Navigation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Navigation\n",
    "\n",
    "---\n",
    "\n",
    "You are welcome to use this coding environment to train your agent for the project.  Follow the instructions below to get started!\n",
    "\n",
    "### 1. Start the Environment\n",
    "\n",
    "Run the next code cell to install a few packages.  This line will take a few minutes to run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mERROR: Invalid requirement: './python'\r\n",
      "Hint: It looks like a path. File './python' does not exist.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip -q install ./python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The environment is already saved in the Workspace and can be accessed at the file path provided below.  Please run the next code cell without making any changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnityEnvironmentException",
     "evalue": "Couldn't launch the Banana environment. Provided filename does not match any environments.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnityEnvironmentException\u001b[0m                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-0b6dbeec0db6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# please do not modify the line below\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0menv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUnityEnvironment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"/data/Banana_Linux_NoVis/Banana.x86_64\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/envs/unity_project/lib/python3.6/site-packages/unityagents/environment.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, file_name, worker_id, base_port, curriculum, seed, docker_training, no_graphics)\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;31m# and the communicator will directly try to connect to an existing unity environment.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfile_name\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecutable_launcher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdocker_training\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mno_graphics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Start training by pressing the Play button in the Unity Editor.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/unity_project/lib/python3.6/site-packages/unityagents/environment.py\u001b[0m in \u001b[0;36mexecutable_launcher\u001b[0;34m(self, file_name, docker_training, no_graphics)\u001b[0m\n\u001b[1;32m    182\u001b[0m             raise UnityEnvironmentException(\"Couldn't launch the {0} environment. \"\n\u001b[1;32m    183\u001b[0m                                             \u001b[0;34m\"Provided filename does not match any environments.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m                                             .format(true_filename))\n\u001b[0m\u001b[1;32m    185\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This is the launch string {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlaunch_string\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mUnityEnvironmentException\u001b[0m: Couldn't launch the Banana environment. Provided filename does not match any environments."
     ]
    }
   ],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np\n",
    "\n",
    "# please do not modify the line below\n",
    "env = UnityEnvironment(file_name=\"/data/Banana_Linux_NoVis/Banana.x86_64\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Environments contain **_brains_** which are responsible for deciding the actions of their associated agents. Here we check for the first brain available, and set it as the default brain we will be controlling from Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the default brain\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Examine the State and Action Spaces\n",
    "\n",
    "Run the code cell below to print some information about the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of agents: 1\n",
      "Number of actions: 4\n",
      "States look like: [ 1.          0.          0.          0.          0.84408134  0.          0.\n",
      "  1.          0.          0.0748472   0.          1.          0.          0.\n",
      "  0.25755     1.          0.          0.          0.          0.74177343\n",
      "  0.          1.          0.          0.          0.25854847  0.          0.\n",
      "  1.          0.          0.09355672  0.          1.          0.          0.\n",
      "  0.31969345  0.          0.        ]\n",
      "States have length: 37\n"
     ]
    }
   ],
   "source": [
    "# reset the environment\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "\n",
    "# number of agents in the environment\n",
    "print('Number of agents:', len(env_info.agents))\n",
    "\n",
    "# number of actions\n",
    "action_size = brain.vector_action_space_size\n",
    "print('Number of actions:', action_size)\n",
    "\n",
    "# examine the state space \n",
    "state = env_info.vector_observations[0]\n",
    "print('States look like:', state)\n",
    "state_size = len(state)\n",
    "print('States have length:', state_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Take Random Actions in the Environment\n",
    "\n",
    "In the next code cell, you will learn how to use the Python API to control the agent and receive feedback from the environment.\n",
    "\n",
    "Note that **in this coding environment, you will not be able to watch the agent while it is training**, and you should set `train_mode=True` to restart the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.0\n"
     ]
    }
   ],
   "source": [
    "env_info = env.reset(train_mode=True)[brain_name] # reset the environment\n",
    "state = env_info.vector_observations[0]            # get the current state\n",
    "score = 0                                          # initialize the score\n",
    "while True:\n",
    "    action = np.random.randint(action_size)        # select an action\n",
    "    env_info = env.step(action)[brain_name]        # send the action to the environment\n",
    "    next_state = env_info.vector_observations[0]   # get the next state\n",
    "    reward = env_info.rewards[0]                   # get the reward\n",
    "    done = env_info.local_done[0]                  # see if episode has finished\n",
    "    score += reward                                # update the score\n",
    "    state = next_state                             # roll over the state to next time step\n",
    "    if done:                                       # exit loop if episode finished\n",
    "        break\n",
    "    \n",
    "print(\"Score: {}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When finished, you can close the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. It's Your Turn!\n",
    "\n",
    "Now it's your turn to train your own agent to solve the environment!  A few **important notes**:\n",
    "- When training the environment, set `train_mode=True`, so that the line for resetting the environment looks like the following:\n",
    "```python\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "```\n",
    "- To structure your work, you're welcome to work directly in this Jupyter notebook, or you might like to start over with a new file!  You can see the list of files in the workspace by clicking on **_Jupyter_** in the top left corner of the notebook.\n",
    "- In this coding environment, you will not be able to watch the agent while it is training.  However, **_after training the agent_**, you can download the saved model weights to watch the agent on your own machine! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import random\n",
    "from collections import deque, namedtuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BUFFER_SIZE = int(1e5)  # replay buffer size\n",
    "BATCH_SIZE = 64         # minibatch size\n",
    "GAMMA = 0.99            # discount factor\n",
    "TAU = 1e-3              # for soft update of target parameters\n",
    "LR = 5e-4               # learning rate \n",
    "UPDATE_EVERY = 4        # how often to update the network\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "env_info = env.reset(train_mode=True)[brain_name] # reset the environment\n",
    "state = env_info.vector_observations[0]            # get the current state\n",
    "score = 0   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dqn_agent import Agent\n",
    "\n",
    "agent = Agent(state_size=37, action_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 100\tAverage Score: 6.30\n",
      "Episode 200\tAverage Score: 8.43\n",
      "Episode 300\tAverage Score: 10.64\n",
      "Episode 400\tAverage Score: 11.09\n",
      "Episode 500\tAverage Score: 12.73\n",
      "Episode 600\tAverage Score: 11.92\n",
      "Episode 675\tAverage Score: 13.00\n",
      "Environment solved in 575 episodes!\tAverage Score: 13.00\n",
      "\n",
      "Total Training time = 15.4 min\n"
     ]
    }
   ],
   "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",
    "    eps = eps_start                    # initialize epsilon\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "        \n",
    "        env_info = env.reset(train_mode=True)[brain_name]  # reset the environment\n",
    "        state = env_info.vector_observations[0]            # get the current state\n",
    "\n",
    "        score = 0\n",
    "        for t in range(max_t):           \n",
    "            #action = np.random.randint(action_size)        # select an action\n",
    "            action = agent.act(state)\n",
    "            env_info = env.step(action)[brain_name]        # send the action to the environment\n",
    "            next_state = env_info.vector_observations[0]   # get the next state\n",
    "            reward = env_info.rewards[0]                   # get the reward\n",
    "            done = env_info.local_done[0]                  # see if episode has finished\n",
    "            \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",
    "        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)>=13.0:\n",
    "            print('\\nEnvironment solved in {:d} episodes!\\tAverage Score: {:.2f}'.format(i_episode-100, np.mean(scores_window)))\n",
    "            torch.save(agent.local_nn.state_dict(), 'checkpoint.pth')\n",
    "            break\n",
    "    return scores\n",
    "\n",
    "\n",
    "# Train the agent using DQN\n",
    "start_time = time.time() # Monitor Training Time  \n",
    "scores = dqn(n_episodes=2000, max_t=1000, eps_start=1.0, eps_end=0.01, eps_decay=0.995)\n",
    "print(\"\\nTotal Training time = {:.1f} min\".format((time.time()-start_time)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3cf8c3a2e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(np.arange(len(scores)), scores)\n",
    "plt.title('Score (Rewards)')\n",
    "plt.ylabel('Score')\n",
    "plt.xlabel('Episode #')\n",
    "plt.grid(True)      \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. To Test\n",
    "\n",
    "Now it is our turn to test our own agent to solve the environment!\n",
    "- When testing the environment, set `train_mode=False`\n",
    "\n",
    "Also, set the path to load the checkpoint and specify the map location if you trained the agent on GPU and testing it on CPU.\n",
    "I have specified two checkpoints - checkpoints0, checkpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dqn_agent import Agent\n",
    "\n",
    "agent = Agent(state_size=37, action_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env_info = env.reset(train_mode=False)[brain_name] # reset the environment\n",
    "state = env_info.vector_observations[0]            # get the current state\n",
    "score = 0                                          # initialize the score\n",
    "done = False\n",
    "while not done:\n",
    "    action = agent.act(state)        # select an action\n",
    "    env_info = env.step(action)[brain_name]        # send the action to the environment\n",
    "    next_state = env_info.vector_observations[0]   # get the next state\n",
    "    reward = env_info.rewards[0]                   # get the reward\n",
    "    done = env_info.local_done[0]                  # see if episode has finished\n",
    "    score += reward                                # update the score\n",
    "    state = next_state                             # roll over the state to next time step\n",
    "    if done:                                       # exit loop if episode finished\n",
    "        break\n",
    "    \n",
    "print(\"Score: {}\".format(score))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
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   "pygments_lexer": "ipython3",
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