Continuous_Control.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Continuous Control\n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, you will learn how to use the Unity ML-Agents environment for the second project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) program.\n",
    "\n",
    "### 1. Start the Environment\n",
    "\n",
    "We begin by importing the necessary packages.  If the code cell below returns an error, please revisit the project instructions to double-check that you have installed [Unity ML-Agents](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Installation.md) and [NumPy](http://www.numpy.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "from ddpg_agent import Agents\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will start the environment!  **_Before running the code cell below_**, change the `file_name` parameter to match the location of the Unity environment that you downloaded.\n",
    "\n",
    "- **Mac**: `\"path/to/Reacher.app\"`\n",
    "- **Windows** (x86): `\"path/to/Reacher_Windows_x86/Reacher.exe\"`\n",
    "- **Windows** (x86_64): `\"path/to/Reacher_Windows_x86_64/Reacher.exe\"`\n",
    "- **Linux** (x86): `\"path/to/Reacher_Linux/Reacher.x86\"`\n",
    "- **Linux** (x86_64): `\"path/to/Reacher_Linux/Reacher.x86_64\"`\n",
    "- **Linux** (x86, headless): `\"path/to/Reacher_Linux_NoVis/Reacher.x86\"`\n",
    "- **Linux** (x86_64, headless): `\"path/to/Reacher_Linux_NoVis/Reacher.x86_64\"`\n",
    "\n",
    "For instance, if you are using a Mac, then you downloaded `Reacher.app`.  If this file is in the same folder as the notebook, then the line below should appear as follows:\n",
    "```\n",
    "env = UnityEnvironment(file_name=\"Reacher.app\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "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\tgoal_size -> 5.0\n",
      "\t\tgoal_speed -> 1.0\n",
      "Unity brain name: ReacherBrain\n",
      "        Number of Visual Observations (per agent): 0\n",
      "        Vector Observation space type: continuous\n",
      "        Vector Observation space size (per agent): 33\n",
      "        Number of stacked Vector Observation: 1\n",
      "        Vector Action space type: continuous\n",
      "        Vector Action space size (per agent): 4\n",
      "        Vector Action descriptions: , , , \n"
     ]
    }
   ],
   "source": [
    "env = UnityEnvironment(file_name='Reacher_Linux/Reacher.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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ReacherBrain\n"
     ]
    }
   ],
   "source": [
    "# get the default brain\n",
    "brain_name = env.brain_names[0]\n",
    "print(brain_name)\n",
    "brain = env.brains[brain_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Examine the State and Action Spaces\n",
    "\n",
    "In this environment, a double-jointed arm can move to target locations. A reward of `+0.1` is provided for each step that the agent's hand is in the goal location. Thus, the goal of your agent is to maintain its position at the target location for as many time steps as possible.\n",
    "\n",
    "The observation space consists of `33` variables corresponding to position, rotation, velocity, and angular velocities of the arm.  Each action is a vector with four numbers, corresponding to torque applicable to two joints.  Every entry in the action vector must be a number between `-1` and `1`.\n",
    "\n",
    "The simulation contains `20` identical agents, each with its own copy of the environment.\n",
    "\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",
    "\n",
    "Run the code cell below to print some information about the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of agents: 20\n",
      "Size of each action: 4\n",
      "There are 20 agents. Each observes a state with length: 33\n",
      "The state for the first agent looks like: [ 0.00000000e+00 -4.00000000e+00  0.00000000e+00  1.00000000e+00\n",
      " -0.00000000e+00 -0.00000000e+00 -4.37113883e-08  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00 -1.00000000e+01  0.00000000e+00\n",
      "  1.00000000e+00 -0.00000000e+00 -0.00000000e+00 -4.37113883e-08\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  5.75471878e+00 -1.00000000e+00\n",
      "  5.55726624e+00  0.00000000e+00  1.00000000e+00  0.00000000e+00\n",
      " -1.68164849e-01]\n"
     ]
    }
   ],
   "source": [
    "# reset the environment\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "\n",
    "# number of agents\n",
    "num_agents = len(env_info.agents)\n",
    "print('Number of agents:', num_agents)\n",
    "\n",
    "# size of each action\n",
    "action_size = brain.vector_action_space_size\n",
    "print('Size of each action:', action_size)\n",
    "\n",
    "# examine the state space \n",
    "states = env_info.vector_observations\n",
    "state_size = states.shape[1]\n",
    "print('There are {} agents. Each observes a state with length: {}'.format(states.shape[0], state_size))\n",
    "print('The state for the first agent looks like:', states[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Train the Agent with DDPG\n",
    "\n",
    "Run the code cell below to train the agent from scratch.  Alternatively, you can skip to the next code cell to load the pre-trained weights from file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "agent = Agents(state_size=state_size, action_size=action_size,\n",
    "               num_agents = num_agents, random_seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 100\tAverage Score: 16.71\tScore: 35.326\n",
      "Episode 200\tAverage Score: 34.56\tScore: 34.425\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def ddpg(n_episodes=200, max_t=1000):\n",
    "    scores_deque = deque(maxlen=100)\n",
    "    scores = []\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "        env_info = env.reset(train_mode=True)[brain_name]\n",
    "        state = env_info.vector_observations\n",
    "        agent.reset()\n",
    "        score = np.zeros(num_agents)\n",
    "        for t in range(max_t):\n",
    "            action = agent.act(state)\n",
    "            env_info = env.step(action)[brain_name]\n",
    "            next_state = env_info.vector_observations\n",
    "            rewards = env_info.rewards\n",
    "            dones = env_info.local_done\n",
    "            agent.step(state, action, rewards, next_state, dones)\n",
    "            state = next_state\n",
    "            score += rewards\n",
    "            if np.any(dones):\n",
    "                print('\\tSteps: ', t)\n",
    "                break \n",
    "        scores_deque.append(np.mean(score))\n",
    "        scores.append(np.mean(score))\n",
    "        print('\\rEpisode {}\\tAverage Score: {:.2f}\\tScore: {:.3f}'.format(i_episode, np.mean(scores_deque), np.mean(score)), end=\"\")\n",
    "        if i_episode % 100 == 0:\n",
    "            print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)))\n",
    "            torch.save(agent.actor_local.state_dict(), 'checkpoint_actor.pth')\n",
    "            torch.save(agent.critic_local.state_dict(), 'checkpoint_critic.pth')   \n",
    "    return scores\n",
    "\n",
    "scores = ddpg()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(np.arange(1, len(scores)+1), scores)\n",
    "plt.ylabel('Score')\n",
    "plt.xlabel('Episode #')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save temporary results if the training is canceled preliminary\n",
    "torch.save(agent.actor_local.state_dict(), 'checkpoint_actor.pth')\n",
    "torch.save(agent.critic_local.state_dict(), 'checkpoint_critic.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When finished, you can close the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "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! The environment and the necessary packages should be loaded in advance (run cell \"1. Start the Environment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "env_info = env.reset(train_mode=False)[brain_name]     # reset the environment   \n",
    "\n",
    "num_agents = len(env_info.agents)\n",
    "action_size = brain.vector_action_space_size\n",
    "states = env_info.vector_observations\n",
    "state_size = states.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = Agents(state_size=state_size, action_size=action_size,\n",
    "               num_agents = num_agents, random_seed=0)\n",
    "agent.actor_local.load_state_dict(torch.load('checkpoint_actor.pth'))\n",
    "agent.critic_local.load_state_dict(torch.load('checkpoint_critic.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total score (averaged over agents) this episode: 35.63399920351803\n"
     ]
    }
   ],
   "source": [
    "states = env_info.vector_observations                  # get the current state (for each agent)\n",
    "scores = np.zeros(num_agents)                          # initialize the score (for each agent)\n",
    "agent.reset() \n",
    "while True:\n",
    "    actions = agent.act(states)                         # select an action (for each agent)\n",
    "    actions = np.clip(actions, -1, 1)                  # all actions between -1 and 1\n",
    "    env_info = env.step(actions)[brain_name]           # send all actions to tne environment\n",
    "    next_states = env_info.vector_observations         # get next state (for each agent)\n",
    "    rewards = env_info.rewards                         # get reward (for each agent)\n",
    "    dones = env_info.local_done                        # see if episode finished\n",
    "    scores += env_info.rewards                         # update the score (for each agent)\n",
    "    states = next_states                               # roll over states to next time step\n",
    "    if np.any(dones):                                  # exit loop if episode finished\n",
    "        break\n",
    "print('Total score (averaged over agents) this episode: {}'.format(np.mean(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "env.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "drlnd",
   "language": "python",
   "name": "drlnd"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
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}