01_basic/train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "colab_type": "text",
    "id": "LN0nZwyMGadB"
   },
   "outputs": [],
   "source": [
    "# Traning Agent with DQN\n",
    "\n",
    "## Step 1: Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import random\n",
    "import math\n",
    "from collections import deque\n",
    "\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "\n",
    "from gym_unity.envs import UnityEnv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dqn_agent import DQNAgent\n",
    "from model import DQNLinear"
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "INFO:mlagents_envs:Connected new brain:\nBasic?team=0\nINFO:gym_unity:1 agents within environment.\n"
    }
   ],
   "source": [
    "ENV_PATH = '../unity_envs/Basic/'\n",
    "ENV_NAME = 'Unity Environment'\n",
    "env = UnityEnv(ENV_PATH + ENV_NAME, worker_id=0, no_graphics=True)"
   ]
  },
  {
   "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:  20\nNo. of Actions:  MultiDiscrete([3])\n"
    }
   ],
   "source": [
    "print(\"The size of frame is: \", env.observation_space.shape[0])\n",
    "print(\"No. of Actions: \", env.action_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step 4: Creating our Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "STATE_SIZE = env.observation_space.shape[0]\n",
    "ACTION_SIZE = 3\n",
    "GAMMA = 0.99           # discount factor\n",
    "BUFFER_SIZE = int(1e5)      # replay buffer size\n",
    "BATCH_SIZE = 32        # Update batch size\n",
    "LR = 1e-3              # learning rate \n",
    "TAU = 1e-2             # for soft update of target parameters\n",
    "UPDATE_EVERY = 5       # how often to update the network \n",
    "\n",
    "agent = DQNAgent(STATE_SIZE, ACTION_SIZE, BUFFER_SIZE, BATCH_SIZE, GAMMA, LR, TAU, UPDATE_EVERY, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step 5: Creating Helper Function\n",
    "\n",
    "### Eplision Helper Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "EPS_START = 0.99       # starting value of epsilon\n",
    "EPS_END = 0.01         # Ending value of epsilon\n",
    "EPS_DECAY = 200        # Rate by which epsilon to be decayed\n",
    "\n",
    "epsilon_by_epsiode = lambda frame_idx: EPS_END + (EPS_START - EPS_END) * math.exp(-1. * frame_idx / EPS_DECAY)\n",
    "\n",
    "plt.plot([epsilon_by_epsiode(i) for i in range(2000)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Plot Traning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_result(scores):\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"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step 6: Train the Agent with DQN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(n_episodes, scores_average_window, benchmark_reward):\n",
    "    \"\"\"\n",
    "    Params\n",
    "    ======\n",
    "        n_episodes (int): maximum number of training episodes\n",
    "        benchmark_reward (float): benchmark reward at which environment is solved.\n",
    "    \"\"\"\n",
    "    scores = []\n",
    "    scores_window = deque(maxlen=scores_average_window)\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "        state = env.reset()\n",
    "        score = 0\n",
    "        eps = epsilon_by_epsiode(i_episode)\n",
    "        while True:\n",
    "            action = agent.act(state, eps)\n",
    "            next_state, reward, done, info = env.step(action)\n",
    "            score += reward\n",
    "            agent.step(state, action, reward, next_state, done)\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",
    "        # printing and ploting results\n",
    "        clear_output(wait=True)\n",
    "        plot_result(scores)\n",
    "        print('\\rEpisode {}\\tAverage Score: {:.2f}\\tEplision: {:.3f}'.format(i_episode, np.mean(scores_window), eps))\n",
    "        \n",
    "        if float(np.mean(scores_window)) >= benchmark_reward:\n",
    "            agent.save_model(\"basic_solved.pth\", scores)\n",
    "            print(\"Yah Environment is solved :)\")\n",
    "            break\n",
    "    \n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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 504\tAverage Score: 0.92\tEplision: 0.089\nYah Environment is solved :)\nDone Training\n"
    }
   ],
   "source": [
    "BENCHMARK_REWARD = 0.9300\n",
    "SCORES_AVERAGE_WINDOW = 100\n",
    "NUM_EPISODES = 2000\n",
    "\n",
    "scores = train(NUM_EPISODES, SCORES_AVERAGE_WINDOW, BENCHMARK_REWARD)\n",
    "print(\"Done Training\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "INFO:mlagents_envs:Environment shut down with return code 0 (CTRL_C_EVENT).\n"
    }
   ],
   "source": [
    "env.close()"
   ]
  }
 ],
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