cgames/02_space_invader/space_invader_ppo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "colab_type": "text",
    "id": "LN0nZwyMGadB"
   },
   "outputs": [],
   "source": [
    "# Space Invader with Poximal Policy Optimization\n",
    "\n",
    "## Step 1: Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import gym\n",
    "import random\n",
    "import torch\n",
    "import numpy as np\n",
    "from collections import deque\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import clear_output\n",
    "import math\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../../')\n",
    "from algos.agents import PPOAgent\n",
    "from algos.models import ActorCnn, CriticCnn\n",
    "from algos.preprocessing.stack_frame import preprocess_frame, stack_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "colab_type": "text",
    "id": "tfo8jleHGadK"
   },
   "outputs": [],
   "source": [
    "## Step 2: Create our environment\n",
    "\n",
    "Initialize the environment in the code cell below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[0, 592379725]"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env = gym.make('SpaceInvaders-v0')\n",
    "env.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Device:  cuda\n"
    }
   ],
   "source": [
    "# if gpu is to be used\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"Device: \", device)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "colab_type": "text",
    "id": "nS221MgXGadP"
   },
   "outputs": [],
   "source": [
    "## Step 3: Viewing our Enviroment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "The size of frame is:  (210, 160, 3)\nNo. of Actions:  6\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"The size of frame is: \", env.observation_space.shape)\n",
    "print(\"No. of Actions: \", env.action_space.n)\n",
    "env.reset()\n",
    "plt.figure()\n",
    "plt.imshow(env.reset())\n",
    "plt.title('Original Frame')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Execute the code cell below to play Pong with a random policy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_play():\n",
    "    score = 0\n",
    "    env.reset()\n",
    "    while True:\n",
    "        env.render()\n",
    "        action = env.action_space.sample()\n",
    "        state, reward, done, _ = env.step(action)\n",
    "        score += reward\n",
    "        if done:\n",
    "            env.close()\n",
    "            print(\"Your Score at end of game is: \", score)\n",
    "            break\n",
    "random_play()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "colab_type": "text",
    "id": "Sr52nmcpGada"
   },
   "outputs": [],
   "source": [
    "## Step 4:Preprocessing Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "env.reset()\n",
    "plt.figure()\n",
    "plt.imshow(preprocess_frame(env.reset(), (8, -12, -12, 4), 84), cmap=\"gray\")\n",
    "plt.title('Pre Processed image')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "colab_type": "text",
    "id": "mJMc3HA8Gade"
   },
   "outputs": [],
   "source": [
    "## Step 5: Stacking Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stack_frames(frames, state, is_new=False):\n",
    "    frame = preprocess_frame(state, (8, -12, -12, 4), 84)\n",
    "    frames = stack_frame(frames, frame, is_new)\n",
    "\n",
    "    return frames\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step 6: Creating our Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_SHAPE = (4, 84, 84)\n",
    "ACTION_SIZE = env.action_space.n\n",
    "SEED = 0\n",
    "GAMMA = 0.99           # discount factor\n",
    "ALPHA= 0.0001          # Actor learning rate\n",
    "BETA = 0.0001          # Critic learning rate\n",
    "TAU = 0.95\n",
    "BATCH_SIZE = 32\n",
    "PPO_EPOCH = 5\n",
    "CLIP_PARAM = 0.2\n",
    "UPDATE_EVERY = 1000     # how often to update the network \n",
    "\n",
    "\n",
    "agent = PPOAgent(INPUT_SHAPE, ACTION_SIZE, SEED, device, GAMMA, ALPHA, BETA, TAU, UPDATE_EVERY, BATCH_SIZE, PPO_EPOCH, CLIP_PARAM, ActorCnn, CriticCnn)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step 7: Watching untrained agent play"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# watch an untrained agent\n",
    "state = stack_frames(None, env.reset(), True) \n",
    "for j in range(200):\n",
    "    env.render()\n",
    "    action, _, _ = agent.act(state)\n",
    "    next_state, reward, done, _ = env.step(action)\n",
    "    state = stack_frames(state, next_state, False)\n",
    "    if done:\n",
    "        break \n",
    "        \n",
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step 8: Loading Agent\n",
    "Uncomment line to load a pretrained agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_epoch = 0\n",
    "scores = []\n",
    "scores_window = deque(maxlen=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step 9: Train the Agent with DQN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(n_episodes=1000):\n",
    "    \"\"\"\n",
    "    Params\n",
    "    ======\n",
    "        n_episodes (int): maximum number of training episodes\n",
    "    \"\"\"\n",
    "    for i_episode in range(start_epoch + 1, n_episodes+1):\n",
    "        state = stack_frames(None, env.reset(), True)\n",
    "        score = 0\n",
    "        while True:\n",
    "            action, log_prob, value = agent.act(state)\n",
    "            next_state, reward, done, info = env.step(action)\n",
    "            score += reward\n",
    "            next_state = stack_frames(state, next_state, False)\n",
    "            agent.step(state, action, value, log_prob, reward, done, next_state)\n",
    "            if done:\n",
    "                break\n",
    "            else:\n",
    "                state = next_state\n",
    "        scores_window.append(score)       # save most recent score\n",
    "        scores.append(score)              # save most recent score\n",
    "        \n",
    "        clear_output(True)\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "        plt.plot(np.arange(len(scores)), scores)\n",
    "        plt.ylabel('Score')\n",
    "        plt.xlabel('Episode #')\n",
    "        plt.show()\n",
    "        print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)), end=\"\")\n",
    "    \n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\rEpisode 411\tAverage Score: -19.85"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-b9faf9c8607f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-12-2f6574c98137>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(n_episodes)\u001b[0m\n\u001b[0;32m      6\u001b[0m     \"\"\"\n\u001b[0;32m      7\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi_episode\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart_epoch\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_episodes\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\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----> 8\u001b[1;33m         \u001b[0mstate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstack_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\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;32mTrue\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      9\u001b[0m         \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ai\\lib\\site-packages\\gym\\wrappers\\time_limit.py\u001b[0m in \u001b[0;36mreset\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_elapsed_steps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\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[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\.conda\\envs\\ai\\lib\\site-packages\\gym\\envs\\atari\\atari_env.py\u001b[0m in \u001b[0;36mreset\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    142\u001b[0m     \u001b[1;31m# return: (states, observations)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    143\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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--> 144\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0male\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_game\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    145\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_obs\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    146\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\ai\\lib\\site-packages\\atari_py\\ale_python_interface.py\u001b[0m in \u001b[0;36mreset_game\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mreset_game\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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--> 158\u001b[1;33m         \u001b[0male_lib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_game\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\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    159\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    160\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgetLegalActionSet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "scores = train(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
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    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(np.arange(len(scores)), scores)\n",
    "plt.ylabel('Score')\n",
    "plt.xlabel('Episode #')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step 10: Watch a Smart Agent!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "You Final score is: -18.0\n"
    }
   ],
   "source": [
    "score = 0\n",
    "state = stack_frames(None, env.reset(), True)\n",
    "while True:\n",
    "    env.render()\n",
    "    action, _, _ = agent.act(state)\n",
    "    next_state, reward, done, _ = env.step(action)\n",
    "    score += reward\n",
    "    state = stack_frames(state, next_state, False)\n",
    "    if done:\n",
    "        print(\"You Final score is:\", score)\n",
    "        break \n",
    "env.close()"
   ]
  }
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