Quadcopter_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Train a Quadcopter How to Fly\n",
    "\n",
    "Design an agent to fly a quadcopter, and then train it using a reinforcement learning algorithm of your choice! \n",
    "\n",
    "Try to apply the techniques you have learnt, but also feel free to come up with innovative ideas and test them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instructions\n",
    "\n",
    "Take a look at the files in the directory to better understand the structure of the project. \n",
    "\n",
    "- `task.py`: Define your task (environment) in this file.\n",
    "- `agents/`: Folder containing reinforcement learning agents.\n",
    "    - `policy_search.py`: A sample agent has been provided here.\n",
    "    - `agent.py`: Develop your agent here.\n",
    "- `physics_sim.py`: This file contains the simulator for the quadcopter.  **DO NOT MODIFY THIS FILE**.\n",
    "\n",
    "For this project, you will define your own task in `task.py`.  Although we have provided a example task to get you started, you are encouraged to change it.  Later in this notebook, you will learn more about how to amend this file.\n",
    "\n",
    "You will also design a reinforcement learning agent in `agent.py` to complete your chosen task.  \n",
    "\n",
    "You are welcome to create any additional files to help you to organize your code.  For instance, you may find it useful to define a `model.py` file defining any needed neural network architectures.\n",
    "\n",
    "## Controlling the Quadcopter\n",
    "\n",
    "We provide a sample agent in the code cell below to show you how to use the sim to control the quadcopter.  This agent is even simpler than the sample agent that you'll examine (in `agents/policy_search.py`) later in this notebook!\n",
    "\n",
    "The agent controls the quadcopter by setting the revolutions per second on each of its four rotors.  The provided agent in the `Basic_Agent` class below always selects a random action for each of the four rotors.  These four speeds are returned by the `act` method as a list of four floating-point numbers.  \n",
    "\n",
    "For this project, the agent that you will implement in `agents/agent.py` will have a far more intelligent method for selecting actions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "class Basic_Agent():\n",
    "    def __init__(self, task):\n",
    "        self.task = task\n",
    "    \n",
    "    def act(self):\n",
    "        new_thrust = random.gauss(450., 25.)\n",
    "        return [new_thrust + random.gauss(0., 1.) for x in range(4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code cell below to have the agent select actions to control the quadcopter.  \n",
    "\n",
    "Feel free to change the provided values of `runtime`, `init_pose`, `init_velocities`, and `init_angle_velocities` below to change the starting conditions of the quadcopter.\n",
    "\n",
    "The `labels` list below annotates statistics that are saved while running the simulation.  All of this information is saved in a text file `data.txt` and stored in the dictionary `results`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import csv\n",
    "import numpy as np\n",
    "from task import Task\n",
    "\n",
    "# Modify the values below to give the quadcopter a different starting position.\n",
    "runtime = 5.                                     # time limit of the episode\n",
    "init_pose = np.array([0., 0., 10., 0., 0., 0.])  # initial pose\n",
    "init_velocities = np.array([0., 0., 0.])         # initial velocities\n",
    "init_angle_velocities = np.array([0., 0., 0.])   # initial angle velocities\n",
    "file_output = 'data.txt'                         # file name for saved results\n",
    "\n",
    "# Setup\n",
    "task = Task(init_pose, init_velocities, init_angle_velocities, runtime)\n",
    "agent = Basic_Agent(task)\n",
    "done = False\n",
    "labels = ['time', 'x', 'y', 'z', 'phi', 'theta', 'psi', 'x_velocity',\n",
    "          'y_velocity', 'z_velocity', 'phi_velocity', 'theta_velocity',\n",
    "          'psi_velocity', 'rotor_speed1', 'rotor_speed2', 'rotor_speed3', 'rotor_speed4']\n",
    "results = {x : [] for x in labels}\n",
    "\n",
    "# Run the simulation, and save the results.\n",
    "with open(file_output, 'w') as csvfile:\n",
    "    writer = csv.writer(csvfile)\n",
    "    writer.writerow(labels)\n",
    "    while True:\n",
    "        rotor_speeds = agent.act()\n",
    "        _, _, done = task.step(rotor_speeds)\n",
    "        to_write = [task.sim.time] + list(task.sim.pose) + list(task.sim.v) + list(task.sim.angular_v) + list(rotor_speeds)\n",
    "        for ii in range(len(labels)):\n",
    "            results[labels[ii]].append(to_write[ii])\n",
    "        writer.writerow(to_write)\n",
    "        if done:\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code cell below to visualize how the position of the quadcopter evolved during the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09e42f1668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(results['time'], results['x'], label='x')\n",
    "plt.plot(results['time'], results['y'], label='y')\n",
    "plt.plot(results['time'], results['z'], label='z')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next code cell visualizes the velocity of the quadcopter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09d12e4080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(results['time'], results['x_velocity'], label='x_hat')\n",
    "plt.plot(results['time'], results['y_velocity'], label='y_hat')\n",
    "plt.plot(results['time'], results['z_velocity'], label='z_hat')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you can plot the Euler angles (the rotation of the quadcopter over the $x$-, $y$-, and $z$-axes),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09d133a208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(results['time'], results['phi'], label='phi')\n",
    "plt.plot(results['time'], results['theta'], label='theta')\n",
    "plt.plot(results['time'], results['psi'], label='psi')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "before plotting the velocities (in radians per second) corresponding to each of the Euler angles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09d12077f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(results['time'], results['phi_velocity'], label='phi_velocity')\n",
    "plt.plot(results['time'], results['theta_velocity'], label='theta_velocity')\n",
    "plt.plot(results['time'], results['psi_velocity'], label='psi_velocity')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, you can use the code cell below to print the agent's choice of actions.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09d1241470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(results['time'], results['rotor_speed1'], label='Rotor 1 revolutions / second')\n",
    "plt.plot(results['time'], results['rotor_speed2'], label='Rotor 2 revolutions / second')\n",
    "plt.plot(results['time'], results['rotor_speed3'], label='Rotor 3 revolutions / second')\n",
    "plt.plot(results['time'], results['rotor_speed4'], label='Rotor 4 revolutions / second')\n",
    "plt.legend()\n",
    "_ = plt.ylim()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When specifying a task, you will derive the environment state from the simulator.  Run the code cell below to print the values of the following variables at the end of the simulation:\n",
    "- `task.sim.pose` (the position of the quadcopter in ($x,y,z$) dimensions and the Euler angles),\n",
    "- `task.sim.v` (the velocity of the quadcopter in ($x,y,z$) dimensions), and\n",
    "- `task.sim.angular_v` (radians/second for each of the three Euler angles)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  6.78740897  19.50694032  26.99888463   0.55295553   0.10702695   0.        ]\n",
      "[  2.28589199  10.99173461   3.56264661]\n",
      "[ 0.24200505  0.18499246  0.        ]\n"
     ]
    }
   ],
   "source": [
    "# the pose, velocity, and angular velocity of the quadcopter at the end of the episode\n",
    "print(task.sim.pose)\n",
    "print(task.sim.v)\n",
    "print(task.sim.angular_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the sample task in `task.py`, we use the 6-dimensional pose of the quadcopter to construct the state of the environment at each timestep.  However, when amending the task for your purposes, you are welcome to expand the size of the state vector by including the velocity information.  You can use any combination of the pose, velocity, and angular velocity - feel free to tinker here, and construct the state to suit your task.\n",
    "\n",
    "## The Task\n",
    "\n",
    "A sample task has been provided for you in `task.py`.  Open this file in a new window now. \n",
    "\n",
    "The `__init__()` method is used to initialize several variables that are needed to specify the task.  \n",
    "- The simulator is initialized as an instance of the `PhysicsSim` class (from `physics_sim.py`).  \n",
    "- Inspired by the methodology in the original DDPG paper, we make use of action repeats.  For each timestep of the agent, we step the simulation `action_repeats` timesteps.  If you are not familiar with action repeats, please read the **Results** section in [the DDPG paper](https://arxiv.org/abs/1509.02971).\n",
    "- We set the number of elements in the state vector.  For the sample task, we only work with the 6-dimensional pose information.  To set the size of the state (`state_size`), we must take action repeats into account.  \n",
    "- The environment will always have a 4-dimensional action space, with one entry for each rotor (`action_size=4`). You can set the minimum (`action_low`) and maximum (`action_high`) values of each entry here.\n",
    "- The sample task in this provided file is for the agent to reach a target position.  We specify that target position as a variable.\n",
    "\n",
    "The `reset()` method resets the simulator.  The agent should call this method every time the episode ends.  You can see an example of this in the code cell below.\n",
    "\n",
    "The `step()` method is perhaps the most important.  It accepts the agent's choice of action `rotor_speeds`, which is used to prepare the next state to pass on to the agent.  Then, the reward is computed from `get_reward()`.  The episode is considered done if the time limit has been exceeded, or the quadcopter has travelled outside of the bounds of the simulation.\n",
    "\n",
    "In the next section, you will learn how to test the performance of an agent on this task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Agent\n",
    "\n",
    "The sample agent given in `agents/policy_search.py` uses a very simplistic linear policy to directly compute the action vector as a dot product of the state vector and a matrix of weights. Then, it randomly perturbs the parameters by adding some Gaussian noise, to produce a different policy. Based on the average reward obtained in each episode (`score`), it keeps track of the best set of parameters found so far, how the score is changing, and accordingly tweaks a scaling factor to widen or tighten the noise.\n",
    "\n",
    "Run the code cell below to see how the agent performs on the sample task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode = 1000, score =  -0.429 (best =  -0.045), noise_scale = 3.2625"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "from agents.policy_search import PolicySearch_Agent\n",
    "from task import Task\n",
    "\n",
    "num_episodes = 1000\n",
    "target_pos = np.array([0., 0., 10.])\n",
    "task = Task(target_pos=target_pos)\n",
    "agent = PolicySearch_Agent(task) \n",
    "\n",
    "for i_episode in range(1, num_episodes+1):\n",
    "    state = agent.reset_episode() # start a new episode\n",
    "    while True:\n",
    "        action = agent.act(state) \n",
    "        next_state, reward, done = task.step(action)\n",
    "        agent.step(reward, done)\n",
    "        state = next_state\n",
    "        if done:\n",
    "            print(\"\\rEpisode = {:4d}, score = {:7.3f} (best = {:7.3f}), noise_scale = {}\".format(\n",
    "                i_episode, agent.score, agent.best_score, agent.noise_scale), end=\"\")  # [debug]\n",
    "            break\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This agent should perform very poorly on this task.  And that's where you come in!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Define the Task, Design the Agent, and Train Your Agent!\n",
    "\n",
    "Amend `task.py` to specify a task of your choosing.  If you're unsure what kind of task to specify, you may like to teach your quadcopter to takeoff, hover in place, land softly, or reach a target pose.  \n",
    "\n",
    "After specifying your task, use the sample agent in `agents/policy_search.py` as a template to define your own agent in `agents/agent.py`.  You can borrow whatever you need from the sample agent, including ideas on how you might modularize your code (using helper methods like `act()`, `learn()`, `reset_episode()`, etc.).\n",
    "\n",
    "Note that it is **highly unlikely** that the first agent and task that you specify will learn well.  You will likely have to tweak various hyperparameters and the reward function for your task until you arrive at reasonably good behavior.\n",
    "\n",
    "As you develop your agent, it's important to keep an eye on how it's performing. Use the code above as inspiration to build in a mechanism to log/save the total rewards obtained in each episode to file.  If the episode rewards are gradually increasing, this is an indication that your agent is learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode =  496, total_reward = 231.028, total_average_reward =   2.750It has successfully learnt how to take off, consistently achieving that in the last 100 episodes!\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from agents.agent import DDPG\n",
    "from task import Task\n",
    "\n",
    "# objective: takeoff and go upper than z=20\n",
    "\n",
    "num_episodes = 1000\n",
    "target_pos = np.array([0., 0., 20.])\n",
    "task = Task(target_pos=target_pos)\n",
    "agent = DDPG(task)\n",
    "\n",
    "rewards_list = []\n",
    "total_rewards = []\n",
    "total_average_rewards = []\n",
    "\n",
    "learnt_count = 0\n",
    "\n",
    "for i_episode in range(1, num_episodes+1):\n",
    "    if (learnt_count >= 100):\n",
    "        print(\"It has successfully learnt how to take off, consistently achieving that in the last 100 episodes!\")\n",
    "        break\n",
    "\n",
    "    state = agent.reset_episode() # start a new episode\n",
    "    rewards = []\n",
    "    \n",
    "    while True:\n",
    "        action = agent.act(state)\n",
    "        next_state, reward, done = task.step(action)\n",
    "        agent.step(action, reward, next_state, done)\n",
    "        rewards.append(reward)\n",
    "        state = next_state\n",
    "        if done:\n",
    "            total_reward = np.sum(rewards)\n",
    "            total_average_reward = np.mean(rewards)\n",
    "            total_rewards.append(total_reward)\n",
    "            total_average_rewards.append(total_average_reward)\n",
    "            rewards_list.append(rewards)\n",
    "            \n",
    "            if (total_average_reward > 2.5):\n",
    "                learnt_count += 1\n",
    "            else:\n",
    "                learnt_count = 0\n",
    "            \n",
    "            print(\"\\rEpisode = {:4d}, total_reward = {:7.3f}, total_average_reward = {:7.3f}\".format(\n",
    "                i_episode, total_reward, total_average_reward), end=\"\")\n",
    "            break\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe73624668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def map_fn(reward_function, x, y, target_pos):\n",
    "    R = pd.DataFrame(np.zeros([len(x), len(y)]), index=y, columns=x)\n",
    "    for xx in x:\n",
    "        for yy in y:\n",
    "            R[xx][yy] = reward_function([xx, yy], target_pos)\n",
    "\n",
    "    return R\n",
    "\n",
    "init_pos = np.array([0, 0])\n",
    "\n",
    "def reward_function(pose, target_pos):\n",
    "    return np.tanh(pose[1] / target_pos[1])\n",
    "\n",
    "x_range = np.round(np.arange(-10.0,10,0.1), 2)\n",
    "z_range = np.round(np.arange(40,0,-0.2), 2)\n",
    "\n",
    "target_pos = np.array([0, 20])\n",
    "\n",
    "R = map_fn(reward_function, x_range, z_range, target_pos)\n",
    "\n",
    "ax = sns.heatmap(R)\n",
    "ax.set_xlabel(\"Position X-axis\")\n",
    "ax.set_ylabel(\"Position Z-axis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Plot the Rewards\n",
    "\n",
    "Once you are satisfied with your performance, plot the episode rewards, either from a single run, or averaged over multiple runs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Last run'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe69a48d30>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe742d1860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## TODO: Plot the rewards.\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "display('Last run')\n",
    "plt.plot(rewards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Total rewards of episodes histogram'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe699ac7f0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe73524390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display('Total rewards of episodes histogram')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(total_rewards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Total average rewards of episodes histogram'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe69981d30>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe699d91d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display('Total average rewards of episodes histogram')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(total_average_rewards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "# Make sure to change from notebook to inline after your tests\n",
    "%matplotlib notebook\n",
    "import time\n",
    "\n",
    "class AnimatedPlot():\n",
    "    def __init__(self):\n",
    "        \"\"\"Initialize parameters\"\"\"\n",
    "        self.X, self.Y, self.Z = [], [], []\n",
    "\n",
    "        self.fig = plt.figure(figsize = (14,8))\n",
    "        self.ax = self.fig.add_subplot(111, projection='3d')\n",
    "\n",
    "    def plot(self, task, i_episode=None):\n",
    "        pose = task.sim.pose[:3]\n",
    "        self.X.append(pose[0])\n",
    "        self.Y.append(pose[1])\n",
    "        self.Z.append(pose[2])\n",
    "        self.ax.clear()\n",
    "        if i_episode:\n",
    "            plt.title(\"Episode {}\".format(i_episode))\n",
    "\n",
    "        if len(self.X)>1:\n",
    "            self.ax.scatter(self.X[:-1], self.Y[:-1], self.Z[:-1], c='k', alpha=0.3)\n",
    "        if task.sim.done and task.sim.runtime > task.sim.time:\n",
    "            # Colision\n",
    "            self.ax.scatter(pose[0], pose[1], pose[2], c='r', marker='*', linewidths=5)\n",
    "        else:\n",
    "            self.ax.scatter(pose[0], pose[1], pose[2], c='k', marker='s', linewidths=5)\n",
    "\n",
    "        self.fig.canvas.draw()\n",
    "        time.sleep(0.5)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\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) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    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://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://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://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 overriden (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"
    },
    {
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\" width=\"1008\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\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) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    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://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://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://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 overriden (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>"
      ]
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     "output_type": "display_data"
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\" width=\"1008\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myplot = AnimatedPlot()\n",
    "state = agent.reset_episode() # start a new episode\n",
    "i = 0\n",
    "while True:\n",
    "    action = agent.act(state) \n",
    "    next_state, reward, done = task.step(action)\n",
    "    agent.step(action, reward, next_state, done)\n",
    "    state = next_state\n",
    "    i +=1\n",
    "\n",
    "    # CALL THE METHOD plot(task)\n",
    "    myplot.plot(task)\n",
    "\n",
    "    if done:\n",
    "        break\n",
    "\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Reflections\n",
    "\n",
    "**Question 1**: Describe the task that you specified in `task.py`.  How did you design the reward function?\n",
    "\n",
    "**Answer**:\n",
    "\n",
    "The objective is to takeoff and go upper than `z=20`, so, it would be reasonable to give more reward if it goes higher, and give less reward if it goes lower, but the reward function should be based in the current state, in other words, the current position, so we don't know if it is going higher or lower, hence, it is reasonable to reward more if it is high, and less if it is low. So, it would learn that higher `z`s gives more reward, and hence it would learn to go up, takeoff, reach `z=20` and beyond.\n",
    "\n",
    "A simple example would be to reward the same as its z position, for example `self.sim.pose[2]`, but the reward function would not be scaled to a range of `(-1, 1)` (which would be more proper for a Neural Network to learn). If we apply a hyperbolic tangent with `np.tanh` it would scale to `(-1, 1)`, but it would become a high reward too soon, and not after reaching the target value. To make the hyperbolic tangent have a high reward after reaching the target `z`, we can divide the current `z` by the target's `z`, and then apply `np.tanh`, finally having `np.tanh(self.sim.pose[2] / self.target_pos[2])`, as we can see in the following reward chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe73857518>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe677bd470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.plot(R[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2**: Discuss your agent briefly, using the following questions as a guide:\n",
    "\n",
    "- What learning algorithm(s) did you try? What worked best for you?\n",
    "- What was your final choice of hyperparameters (such as $\\alpha$, $\\gamma$, $\\epsilon$, etc.)?\n",
    "- What neural network architecture did you use (if any)? Specify layers, sizes, activation functions, etc.\n",
    "\n",
    "**Answer**:\n",
    "\n",
    "I just tried the provided DDPG algorithm, and just tweaked some functions to make it work, and set the Adam optimizer learning rate to `0.001`, which looked like to work better after analysing some learning paths.\n",
    "\n",
    "I actually didn't need to change the hyperparameters, so the final choice was `gamma=0.99` of discount rate, `tau=0.01` for soft update of target parameters, and for noise exploration: `mu=0`, `theta=0.15`, and `sigma=0.2`.\n",
    "\n",
    "The actor beggins with a layer with the state size of 18, and is followed by three relu layers, and then ends up with a action layer with action size of 4, scaled to something between 0 and 1.\n",
    "\n",
    "The critic begins two paths of layers combined in a renu layer, with the first path being a state layer followed by two renu layers, and the second path being the actions layer followed by two renu layers, all of that ending with a q values layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 3**: Using the episode rewards plot, discuss how the agent learned over time.\n",
    "\n",
    "- Was it an easy task to learn or hard?\n",
    "- Was there a gradual learning curve, or an aha moment?\n",
    "- How good was the final performance of the agent? (e.g. mean rewards over the last 10 episodes)\n",
    "\n",
    "**Answer**:\n",
    "\n",
    "(Plots below: total average rewards episode history plot, then the same for the latest 100 episodes, and then the same for the latest 10 episodes)\n",
    "\n",
    "It was a hard task to learn: it had some relevant peaks of nicely rewarded episodes around runs \\#30, \\#80, and \\#150, and a considerable relevant peak around run \\#280, but it actually had learnt how to takeoff after run \\#400 and so on, it was like an 'aha' moment rather than a gradual learning curve.\n",
    "\n",
    "In the final episodes (latest 100, and latest 10), the performance was clearly great, and with a consistent average reward of `2.75`.\n",
    "\n",
    "Furthermore, in the final performance, the agent could go up high until `z` got around `140`, as we can see in the 3D plot above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe696085f8>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe67c60f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(total_average_rewards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe729b35f8>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe69647128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(total_average_rewards[-100:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe72a1a5f8>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe72a8dbe0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(total_average_rewards[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 4**: Briefly summarize your experience working on this project. You can use the following prompts for ideas.\n",
    "\n",
    "- What was the hardest part of the project? (e.g. getting started, plotting, specifying the task, etc.)\n",
    "- Did you find anything interesting in how the quadcopter or your agent behaved?\n",
    "\n",
    "**Answer**:\n",
    "\n",
    "The agent would be very difficult to code if it weren't the provided excerpts, otherwise I think it would be the hardest part to achieve by far. I'll continue learning more about that part to extend the knowledge on that area. \n",
    "\n",
    "The hardest part was to get a good reward function. I tried lots of other reward functions as well. At first, I tried to make it go up and hover at z=20, but then I saw that it actually had learnt how to go up, and maybe I would have to first make it learn how to go up, and in a second step, starting from z=20, and train it all again from scratch on a separate train set. So, it made me just focus on the takeoff. In my first attempt, I tried to reward by the distance that it is from the target (and lots of variances of that), but then when it goes beyond the target, it starts to be rewarded lesser, and I ended up thinking that it was not what I wanted, so I restarted the design. My second attempt was to reward with the current `z`, and after reaching the target and up, reward with the squared `z`. It's much simpler, and I could made it to work at sometime, but the reward function wasn't properly scaled. After that, I started to design the final reward function explained in Question 1, described step by step.\n",
    "\n",
    "I found it interesting to understand the breadth of the reinforcement learning problem. I saw that if running again, the behavior of the learning path is very different, the actions, the noise, all of it makes it a very wide learning problem, and would need millions of episodes to actually learn consistently on every run. Sometimes it was easier to learn than other, so I made the learner for to stop after having 100 of consecutive successful episodes. I found reinforcement learning much harder to personally learn and to put in practice, specially if compared with supervised or unsupervised learning.\n",
    "\n",
    "Finally, I'd like to cite a special thank to Fernando Wittmann which had nicely mentored our Udacity classes, providing awesome supporting material for completing this project with Webinars and a best practices repository (https://github.com/WittmannF/quadcopter-best-practices) that engaged and helped a lot to complete this exercise."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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
}