",
    "Once you have completed the code in `dqn_agent.py` and `model.py`, run the code cell below.  (_If you end up needing to make multiple changes and get unexpected behavior, please restart the kernel and run the cells from the beginning of the notebook!_)\n",
    "\n",
    "You can find the solution files, along with saved model weights for a trained agent, in the `solution/` folder.  (_Note that there are many ways to solve this exercise, and the \"solution\" is just one way of approaching the problem, to yield a trained agent._)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc78efc54e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dqn_agent import Agent\n",
    "\n",
    "agent = Agent(state_size=8, action_size=4, seed=0)\n",
    "\n",
    "# watch an untrained agent\n",
    "state = env.reset()\n",
    "img = plt.imshow(env.render(mode='rgb_array'))\n",
    "for j in range(200):\n",
    "    action = agent.act(state)\n",
    "    img.set_data(env.render(mode='rgb_array')) \n",
    "    plt.axis('off')\n",
    "    display.display(plt.gcf())\n",
    "    display.clear_output(wait=True)\n",
    "    state, reward, done, _ = env.step(action)\n",
    "    if done:\n",
    "        break \n",
    "        \n",
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Train the Agent with DQN\n",
    "\n",
    "Run the code cell below to train the agent from scratch.  You are welcome to amend the supplied values of the parameters in the function, to try to see if you can get better performance!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 100\tAverage Score: -206.09\n",
      "Episode 200\tAverage Score: -172.98\n",
      "Episode 300\tAverage Score: -63.395\n",
      "Episode 400\tAverage Score: -103.26\n",
      "Episode 500\tAverage Score: -51.834\n",
      "Episode 600\tAverage Score: 48.197\n",
      "Episode 700\tAverage Score: 100.54\n",
      "Episode 800\tAverage Score: 185.62\n",
      "Episode 900\tAverage Score: 178.25\n",
      "Episode 1000\tAverage Score: 178.86\n",
      "Episode 1100\tAverage Score: 187.03\n",
      "Episode 1200\tAverage Score: 179.99\n",
      "Episode 1300\tAverage Score: 166.90\n",
      "Episode 1400\tAverage Score: 153.16\n",
      "Episode 1500\tAverage Score: 68.563\n",
      "Episode 1600\tAverage Score: 175.50\n",
      "Episode 1638\tAverage Score: 200.37\n",
      "Environment solved in 1538 episodes!\tAverage Score: 200.37\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f096d48f5f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def dqn(n_episodes=2000, max_t=1000, eps_start=1.0, eps_end=0.01, eps_decay=0.995):\n",
    "    \"\"\"Deep Q-Learning.\n",
    "    \n",
    "    Params\n",
    "    ======\n",
    "        n_episodes (int): maximum number of training episodes\n",
    "        max_t (int): maximum number of timesteps per episode\n",
    "        eps_start (float): starting value of epsilon, for epsilon-greedy action selection\n",
    "        eps_end (float): minimum value of epsilon\n",
    "        eps_decay (float): multiplicative factor (per episode) for decreasing epsilon\n",
    "    \"\"\"\n",
    "    scores = []                        # list containing scores from each episode\n",
    "    scores_window = deque(maxlen=100)  # last 100 scores\n",
    "    moving_avgs = []                   # list containing scores window averages\n",
    "    eps = eps_start                    # initialize epsilon\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "        state = env.reset()\n",
    "        score = 0\n",
    "        for t in range(max_t):\n",
    "            action = agent.act(state, eps)\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            agent.step(state, action, reward, next_state, done)\n",
    "            state = next_state\n",
    "            score += reward\n",
    "            if done:\n",
    "                break \n",
    "        scores_window.append(score)       # save most recent score\n",
    "        scores.append(score)              # save most recent score\n",
    "        moving_avgs.append(np.mean(scores_window)) # save the moving average\n",
    "        eps = max(eps_end, eps_decay*eps) # decrease epsilon\n",
    "        print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)), end=\"\")\n",
    "        if i_episode % 100 == 0:\n",
    "            print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)))\n",
    "        if np.mean(scores_window)>=200.0:\n",
    "            print('\\nEnvironment solved in {:d} episodes!\\tAverage Score: {:.2f}'.format(i_episode-100, np.mean(scores_window)))\n",
    "            torch.save(agent.qnetwork_local.state_dict(), 'checkpoint.pth')\n",
    "            break\n",
    "    return scores, moving_avgs\n",
    "\n",
    "scores, moving_avgs = dqn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0961073da0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(np.arange(len(scores)), scores, c='k', label='DQN agent score', alpha=0.3)\n",
    "plt.plot(np.arange(len(scores)), moving_avgs, c='r', label='Moving average score')\n",
    "plt.plot(np.arange(len(scores)), [200.0]*len(scores), c='b', label='baseline', alpha=0.5)\n",
    "plt.ylabel('Score')\n",
    "plt.xlabel('Episode #')\n",
    "plt.legend();\n",
    "plt.grid()\n",
    "# plt.savefig('scores_plot.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Watch a Smart Agent!\n",
    "\n",
    "In the next code cell, you will load the trained weights from file to watch a smart agent!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc77a5a7e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load the weights from file\n",
    "if torch.cuda.is_available():\n",
    "    map_location=lambda storage, loc: storage.cuda()\n",
    "else:\n",
    "    map_location='cpu'\n",
    "\n",
    "agent.qnetwork_local.load_state_dict(torch.load('checkpoint.pth', map_location=map_location))\n",
    "\n",
    "for i in range(3):\n",
    "    state = env.reset()\n",
    "    img = plt.imshow(env.render(mode='rgb_array'))\n",
    "    for j in range(200):\n",
    "        action = agent.act(state)\n",
    "        img.set_data(env.render(mode='rgb_array')) \n",
    "        plt.axis('off')\n",
    "        display.display(plt.gcf())\n",
    "        display.clear_output(wait=True)\n",
    "        state, reward, done, _ = env.step(action)\n",
    "        if done:\n",
    "            break \n",
    "            \n",
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Explore\n",
    "\n",
    "In this exercise, you have implemented a DQN agent and demonstrated how to use it to solve an OpenAI Gym environment.  To continue your learning, you are encouraged to complete any (or all!) of the following tasks:\n",
    "- Amend the various hyperparameters and network architecture to see if you can get your agent to solve the environment faster.  Once you build intuition for the hyperparameters that work well with this environment, try solving a different OpenAI Gym task with discrete actions!\n",
    "- You may like to implement some improvements such as prioritized experience replay, Double DQN, or Dueling DQN! \n",
    "- Write a blog post explaining the intuition behind the DQN algorithm and demonstrating how to use it to solve an RL environment of your choosing.  "
   ]
  }
 ],
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