Navigation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "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!"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# !pip -q install ./python"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "INFO:unityagents:\n",
      "'Academy' started successfully!\n",
      "Unity Academy name: Academy\n",
      "        Number of Brains: 1\n",
      "        Number of External Brains : 1\n",
      "        Lesson number : 0\n",
      "        Reset Parameters :\n",
      "\t\t\n",
      "Unity brain name: BananaBrain\n",
      "        Number of Visual Observations (per agent): 0\n",
      "        Vector Observation space type: continuous\n",
      "        Vector Observation space size (per agent): 37\n",
      "        Number of stacked Vector Observation: 1\n",
      "        Vector Action space type: discrete\n",
      "        Vector Action space size (per agent): 4\n",
      "        Vector Action descriptions: , , , \n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# get the default brain\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 2. Examine the State and Action Spaces\n",
    "\n",
    "Run the code cell below to print some information about the environment."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "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"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "When finished, you can close the environment."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# env.close()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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! "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "from workspace_utils import active_session\n",
    "from agent import Agent\n",
    "from train import train\n",
    "\n",
    "with active_session():\n",
    "    agent = Agent(state_size, action_size)\n",
    "    scores = train(env, agent, brain_name)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "episode 100\tepsilon: 0.61\t avg score: 0.49\t\n",
      "episode 200\tepsilon: 0.37\t avg score: 3.50\t\n",
      "episode 300\tepsilon: 0.22\t avg score: 8.39\t\n",
      "episode 400\tepsilon: 0.13\t avg score: 11.43\t\n",
      "episode 455\tepsilon: 0.10\t avg score: 13.00\t\n",
      "env solved in 355 episodes!\t avg score: 13.00\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Plot the 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()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7c388ead68>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "import json\n",
    "\n",
    "with open(\"scores.json\", \"w\") as f:\n",
    "    json.dump(scores, f)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}