Tennis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Collaboration and Competition\n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, you will learn how to use the Unity ML-Agents environment for the third 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:https://www.numpy.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np\n",
    "from maddpg import MADDPG\n",
    "from buffer import ReplayBuffer\n",
    "import torch\n",
    "from collections import namedtuple, deque"
   ]
  },
  {
   "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/Tennis.app\"`\n",
    "- **Windows** (x86): `\"path/to/Tennis_Windows_x86/Tennis.exe\"`\n",
    "- **Windows** (x86_64): `\"path/to/Tennis_Windows_x86_64/Tennis.exe\"`\n",
    "- **Linux** (x86): `\"path/to/Tennis_Linux/Tennis.x86\"`\n",
    "- **Linux** (x86_64): `\"path/to/Tennis_Linux/Tennis.x86_64\"`\n",
    "- **Linux** (x86, headless): `\"path/to/Tennis_Linux_NoVis/Tennis.x86\"`\n",
    "- **Linux** (x86_64, headless): `\"path/to/Tennis_Linux_NoVis/Tennis.x86_64\"`\n",
    "\n",
    "For instance, if you are using a Mac, then you downloaded `Tennis.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=\"Tennis.app\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "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: TennisBrain\n",
      "        Number of Visual Observations (per agent): 0\n",
      "        Vector Observation space type: continuous\n",
      "        Vector Observation space size (per agent): 8\n",
      "        Number of stacked Vector Observation: 3\n",
      "        Vector Action space type: continuous\n",
      "        Vector Action space size (per agent): 2\n",
      "        Vector Action descriptions: , \n"
     ]
    }
   ],
   "source": [
    "env = UnityEnvironment(file_name=\"Tennis_Windows_x86/Tennis.exe\")"
   ]
  },
  {
   "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",
    "In this environment, two agents control rackets to bounce a ball over a net. If an agent hits the ball over the net, it receives a reward of +0.1.  If an agent lets a ball hit the ground or hits the ball out of bounds, it receives a reward of -0.01.  Thus, the goal of each agent is to keep the ball in play.\n",
    "\n",
    "The observation space consists of 8 variables corresponding to the position and velocity of the ball and racket. Two continuous actions are available, corresponding to movement toward (or away from) the net, and jumping. \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: 2\n",
      "Size of each action: 2\n",
      "There are 2 agents. Each observes a state with length: 24\n",
      "The state for the first agent looks like: [ 0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.         -6.65278625 -1.5\n",
      " -0.          0.          6.83172083  6.         -0.          0.        ]\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. 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 agents and receive feedback from the environment.\n",
    "\n",
    "Once this cell is executed, you will watch the agents' performance, if they select actions at random with each time step.  A window should pop up that allows you to observe the agents.\n",
    "\n",
    "Of course, as part of the project, you'll have to change the code so that the agents are able to use their experiences to gradually choose better actions when interacting with the environment!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(5):                                      # play game for 5 episodes\n",
    "    env_info = env.reset(train_mode=True)[brain_name]     # reset the environment    \n",
    "    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",
    "    step = 0\n",
    "    while True:\n",
    "        actions = np.random.randn(num_agents, action_size) # 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",
    "        step += 1\n",
    "        if np.any(dones):                                  # exit loop if episode finished\n",
    "            print(step)\n",
    "            break\n",
    "    print('Score (max over agents) from episode {}: {}'.format(i, np.max(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "buffersize = (100000) #change 1000 to 5000\n",
    "batchsize = 100 #change 100 to 500\n",
    "buffer = ReplayBuffer(buffersize,batchsize,0)\n",
    "maddpg = MADDPG(state_size, action_size, num_agents, batchsize ,discount_factor=0.95, tau=0.02,)\n",
    "uscores = []\n",
    "plotscores = []\n",
    "scores_window = deque(maxlen=100)\n",
    "noise = 2\n",
    "noise_reduction = 0.9999\n",
    "for i_episode in range(5000):\n",
    "        env_info = env.reset(train_mode=True)[brain_name]\n",
    "        states = env_info.vector_observations\n",
    "        for agent in maddpg.maddpg_agent:\n",
    "            agent.noise.reset()\n",
    "        score1 = 0\n",
    "        score2 = 0\n",
    "        totscores = np.zeros(num_agents)\n",
    "        while True:\n",
    "            actions = maddpg.act(torch.tensor(states, dtype=torch.float),noise)\n",
    "            noise = noise * noise_reduction\n",
    "            env_info = env.step(actions)[brain_name]\n",
    "            actions = np.asarray(actions)\n",
    "            next_states = env_info.vector_observations\n",
    "            rewards = env_info.rewards\n",
    "            dones = env_info.local_done\n",
    "            buffer.add(states, actions, rewards, next_states,dones)\n",
    "            states = next_states\n",
    "            score1 += rewards[0]\n",
    "            score2 += rewards[1]\n",
    "            totscores += rewards\n",
    "            if (len(buffer) > batchsize) and (i_episode % 1 == 0):\n",
    "                for i in range(num_agents):\n",
    "                    samples = buffer.sample()\n",
    "                    maddpg.update(samples, i)\n",
    "                maddpg.update_targets() \n",
    "            if i_episode % 50 == 0:\n",
    "                torch.save(maddpg.maddpg_agent[0].actor.state_dict(), 'checkpoint_actor1.pth')\n",
    "                torch.save(maddpg.maddpg_agent[1].actor.state_dict(), 'checkpoint_actor2.pth')\n",
    "                torch.save(maddpg.maddpg_agent[0].critic.state_dict(), 'checkpoint_critic1.pth')\n",
    "                torch.save(maddpg.maddpg_agent[1].critic.state_dict(), 'checkpoint_critic2.pth')\n",
    "            if np.any(dones):\n",
    "                break\n",
    "        uscores.append(max(score1, score2))\n",
    "        scores_window.append(np.max(totscores))\n",
    "        plotscores.append(np.mean(scores_window))\n",
    "        print('\\rEpisode {}\\tAvgScore: {:.4f}\\tMaxScore: {:.4f}'.format(i_episode+1, np.mean(scores_window),np.max(totscores)))    \n",
    "        if np.mean(scores_window) > 0.5:\n",
    "            torch.save(maddpg.maddpg_agent[0].actor.state_dict(), 'checkpoint_actor1.pth')\n",
    "            torch.save(maddpg.maddpg_agent[1].actor.state_dict(), 'checkpoint_actor2.pth')\n",
    "            torch.save(maddpg.maddpg_agent[0].critic.state_dict(), 'checkpoint_critic1.pth')\n",
    "            torch.save(maddpg.maddpg_agent[1].critic.state_dict(), 'checkpoint_critic2.pth')\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### When finished, you can close the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http:https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http:https://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http:https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "plt.plot(np.arange(1,553),plotscores)\n",
    "plt.ylabel('Average Score')\n",
    "plt.xlabel('Episode #')\n",
    "plt.title(\"Average Score vs Episode\")\n",
    "plt.savefig('07282019-01.png',bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "314\n",
      "Score (max over agents) from episode 0: 0.800000011920929\n",
      "220\n",
      "Score (max over agents) from episode 1: 0.6000000089406967\n",
      "203\n",
      "Score (max over agents) from episode 2: 0.5000000074505806\n",
      "102\n",
      "Score (max over agents) from episode 3: 0.30000000447034836\n",
      "106\n",
      "Score (max over agents) from episode 4: 0.30000000447034836\n"
     ]
    }
   ],
   "source": [
    "#For GIF\n",
    "for i in range(5):                                      # play game for 5 episodes\n",
    "    env_info = env.reset(train_mode=False)[brain_name]     # reset the environment    \n",
    "    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",
    "    step = 0\n",
    "    while True:\n",
    "        actions = maddpg.act(torch.tensor(states, dtype=torch.float),noise)\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",
    "        step += 1\n",
    "        if np.any(dones):                                  # exit loop if episode finished\n",
    "            print(step)\n",
    "            break\n",
    "    print('Score (max over agents) from episode {}: {}'.format(i, np.max(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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!  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",
    "```"
   ]
  }
 ],
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