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": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import csv\n",
    "import numpy as np\n",
    "from task import Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\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": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f359a6d8f60>"
      ]
     },
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5ZblEBkVy29m3cU3Pa4ixxRhdshCinZFg95LiumKeXv00iwsWU99cD0DXiK6YlAl7sx17s52ooCgeHvowP+v+M2wBNoMrFkK0V60OdqVUCvAmkAC4gFla63+2drvtjUu72F6xnW3l2+gV3YuMqAxMyoTT5WTetnk8u+ZZXNrFxG4TGZY4jCEJQ4iyRhldthDCD3mixd4M3Ku1XqOUCgNWK6UWaK03e2DbPq2wppBl+5axsngl2cXZVDZUHv4sKiiKIYlD2Fezj9yyXEYkjeDhoQ+THJZsYMVCiI6g1cGutS4Cilpe1yiltgBJgF8G+86Knby7412W7VtGXnUeAIkhiYxJGcOQhCH0iu7FlvItrCxayYr9K9Bonhz9JBemXSijWIQQXuHRPnalVBowAFjpye36AqfLyexNs/n3un9jUiYGJwzm2l7XMjJpJKlhqceEdkZUBj/r9jO0do85l0AXQniTx4JdKRUKvAfcpbWuPs7nU4ApAKmpqZ7arVfkVeXx0LKH2FCygQldJvDwsIeJtkafdD0JdCGEETwS7EopC+5Qn6u1fv94y2itZwGzALKysrQn9tvWtNa8v+N9/r7q7wSaA3li1BNclH6RBLYQwqd5YlSMAl4Ftmitn2p9Sb6hrqmOx75/jM/3fM6wxGH8deRf5Z6eQoh2wRMt9hHADcBGpdS6lvf+T2v9uQe2bYjNZZu5/9v7Kagp4I7+d3Br5q2YTWajyxJCiFPiiVEx3wHtvm/CpV0s27eM/279L9/t+444WxyvnP8KgxMGG12aEEKclg595anWmm0V21hSsIRPdn9CfnU+sbZYbu9/O7/o+QsirZFGlyiEEKetQwb79ortzNs6jyWFSzhYfxCAAXEDuP3s25nQZQIWs8XgCoUQ4sx1qGDfULKBlze+zOKCxdgCbIxMGsmopFGMSh5FrC3W6PKEEMIjOkSwl9pLeWTZI3y37zsigiK4vf/tXNfrOiKCIowuTQghPM7vgz27OJv7ltxHXVMd9wy6h2t6XkOwJdjosoQQos34bbC7tItXN77Kc+ueIzUslZfPf5mMqAyjyxJCiDbnl8FeUl/Cw8seZvn+5VyUdhGPDn+UEEuI0WUJIYRX+F2wLylYwiPLHsHebOeRYY9wVY+rZAoAIUSH4jfB3uhsZEb2DOZtm0fPqJ48OfpJukZ2NbosIYTwOr8I9nJHOXctuou1B9dyQ+8buGvgXQSaA40uSwghDNHug31X5S5++81vKbWXMuPcGVyYdqHRJQkhhKHadbAv37+cexffS5A5iNcveJ3MTplGlyREx+ZshsZa96PJDk31R56bG9wPZ+NRj2ZwNYHLCWjQLtDHmdVbKUCBMh15mMwtzwHuh9nS8hzY8rC4nwOCjry22MASfORhbtcReELt9qi+zPuS33/7e7pGduW5cc+RGJpodElC+A+twVEJtSVQVwJ1B6GuFOrL3e/bK1ueK448HFXQ7DC68tNjCoAAG1isRz1bW74AbC3vHXrd8llA0LFfFubAI+sEBLm/MAKsLV8eLesc/oIJhMDQNv9CaZfB/tHOj/jD8j/Qv1N/njvvOcICw4wuSQjf11B7JKCPDuS60iPv1x5seRwAZ8PxtxMYCrYosEaCLRJie7T8HAFBYe7PA0Pcj6MD8nAgHh2KLa3sQ61vZeLHk8Xqllb8US167XQ/u5zgamn1O5vcr51NLX8NHHpucL9udkCTo+WviEN/Sdhb3m95PrRMs939XF9+7HLNDe7PXM1n/v9h8ruQMeHM1z8F7S7Y39r6Fn9Z+ReGJQ7jn2P/KVeRio6roRZqiqB6v7tV3Vjrfq+xFurL3OFcc8D9XHsQmuqOvx1lhpBYCIlzP8d0h7B4CI13vxfaCUI6QXAsBEe7w7ijc7lavkwaobnxyBfBD58PdUE5j+qGiu3R5uW1q2B/Y9MbzMyZyZjkMcwcM5Mgc5DRJQnhWS5XSyu6BGqL3aFdvc/9XHuwpVuk1P3c8KNbCx9hjXQHc2gcJA2E0AT369D4lpA+1OJueTaZvHeM/sBkAlPLXyA+GEPtKthjbDFclH4Rfx35VywmaTWIdqihBqoKoWofVBVA5d6WRz5UFrgDWzt/vF5wTEsox0Li2e7nsEQI7+x+hMRBUOiRrhC541eHpvTxzkCf7kaUuhD4J2AGXtFa//2nls/KytI5OTlntC+ttVxJKnyLywX1pS0t66Ij/dWHWta1B6Cm2P1orDl2XWWGiGSI6gKRqUda1CGd3C3s8CR3cFtsxhyb8ClKqdVa66yTLeeJm1mbgX8DE4BCIFsp9bHWenNrt32C/bXFZoU4VmN9y8nF8mNPNNor3e9V7z/S8q4pcve3/lBgGITEuFvW8X2g+3gIS3AHeUSyO7TDEv12yJ0wjif+RQ0BdmqtdwMopeYBlwJtEuxCnBZnszuI60rdJxSPCemjg7vSPQLi0M8/NWzPHOgO5IhkSB3W0h3S0rIOT3S3uoNj3UPdhDCAJ4I9CSg46udCYKgHtvsj379yN+mFnxz1zrHdSArtvobhB+8e+lkfbu0rUAoXJrQyQcuzNgXgUgFoUwDaZEGbgyAgENUyNtUUaMMcGIw5KBiLLZRAWyhB1hAstnD30K6glmFehy5+OHrIlzgzzmb3ScKGmqOea91dGodGgNgr3WOoHVXHBnZ9S0hzgu5GcyDYot0nEG2REJUGSQOOvBd86LOolvci3ScaA0NaLpgRwjd5ItiP9y/8R79JSqkpwBSA1NTUM9pRQKcMCmqP7V7SR+1eo0ErWka8cuj8gdaHXmt0yzhYrQ+NiXU/lMuJanZi0s2YdBMmVyMBupZAmgmiiSAasapGrDRio4EA5Trlup2mQJoDI9DWCEy2SAKCIzBZI8Aa7j7RdeiEV1AoBIW73w86zuftdeSCywUNh4K3JYTry9yPulJ3/7SjChzV7ueG6iNB3Vh7CjtQR/6b2SLdgRyR5A7jkNiWUSAxLY/oIyNBJKCFn/JEsBcCKUf9nAzs/+FCWutZwCxwnzw9kx0NvvR24PYzWfWMaK1xNLmoa2ymrqGZA45mahzN1DiaqLPX46ivpaG+loa6Ghrt1TTVV9PsqEE31uFqtKOa6jA31RFGHRGNdUTU1RFOHWGqnEiTnXBVTzAOgvQJLgT5IUsIBB76SyD0yJfBodcWm/tzS8hRV9IdumIu0H1hSEAgmCzuUROmAPfJu6OzTeMelXHowo9Dl34fGoPb3ODupnA2ui/aaKxruYT86Oc6d2v6UDg3VHPCVjMcubjFGuH+Yovu6g7eQ19+h94PCjvyOHQRzKEvQxkFIsRhngj2bCBDKZUO7AOuBa7zwHYNp5TCFmjGFmgmNvTMBqs6XZqy2gYOVDdwoNpBQW0DB6sbKKl1UFLTQFltIxW1dux11WhHDWGqnjDqCVP1hOIgPqiRRGsTcUHNxAQ2EWFuJNzUQKhyEIKDwPpyVOVe98m+pjr384muGGwLynzsl8uh7qeIZPcJQ1vkkdC2HvU6OMbdmrZFy8lDITys1b9RWutmpdQdwJe4hzu+prXe1OrK/ITZpIgLtxIXbiWTn755tqPJSVGVg6IqO0WV7uf9VQ6WVdrd75c4qLIfO/oiMMBESpSN1OhgusSEkBIdTJfIQLpEmkkJVVhpPOrKN4f7KjlXs/txqGX+Q8rc0qI3HzUXRtCRFv/huS9a5s2Q7gwhfIpHmkpa68+Bzz2xrY7MajGTHhtCeuyJb+Nnb3RSXO2gsKKe/LJ69pbXk19WR0G5ney8Cmobjp3DIjHCSlpMCOmdQugaG0W3TqGkx4aQHGUjwNxO++yFED9J/gZuZ2yBR8J/1A/uza21prK+ifyWsM8vqyevrI49pXV8tqHomNZ+gEmRHGWjS0wIqdHBpETbSIkKJjUmmLSYEEKC5J+GEO2V/Pb6EaUUUSGBRIUE0j8l8pjPtNaU1zWyu9Qd9PlldeSVuoN/7d4Kqh3HtvTjw4NIjw0hIy6MsxLD6ZUYRq+EMIID5Z+MEL5Ofks7CKUUMaFBxIQGMTgt+kefV9mbKCivP6aVv7uklg/X7uM/K/IPL5cUaaNbXCjdOoXQMz6MAalRZMSFYjJJP7sQvkKCXQAQYbMQkRRB36RjT/BqrSmssLOlqJqtxTXsKqllV0kt2XvKsTe5T7yGBQXQPzWSs5Mj6ZsUTp/OESRH2WT6ByEMIsEufpJSipToYFKigzm/T8Lh910uTX55PWvyK1hbUMGa/EpeXLKLZpd7vHpUsIUh6dEM7xbL8G4xdI8LlaAXwksk2MUZMZnU4ZO4VwxKBtzDNbcV15C7v4r1BZV8v7uMLzcdAKBTWBAjusUwonssI7rH0jlSplkQoq14ZNre09WaaXtF+1JQXs/yXaUs31XGsp2llNY2ApCZFMHlA5P42dmdiTnDi7+E6GhOddpeCXbhNVprth2o4dvtJXy8fj+5+6oJMCnO7dGJ8/vEM7ZnHHHhMiOiECciwS583rbiGt5fW8jH6/ZTVOWeJrdvUjjjesUzsV8iPeLlJuVCHE2CXbQbWmu2FtewcOtBFm09yJq9Fbg09IgPZWK/zlycmUj3uFCjyxTCcBLsot06WOPgi43FfLphP9l5FQB0jwvlwj4JXNAngb5J4TLCRnRIEuzCLxRXOfhqczHzc4tZuaccp0sTFxbEuT06MaZnHKN6xBJulRubi45Bgl34nfK6Rr7ZcoDF20tYur2EakczgWYTY3p24tL+SZx3VhxWi8zLLvyXBLvwa81OF2sLKpmfW8wn6/dzsKaBkEAzl/RL5JrBqQxMjZTuGuF3JNhFh+F0aVbuLuPDdfv4dEMR9Y1OesSHcu3gVK7MSpauGuE3JNhFh1Tb0Mwn6/czL7uA9QWVhAYFcO3gFH41Mp0kudpVtHMS7KLD21hYxSvf7ebTDUUAXNgngasHpzCyeyxmmY1StEMS7EK02FdpZ/ayPbyzupDK+iYSwq1cPjCJq7JSfvJuVUL4Gq8Eu1JqBjAJaAR2Ab/SWleebD0JdmGEhmYn32w5yLurC1m87SAuDUPSo7kmK4WLMxOxBcqIGuHbvBXs5wMLW25o/QSA1vqBk60nwS6MdqDawXtrCnk7u4C8snrCggKY1L8zV2elcHZyhIyoET7J610xSqnLgCu11pNPtqwEu/AVWmtW7SnnrZwCPt9YhKPJRc/4MK7KSubnA5KIlZknhQ8xItg/Ad7SWs85wedTgCkAqampg/Lz84+3mBCGqXY08en6It7KcY+oCTApxvSM48pByYzrFUdggMnoEkUH57FgV0p9DSQc56OHtNYftSzzEJAFXK5P4ZtCWuzC1+04UMO7awp5f80+SmoaCLcGcH6fBCb2S2RE91gsZgl54X1ea7ErpW4CpgLnaa3rT2UdCXbRXjQ7XSzdWcon6/ezYNMBahqaiQq2cOuortw8Il1OuAqv8tbJ0wuBp4BztdYlp7qeBLtojxqanXy7vZT/rdrLwq0HiQsLYvr4DK7OSpEWvPAKbwX7TiAIKGt5a4XWeurJ1pNgF+1ddl45T3yxlZz8CtJigrl9bHcuG5AkAS/alFygJEQb01qzcOtBnv56O7n7qkmOsnHbmG5cOSiZoADpohGeJ8EuhJdorVm07SDPfrOTdQWVRAVb+PmAJK4ZnEKvhHCjyxN+RIJdCC/TWrN8Vxn/XbmXrzYX0+TUnJ0cwc0j07kkM5EA6aYRrSTBLoSByusa+XDtPuaszGd3SR2p0cFMGd2VKwcly81AxBmTYBfCB7hcmgVbDvD84l2sL6gkLiyIaedlcM1gGUkjTp8EuxA+RGvN97vKeGrBdnLyK0iPDeHe83twcd9ETDKFsDhFpxrs0mQQwguUUgzvHss7U8/hlRuzCDSbuOO/a7n8heWsLzjphKhCnBYJdiG8SCnF+N7xfD59FDOu7Me+SjuX/nsZ972znpKaBqPLE34iwOgChOiIzCbFVVkpXNg3gecW7uS1ZXv4IreYq7KSuX5YF7p1CjW6RNGOSR+7ED5gd0ktT3+9g/m5RTQ5NcO7xXDT8DTO7x0vc8OLw+TkqRDtUElNA2/nFPDflXvZV2mnf0ok/3fxWQxJjza6NOEDJNiFaMecLs17qwt5asF2iqsdjD8rjrs4kmkfAAAUZklEQVQn9KBP5wijSxMGkmAXwg/YG528tmwPLy7eRU1DM0PSovnlCHcXjVzJ2vFIsAvhR6rsTbyTU8Ab3+dRUG6nc4SVaedlcFVWCmYZB99hSLAL4YecLveMki8s3smavZX0jA/j/y45i3N7dDK6NOEFEuxC+DGtNV/kFvP3L7ayt7yeIWnRXNA3gTE9O9E1NkRG0vgpCXYhOoCGZif/+T6fedkF7DxYC0BqdDBXDkrmVyPSCLNaDK5QeJIEuxAdTEF5PYu3HeSrzQdYuqOUqGALt43pxg3D0uTerH7Cq8GulPodMAPopLUuPdnyEuxCtK31BZX8Y8F2vt1eQlxYEHeel8G1MqNku+e1ScCUUinABGBva7clhPCMs1MiefPmIbw1ZRhdYoJ55MNcxj+1hI/X78fl8v5f6cK7PPH1/TRwPyD/WoTwMUO7xvD2b87htV9mYbOYmfa/tVz87FLezi7A0eQ0ujzRRloV7EqpnwH7tNbrPVSPEMLDlFKM6xXP59NG8fQ1Z6M13P/eBs55/BuemL+VA9UOo0sUHnbSPnal1NdAwnE+egj4P+B8rXWVUioPyDpRH7tSagowBSA1NXVQfn5+a+oWQpwhrTUrdpcze/keFmw+QFCAmd+c25Upo7sSHCgTvvqyNj95qpTKBL4B6lveSgb2A0O01sU/ta6cPBXCN+SX1fHk/G18trGI+PAg7rugF5cPSJK7Ovkorw93PFmL/WgS7EL4lpy8cv782RbWF1SS1SWKxy/PJCM+zOiyxA/IrfGEEKcsKy2aD24bzpNX9mNnSS0XP7uUp77aJidY2ymPBbvWOu1UWutCCN9kMimuzkrhm3vOZWK/zjy7cCcX/XMpS3eUGF2aOE3SYhdCHCMmNIinr+nPmzcPQWvNDa+u4o7/rpHRM+2IBLsQ4rhG9+jE/LtGc/f4Hny1+QDjZi7mhcW7sDdK94yvk2AXQpyQ1WJm+vgMFtw9mmFdY3hi/lbOnbGI/6zIp8npMro8cQIS7EKIk+oSE8KrvxzMO1PPOTxFwXn/WMLCrQeMLk0chwS7EOKUDU6L5u3fnMPrvxxMUICJm2fncOf/1lJS02B0aeIoEuxCiNOilGJsrzg+mzaKu8f34MvcYsY/tYS3svfKBGM+QoJdCHFGAgNMTB+fwefTR9IjPpQH3tvIxH99x/KdMurZaBLsQohW6R4XxltTzuGf1/anyt7Eda+s5JbZ2ewqqTW6tA5Lgl0I0Womk+LS/kl8c++5PHBhL1btKeeiZ+TqVaNIsAshPMZqMXPbmG4s/N0YLs5M4NmFO7ngmW/5drtcvepNEuxCCI/rFBbEM9cOYO6tQzEpxY2vreKueWspr2s0urQOQYJdCNFmRnSP5Yvpo5h2XgafbSxi/FNL+GjdPjw1q6w4Pgl2IUSbslrM3DOhB5/eOYqU6GCmz1vHr9/MkdZ7G5JgF0J4Rc+EMN6/bTgPX3IW324v5ZJnl5KTV250WX5Jgl0I4TVmk+LWUV15//bhWMwmrpm1glnf7pKuGQ+TYBdCeF3fpAg+nTaS83vH87fPt3L1S9+zao+03j1Fgl0IYYhwq4XnJw/k8cszySur5+qXvufG11axobDS6NLavVbf81QpdSdwB9AMfKa1vv9k6xzvnqdNTU0UFhbicHSsyfytVivJyclYLBajSxHCMPZGJ29+n8cLS3ZRWd/Er0elc98FvQgMkLbn0U71nqcBrdzJWOBSoJ/WukEpFXem2yosLCQsLIy0tDSU6hh3SNdaU1ZWRmFhIenp6UaXI4RhbIFmfnNuN64bmsqT87fx8tI9ZOdV8K9fDCAlOtjo8tqd1n4d3gb8XWvdAKC1PnimG3I4HMTExHSYUAf3LHkxMTEd7q8UIU4kzGrhzz/vywuTB7Kr5aba83OLjC6r3WltsPcARimlViqlliilBrdmYx0p1A/piMcsxMlclJnIZ3eOomtsCFPnrOHRj3JpaJY5Z07VSbtilFJfAwnH+eihlvWjgGHAYOBtpVRXfZyOe6XUFGAKQGpqamtqFkJ0AKkxwbwzdTh//2Irry3bw5q9lTx33QC6xIQYXZrPO2mLXWs9Xmvd9ziPj4BC4H3ttgpwAbEn2M4srXWW1jqrU6dOnj0KL1q8eDETJ048rXVmz57N/v3726giIfxXYICJP0zqzawbBpFfVsfEZ7/jnZwCuaHHSbS2K+ZDYByAUqoHEAjILPs/IMEuROuc3yeBz6ePokdCGPe9u4ErX1zOxsIqo8vyWa0N9teArkqpXGAecNPxumHag+zsbPr164fD4aCuro4+ffqQm5t73GVra2u58sor6dWrF5MnTz581dxjjz3G4MGD6du3L1OmTEFrzbvvvktOTg6TJ0+mf//+2O12bx6WEH4jOSqYd35zDjOu7Mfe8np+9u/v+P17G6iyNxldms9p9Tj2M3G8cexbtmzhrLPOAuBPn2xi8/5qj+6zd+dwHp3U5yeXefjhh3E4HNjtdpKTk3nwwQd/tMzixYu59NJL2bRpE507d2bEiBHMmDGDkSNHUl5eTnR0NAA33HADV199NZMmTWLMmDHMnDmTrKzjDz89+tiFECdX7WjiX9/s4PVleSREWPnXLwYwIDXK6LLa3KmOY5fR/0f5wx/+wIIFC8jJyeH++098ndWQIUNITk7GZDLRv39/8vLyAFi0aBFDhw4lMzOThQsXsmnTJi9VLkTHEm618NAlvXl76jloDVe9+D0vLdklfe8tWnWBUls5Wcu6rZSXl1NbW0tTUxMOh4OQkOOffQ8KCjr82mw209zcjMPh4PbbbycnJ4eUlBT++Mc/yvh0IdrYwNQoPp82igfe28DjX2xl+a4yZlzVj7gwq9GlGUpa7EeZMmUKf/7zn5k8eTIPPPDAaa17KMRjY2Opra3l3XffPfxZWFgYNTU1Hq1VCOEWEWzhhesH8udL+7BidxkXPP0t83OLjS7LUD7ZYjfCm2++SUBAANdddx1Op5Phw4ezcOFCxo0bd0rrR0ZG8utf/5rMzEzS0tIYPPjItVq//OUvmTp1Kjabje+//x6bzdZWhyFEh6SU4oZz0jinWyx3v7WOqXNWc9WgZP4wqTdh1o43D5NPnjztaDrysQvhaY3NLp79ZgfPL95JQriVv16WydheZzyNlU+Rk6dCiA4pMMDE7y7oybu3DSckKIBfzc5m+ry1lNU2GF2a10hXzAls3LiRG2644Zj3goKCWLlypUEVCSFOx8DUKD6dNpLnF+3i+cU7+XZ7CS9eP4ihXWOMLq3NSbCfQGZmJuvWrTO6DCFEKwQFmLl7Qg8u6ZfIbXNWc9Prq5h1Qxaje7TfaU1OhXTFCCH8Xo/4MN76zTmkx4Zy6xs5fLXJv0fNSLALITqE2NAg5v16GL07h3Pb3DV8tG6f0SW1GQl2IUSHERFsYc6tQ8nqEsX0eet4YfEu2un0Vj9Jgl0I0aGEBgXwxs1DmHR2Z56Yv5UH3ttAY7PL6LI8SoL9NMl87EK0f1aLmWev7c+08zJ4O6eQm15bRWV9o9FleYwEuxdIsAvhe5RS3DOhB09fczar8yu4/Pnl5JXWGV2WR/jmcMcvfg/FGz27zYRMuOjvJ/z4kUceITY2lunTpwPw0EMPER8fz7Rp03607KH52HNzcxk0aBBz5sxBKcVjjz3GJ598gt1uZ/jw4bz00ku89957h+djlykFhPA9lw1IJjkqmClv5nDZ88uYdWMWg9OijS6rVaTF3uKWW27hjTfeAMDlcjFv3jwmT5583GXXrl3LM888w+bNm9m9ezfLli0D4I477iA7O5vc3FzsdjuffvopV155JVlZWcydO5d169ZJqAvhgwanRfPB7SOIDA5k8ssr2/2IGd9ssf9Ey7qtpKWlERMTw9q1azlw4AADBgwgJub4V6gdmo8dODwf+8iRI1m0aBFPPvkk9fX1lJeX06dPHyZNmuTNwxBCnKG02BDev204v5mzmunz1lFW28jNI9ONLuuM+GawG+TWW29l9uzZFBcXc/PNN59wOZmPXQj/FBUSyH9uGcK0/63lsU8349KaW0d1Nbqs09aqrhilVH+l1Aql1DqlVI5SaoinCjPCZZddxvz588nOzuaCCy44rXVlPnYh/ENQgJnnrhvIRX0T+MtnW3hl6W6jSzptrW2xPwn8SWv9hVLq4pafx7S6KoMEBgYyduxYIiMjMZvNp7WuzMcuhP+wmE08+4sB3DVvHX/5bAtOl+Y353YzuqxT1qr52JVSXwKvaa3fUkr9Apiktb7uZOv56nzsLpeLgQMH8s4775CRkeG1/frCsQshfqzZ6eKut9bx6YYipp2Xwd3jM1BKGVbPqc7H3toW+13Al0qpmbi7dYb/REFTgCkAqamprdyt523evJmJEydy2WWXeTXUhRC+K8Bs4plr+mOzmHn2mx1U25v4w8TemEzGhfupOGmwK6W+BhKO89FDwHnA3Vrr95RSVwOvAuOPtx2t9SxgFrhb7GdccRvp3bs3u3cf6UuT+diFEOAO9yeu6EeY1cJry/ZQ42jmiSsyCTD77mjxkwa71vq4QQ2glHoTmN7y4zvAKx6qy3AyH7sQ4hCTSfHIxLOIsFl4+uvtHKxx8I+rziYu3Gp0acfV2q+c/cC5La/HATtauT0hhPBJSimmj8/giSsyyc4r58J/LmXB5gNGl3VcrQ32XwP/UEqtB/5GSx+6EEL4q2sGp/LpnSNJCLfy6zdzeOiDjTQ0O40u6xitOnmqtf4OGOShWoQQol3oHhfGB78dzswvt/Hy0j3sr7TzwvWDsFpOb5h0W/Hd3n8hhPBhQQFmHrqkN3+7LJNF20qYOmc1jibfaLlLsJ+m0NDQ01p+8eLFLF++vI2qEUIY7bqhqfztskwW+1C4S7C3MQl2IfzfdUNTefzyI+FudJ+7T04C9sSqJ9havtWj2+wV3YsHhjxwws9ffPFFXnzxRQCqqqpIS0tj0aJFx132oYce4tNPP8Vms/HRRx8RHx/PJ598wl/+8hcaGxuJiYlh7ty52O12XnzxRcxmM3PmzOFf//oXo0aN8uhxCSF8wy+GuC+8fPD9jdz537X8e/JALAaNdZcWe4upU6eybt06srOzSU5O5p577jnucnV1dQwbNoz169czevRoXn75ZQBGjhzJihUrWLt2Lddeey1PPvkkaWlpTJ06lbvvvpt169ZJqAvh534xJJVHJ/Xmq80H+N0763G6jLkW0ydb7D/Vsm5r06dPZ9y4cSecRz0wMPDwPU8HDRrEggULACgsLOSaa66hqKiIxsZG0tPb5zzOQojW+dWIdOobncz4chs2i5nHL8/0+vwy0mI/yuzZs8nPz+fRRx894TIWi+Xw/6RDc7ED3Hnnndxxxx1s3LiRl156SeZiF6ID++3Y7twxtjvzsgv4/XsbaXK6vLp/n2yxG2H16tXMnDmTpUuXYjKd/vddVVUVSUlJAIdvsQfuudirq6s9VqcQon249/weADy3aCd7y+t54fqBRAYHemXf0mJv8dxzz1FeXs7YsWPp378/t95662mt/8c//pGrrrqKUaNGERsbe/j9SZMm8cEHH9C/f3+WLl3q6bKFED5KKcXvLujJP646m9X5Ffz838vYebDWO/tuzXzsZ8pX52M3Skc+diE6gpy8cn7zn9U0Ol28etNghqRHn9F2TnU+dmmxCyFEG8tKi+bD346gf0okSVFtfwc16WM/gaFDh9LQ0HDMe//5z3/IzMw0qCIhRHuWEh3Mf24Z6pV9SbCfgNxQQwjRXvlUV4wR/f1G64jHLIRoWz4T7FarlbKysg4VdFprysrKsFp98y4sQoj2yWe6YpKTkyksLKSkpMToUrzKarWSnJxsdBlCCD/iM8FusVjkMnwhhPAAn+mKEUII4RkS7EII4Wck2IUQws8YMqWAUqoEyD+NVWKB0jYqx1fJMXcMcswdg6eOuYvWutPJFjIk2E+XUirnVOZH8CdyzB2DHHPH4O1jlq4YIYTwMxLsQgjhZ9pLsM8yugADyDF3DHLMHYNXj7ld9LELIYQ4de2lxS6EEOIU+XSwK6UuVEptU0rtVEr93uh6vEEp9ZpS6qBSKtfoWrxBKZWilFqklNqilNqklJpudE1tTSllVUqtUkqtbznmPxldk7copcxKqbVKqU+NrsVblFJ5SqmNSql1Sqmck6/hgX36aleMUsoMbAcmAIVANvALrfVmQwtrY0qp0UAt8KbWuq/R9bQ1pVQikKi1XqOUCgNWAz/35//PSikFhGita5VSFuA7YLrWeoXBpbU5pdQ9QBYQrrWeaHQ93qCUygOytNZeG7vvyy32IcBOrfVurXUjMA+41OCa2pzW+lug3Og6vEVrXaS1XtPyugbYAiQZW1Xb0m6H7mpsaXn4ZgvLg5RSycAlwCtG1+LvfDnYk4CCo34uxM9/4Ts6pVQaMADw+9tXtXRJrAMOAgu01n5/zMAzwP2Ay+hCvEwDXymlViulpnhjh74c7Oo47/l9q6ajUkqFAu8Bd2mtq42up61prZ1a6/5AMjBEKeXX3W5KqYnAQa31aqNrMcAIrfVA4CLgty3drW3Kl4O9EEg56udkYL9BtYg21NLP/B4wV2v9vtH1eJPWuhJYDFxocCltbQTws5b+5nnAOKXUHGNL8g6t9f6W54PAB7i7mduULwd7NpChlEpXSgUC1wIfG1yT8LCWE4mvAlu01k8ZXY83KKU6KaUiW17bgPHAVmOralta6we11sla6zTcv8sLtdbXG1xWm1NKhbQMCkApFQKcD7T5iDefDXatdTNwB/Al7hNqb2utNxlbVdtTSv0P+B7oqZQqVErdYnRNbWwEcAPuFty6lsfFRhfVxhKBRUqpDbgbMAu01h1m+F8HEw98p5RaD6wCPtNaz2/rnfrscEchhBBnxmdb7EIIIc6MBLsQQvgZCXYhhPAzEuxCCOFnJNiFEMLPSLALIYSfkWAXQgg/I8EuhBB+5v8Bk8UEiRPgKPYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3591a41128>"
      ]
     },
     "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": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rnnvuOezcuRNvf/vbcf/99+Od73wnnnrqqdra0wlzpmrZmX4R83G3hRY3sUgQx/ndtQUuuOAC3Hvvvdi4cSMOHz6M6667ru4mtcWcxZ07JwuPcds6AKzhMaqEGCSMNC9kbQHHcXDnnXfOWrd///78+djYGHbs2AEA2Lp1K7Zu3bp4jWuBOcXLnZMFLcTA2AFgDREpneOckF4TRjS4usFcz2X5eQvHcfMA6IjrlMtxTkivsVRycHR0FHv27Km7GaUwp3jNMyeLYOkAsIrvOpw5SUzBylXdYU64y8YdMztgZzy3XMZFQnpNwBxDXWGu58pY3KqaRpWY2w1TeI7D7IDEFGHEiXPdYE7xyuQqycSIB0B76Jwk1ohipqroBnvCXWLmZCbuLDjanrIFmAnpNYGRylVHjhzJ85Ls2LEDl112WanP33PPPXj++ed70bS21N9zc2hUJe9sIWazAWlxt8d3yxVgJqTXWPFNNQt3FeoSbnMTcHy3uHMyZKXoQnCohFgjMJJj6MYbb8Svf/1rbNq0Cb7vY/ny5diyZQv27NmDiy66CN/4xjcgIti1axc+9alP4fjx41i7di3uuecePPbYYxgfH8eHPvQhDA8PY+fOnbj11lvx/e9/H5OTk3jHO96Br3zlKz2p8FN/z82hTP7oTIwYx90e1xE6J4kprKSquOWWW/Ca17wGu3fvxq233oqnnnoKt99+O55++mk8++yzeOyxxxAEAT72sY9h+/bt2LVrF66++mp85jOfwZYtWzA2Nob77rsPu3fvxvDwMK6//no88cQT2LNnDyYnJ/HQQw/1pN0GLe7i+aMzcbdwAFgmGSqhxU3s0DKO+59uBH7/Lwv7j155IfDHxcuMXXzxxVi3bh0AYNOmTdi/fz9Wr16NPXv24H3vex8AIIoinHXWWS0//8gjj+Bzn/scJiYmcPjwYWzYsAEf+MAHut+POZgT7jLOyXyohBZ3WxjHTawRxDFW+ObkJ0/jCjRSuaoqNmzYgJ07d7b97NTUFD760Y9ifHwc69evx0033dR1NfeT0bHnRGQIwKMABtPtt6vq3/SkNUCed6RIGlI6J4vhO5w5SWwRRi2GSkpYxgvFypUrcezYsbbbnHfeeTh48GCe9jUIAvziF7/Ahg0bZn0+E+m1a9fi+PHj2L59O7Zs2dKTdhe55E0DeI+qHhcRH8CPROSfVPXHvWiQWyKOu+GcpMXdjmSMm8JN7BDGaiKMd82aNdi8eTPe+MY3Ynh4GGeeeea8bQYGBrB9+3Z8/OMfx9GjRxGGIT75yU9iw4YN2Lp1K6699trcOfmRj3wEF154IUZHR/GWt7ylZ+3uKNyqqgCOpy/99NGz+24vr0peQLhTi5uB/O3xXMFEwKESYocwis3cKX/zm99suf6OO+7In2/atAmPPvrovG2uuOIKXHHFFfnrm2++GTfffPPCN3IOhS55IuKKyG4ALwJ4WFV/0qsGlXJOcuZkIXzXQUTnJDFEEsddv8XdrxTqOVWNVHUTgHUALhaRN87dRkS2ici4iIwfPHiwcoO8EkmmMoubB0B7kjhuWtzEDkEUM49+F5RSPFU9AmAHgEtavHeXqo6p6tjIyEjlBuUVWwoITZ6rhAdAWzyXY9zEFsxV0h0dhVtERkRkdfp8GMB7AezrWYMcgSPFSpc1cpXwAGiH53DKO7FFUuSbd8pVKRJVchaAe0XERSL031bV3kwHyhrlFktDGnDmZCE8l0MlxBZhbMc52Y8UiSr5GYA3LUJbcvyCuTXonCyG73DmJLFFEsdNg6sqJnuuaI3E3DnJA6AtLi1uYowwjk1kByzLnXfeia9//et1N8PelHegeG6NTNxpcbfH5wQcYozGzMn+MiiuvfbaupsAwKjFXXRMNtuGzsn2eK7DXCXEDKpqKo57//79OP/883HVVVdh48aN2LJlCyYmJnDjjTfiDW94AzZu3IhPf/rTAICbbroJt912W80tNmpxF62RmFmRFippWMZzBQGFmxghv1M2ZHA988wzuPvuu7F582ZcffXVuOOOO/Dggw9i3759EBEcOXKk7ibOwqZwu1Jopl+e1pVDJW1hIQViiTyMd855+9nHP4t9hxc20vj8M87HDRff0HG79evXY/PmzQCAK6+8Ep///OcxNDSEa665Bu9///tLlzTrNSZNVc8pZiE28nGb3A0zeI6DWIGYVjcxQH6nbOi8nVulxvd9PP7447jiiivw3e9+F5dcMm/OYa2YtLh91ykYDphFldDibkee/yVWDLCvSM008ujPPhaLWMa94rnnnsvTtt5///3YtGkTjh49iksvvRRve9vb8NrXvra2trXCziWvibLOSQ6VtCcvTsFYbmKAwGCOoQsuuAD33nsvNm7ciMOHD+Oaa67BZZddho0bN+Ld7343vvCFL9TdxFmYtLjdglO0G4UU7BwAFsnuSFh3klggMlhy0HEc3HnnnbPWPf744/O2u+mmmxapRe0xqXi+I8VylbDKeyEaibtocZP6CXnedo1J4U6y2RWxuBnHXYTslpSx3MQC1sJ4R0dHsWfPnrqbUQobPTeHMs5Jz5F5HmEym8w5yVhuYgGG8XaPSeF2nWJVyZPZV/zxO5HV9uNQCbHA3KGSpDri0mEh9tekcBedORlGaioW1Cq5xU3nJDFAc3K4oaEhHDp0aMmIt6ri0KFDGBoa6up7TEaV+G4x52QYx/NmX5H5ZBOUOMZNLBA0hfGuW7cOBw4cQDflDvuNoaEhrFu3rqvvMCncnusULl3GWZOd8XKLm0MlpH7CJuek7/s499xza25R/2FS9byi+bgjVtEoQh4OSIubGCBkNFjX2BXuAtZhROdkIfKZk7S4iQGYR797bAq36xQKXQtiDpUUwafFTQzRyDHEc7cqJnvOd4vWnIw5+6oADYubwk3qJ2COoa4xKdxFa04GkZ0qGpbJxhIDJpkiBmCt2O4x2XN+waiSKKZzsgh5Wlda3MQAEWdOdk1H4RaR9SLyiIjsFZGfi8gnet0or2CSqTBWeqYL4HHmJDFENlTCyXPVKRLHHQL4S1V9UkRWAtglIg+r6tM9a5SbzJxU1bZ5SIIo5o9fgOZCCoTUTe6cpMVdmY6qp6q/U9Un0+fHAOwFcHYvG5U5HDvpTBgxHLAIbh5VQoub1E9gMB93v1HKXBWRUQBvAvCTXjQmo+hMvyTJFC3uTmTpM5mrhFggiuxVwOk3CveciKwA8B0An1TVl1u8v01ExkVkvNu8A9nwR6db+zBmOGARPDoniSGY1rV7Cgm3iPhIRPs+VX2g1Taqepeqjqnq2MjISFeNaghNB4s7Ugp3ARpJpjhUQuqHzsnuKRJVIgDuBrBXVT/f+yYVz60RRLGZKhqWYc1JYonMIGNEWHWKqN5mAB8G8B4R2Z0+Lu1lo4rO9GOukmLkdzC0uIkBmKukezqGA6rqjwAsag83LMT2QhNEjOMuAp2TxBJhHMNlycGuMDnO4BWMOw5jxnEXIbsQspACsQB9U91jUvWKOtMYx12MPI6bMyeJAQIKd9eYFO6iNRLDWOmcLICIwHOEVd6JCaI4Zgx3l5jsvUZujQ7CHcUc4y6IVzBVLiG9JoiVjskuMSncWQHgTmlIA0aVFMZ3HOYqISZI8uiblJ6+wWTv+QWrkodMMlWYxOKmcJP6CRkN1jUmVa9IrpI4VsTKabNFcR2HcdzEBCGHSrrGpHAXSfwfMsNYKXxXGMdNTBDSOdk1JnvPzZNMndxCzMsf8QAohOcK47iJCRgO2D0mVS/PVdLGQswLjvIAKITvOB1nohKyGIRRzCHOLjEp3FlsdrsoiCjPd2ByF8zhOnROEhuEsTKqpEtM9l4R5yQzjJXDc+mcJDYIIzonu8WmcBcZKmGGsVL4rjCOm5ggKYBiUnr6BpO9lzkc2znT8oKjPAAK4XGohBghYI6hrjGper7TeeYkyx+Vw6NzkhghihlV0i0mhbtIIYUwjyoxuQvm8DhUQowQRIzj7haTvecWKKQQ5JWieeUuQuKcpHCT+uHMye4xKdzZj9p2jJvOyVL4DrMDEhskWT1NSk/fYLL38rSubeO46ZwsA+O4iRXCWHM/FqmGSdUrUnOSMyfL4btOxzS5hCwGrFzVPSaF23EEjhR0TtLJUQjmKiFWYJKp7jHbe52caUFM52QZPMfhUAkxAZNMdY9Z4e7kTItSEWIhhWJ4jjCOm5ggYq6SrunYeyLyNRF5UUT2LEaDMjpZ3FneDeYqKQbjuIkVgihmNFiXFLns3QPgkh63Yx6dLMTMOckDoBi+6zAckJggZK3Yruko3Kr6KIDDi9CWWXRyprGQQjk8hxY3qR9VRRQr47i7ZMF6T0S2ici4iIwfPHiw6+9LcmsUmfLOK3cRXBYLJgbI75R53nbFggm3qt6lqmOqOjYyMtL19yVpSJlkaqHwHcZxk/qJYobxLgRme6/TTD+mdS2H5wpUgZjDJaRGMuOBvqnuMKt6foeKLXROliMr8Uarm9RJZowxGqw7ioQD3g9gJ4DzROSAiPxF75uVhq+1sbh5y1UOt0BVIUJ6TX6nzPO2K7xOG6jqny9GQ+biOU5enqwV+cxJXrkLUaQcHCG9Js/qyfO2K8xe9rwOMycZVVKObKiEBYNJnTDH0MJgtvc6zfRjlfdyZNE3jOUmdcI75YXBrHB3mumXVdEQ4QFQhCKpcgnpNQ2Lm+dtN5gV7k4z/UImqilFXpyCY9ykRkIWQFkQzPae22HmZBDFvN0qAYdKiAVChvEuCGaF23clL0/WClbRKAedk8QCzOq5MJgVbs9tn/g/yTBmtvnmYBw3sUBj4hzP3W4w23u+I21n+YVRzFjQEmS3pnROkjrJJ87x3O0Ks8LdMVdJrHA5VFKYzBnEupOkTgLOnFwQzPZex5qTUcyyZSXwcoubwk3qgxPnFgazyue7HWpOsopGKfJwQDonSY2ELPK9IHTMVVIXnaqSJ5WizV53zJGHA9LiJjWS5yrp56ESVWDqCPDy74CXnweO/x449nvg+AvJ+5fe2vMm2BVut4NzMo551S6Bn1vcFG5SH30xVDJ9DDj6W+DlA4kwH/0tcPRA8vpoui6YmP+5wdOANa9elCbaFW6nQ83JSG3/+MZoWNwcKiH1EdRZACWOgIlDDev45d8mVvOx5xsC/fLzwPTROR8UYMWZwGnrgDM3AK/7T8CqVzUeK84EVr4S8IcXbVfsCrebzJxU1Zb5SBKLu49vtxaZPFcJLW5SIwtWclA1sYwn/73pcRiYyB6HgImXgBMvJc9PvAScOAhoNOeLBFg+kgjwmtcA574reX7aulSYzwZWngV4A921d4ExK9xZjPbJnJBhpBj0KdxFyS5ytLhJncwTblVg5gQwdTR9HEmEdzIT32YhPpSsnzySCPU8EW5i8DRg+Rpg2VrgtPXAqzYBK16ZWsdnJs9XvSqxlF1/EfZ8YTEr3G5Tbg3Pnf9+ECuW0TlZmLyQAi1uslCoAsEkMHM8sX6njgBTLzdEePoYMP1ysm46Wf/eF17AmwYOYs1XAcyk6+Pw5P/DHUjEd/kaYPgM4LQLgeHTk8fQamDZGY3Xw6cDy9Ykyz4U4zKYFe5OzrQo5szJMuS5ShhVsjSIIyCcAsLpdNn0PJgCwslkGUwkwjtzApiZAGaOpc/TRzCZPibSz06kn0sFu53VmzG4KnkMrYLEg3hBT8fr170G7rLVyfrh1cDQaY3H8BmpIJ8BDCwHmLp5HmaFu5MzjUmmypHnKmEc9+IQx02CeHyOOB5PBXCy8QgngXAGiGaAaBqIgsQSnbUMml5n2waJIEczs8W5nRXbDncwEcuBFcDAMsBPH8vOSJxv3jDgDyXLwRXA4Mpk28FVTeKbPh9cCQysBJrujB/Y8St87gfPYO+fXAIMtLiVJoUwLNxpVfKTWIhBROdkGXzOnGygmgreVEPwgqkmy7SFVZpZmzMT6bLJKp05BkwfbwwZTB9P1pXB8QFvKLnFdweSpePNWfqN1wPLG9u5A4A3mH5+MBXWoWSdO9h4L1tmwusPAf7yVKjTR4+HGCIWUlgQ7Ap3BwsxKaTAH78o2UWuXarc2ojCJov0xOznwYnZ4jnLukyt0zB9HU03CfB0asU2rYumGxZpZSQROH9ZYpH5XaIIAAAH8ElEQVQOrEgey84AVq9PrMzBVakVuqJhdebi2GTJDixPhXQYcJaG9RkwydSCYF+4T2Ihhpw5WYpG6bIeWNxZaNbUkcTjPysy4HASAZC9N3kkiZNttlCrCKk70GRNDiav/eGGVTmwLHFUeYPJ+uz93DrNrNChOZ8dbnwmHxrIHsuS9zjmWpkwiuE6LDnYLYWEW0QuAfB3AFwAX1XVW3raKjQn/j+JcMcxq2iUIL8QhvGc2//JxnjrzIn5z5ut3+ljsyMF8hCuo+2dVN5w6vVfnUQCrDo7tUyz8dGVTdZo83JZcivvD6WiOdQQaZ74fQnvlBeGjsItIi6A/wHgfQAOAHhCRL6nqk/3tGH95JxUTW/hp5Pb/mYnksaJh1+jZBmHTcvM0ZR9JnU2NTueopOtb3JizV3fInrADSexd/AEhh4LgMdKWt3ZmOrgaQ3BXfEKYO3rm5xRqxNhzsK0hk9vRAb4Q73p9x4Rx4ogjhFEijCKEcaKOFZEqohihWrykysUsQKxJu/HmhgUUawIY0UYJdtH6WezyWSC5LrjiDSWAByn8R6QvJe8TtY3k7yXbiOJ89mVxJJ1HYHnCJx0neMAbrrecQSOZNsm/zvbdjEII+3vPCVGKGJxXwzgV6r6LACIyLcA/AmA3gp3h1v7pObknAMgjhuCGM00jYG2CINqXobTqWd/qmk50bBM8wiAbNn0uczBtVi4g00OrPThDTQ5tPzktn74dGDlUH77L/4Q7vvx73DBOWfiLa87G9MYxLSkDwxgSoby5ZQMYlIHMSFDmNQhTMUuZqIY02GMmfQRxomgRZOKaEIRJ0rWRAjgRUBehEDgpCLhOIkQOU2CpUjFTwHVTOySdYkIxrkIZtvFmgho8jpZ1/z/FZlgJkKcfTYT4URYYwTpMowUM6lIL9Wc5Z4jGPAcDHoOBj0XQ76DId/FsgEXwwMuVgx6WDHoY+WQlzxPlyuHPCwf8LBswMXQgIth38WQn3w+/x7PheMIcwwtEKLa/iAVkS0ALlHVa9LXHwbwVlW9/mSfGRsb0/Hx8dKN+ezdY9iHRAQjVQRhnFsdgCbWB9IzVJGc/PnZmppB3SICiAuIk4QxidN4LW7TurkPSZaQ2c+z78zW58tsvTN7vTS/zr63ebvqPLH/cNeilFmJzZaj5PtyEjT5o42naFoAQMOizKzM9Enjf0jT/2r+jJz0X2fWatY8QaOt+fdm1q7MsXAlOb6k6ftPZvmiQ3ub2zD3ENX0WNZZ6+Z0zuwX895qOgOQnc/5dTS9M9BZ2yqar7OqjXWNi2JyMYzyu4nkYppcVOP0DmJel7fFkeTs9RzBRX9werkP9wnnn3E+brj4hkqfFZFdqjpWZNsiFner02LeTyYi2wBsA4BzzjmnyP+ez/DpQHQcAOAoECJqnOwt5Hv5oA/HywQyE765IljwkQlyy909NTh37XJMBVFyq+ykt86SPeZYwidZEpIRNQ0f5XdD8ey7oHjOXVKsiuUDZmMi+oYiPXgAwPqm1+sAPD93I1W9C8BdQGJxV2nMDf/54SofI4SQJUURL8ETAF4nIueKyACAPwPwvd42ixBCyMnoaHGraigi1wP4X0jCAb+mqj/vecsIIYS0pNBgk6r+I4B/7HFbCCGEFIABlYQQ0mdQuAkhpM+gcBNCSJ9B4SaEkD6Dwk0IIX1Gxynvlb5U5CCAfy24+VoALy14I2zDfV4aLMV9Bpbmfi/EPv+Bqo4U2bAnwl0GERkvOj//VIH7vDRYivsMLM39Xux95lAJIYT0GRRuQgjpMywI9111N6AGuM9Lg6W4z8DS3O9F3efax7gJIYSUw4LFTQghpAS1CreIXCIiz4jIr0TkxjrbshiIyNdE5EUR2VN3WxYLEVkvIo+IyF4R+bmIfKLuNvUaERkSkcdF5KfpPv9t3W1aLETEFZGnROShutuyGIjIfhH5FxHZLSLly35V/b91DZWkRYh/gaYixAD+vNdFiOtERN4F4DiAr6vqG+tuz2IgImcBOEtVnxSRlQB2AfjTU/x3FgDLVfW4iPgAfgTgE6r645qb1nNE5FMAxgCsUtXL6m5PrxGR/QDGVHVR49brtLjzIsSqOgMgK0J8yqKqjwI4XHc7FhNV/Z2qPpk+PwZgL4Cz621Vb9GE4+lLP32c8s4kEVkH4P0Avlp3W0516hTuswH8W9PrAzjFT+iljoiMAngTgJ/U25Lekw4Z7AbwIoCHVfWU32cAtwP4KwBx3Q1ZRBTAP4vIrrTu7qJQp3AXKkJMTg1EZAWA7wD4pKq+XHd7eo2qRqq6CUmN1otF5JQeGhORywC8qKq76m7LIrNZVd8M4I8B/Nd0OLTn1CnchYoQk/4nHef9DoD7VPWButuzmKjqEQA7AFxSc1N6zWYAl6djvt8C8B4R+Ua9Teo9qvp8unwRwINIhoB7Tp3CzSLES4DUUXc3gL2q+vm627MYiMiIiKxOnw8DeC+AffW2qreo6l+r6jpVHUVyLv9QVa+suVk9RUSWpw53iMhyAH8EYFEixmoTblUNAWRFiPcC+PapXoRYRO4HsBPAeSJyQET+ou42LQKbAXwYiQW2O31cWnejesxZAB4RkZ8hMVAeVtUlER63xDgTwI9E5KcAHgfwD6r6g8X4x5w5SQghfQZnThJCSJ9B4SaEkD6Dwk0IIX0GhZsQQvoMCjchhPQZFG5CCOkzKNyEENJnULgJIaTP+P+cNbMX522FLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f359a77c828>"
      ]
     },
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3591a655f8>"
      ]
     },
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f359a7482e8>"
      ]
     },
     "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": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-13.92311359   0.98782177  30.67251906   6.26437772   0.43855651   0.        ]\n",
      "[-9.19240479  0.5451138   4.58360158]\n",
      "[-0.00575804  0.17841612  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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode =    1, score = 3224.008\n",
      "Episode =    2, score = 3227.462\n",
      "Episode =    3, score = 3227.461\n",
      "Episode =    4, score = 3227.462\n",
      "Episode =    5, score = 3227.462\n",
      "Episode =    6, score = 3224.013\n",
      "Episode =    7, score = 3261.947\n",
      "Episode =    8, score = 3227.462\n",
      "Episode =    9, score = 3224.012\n",
      "Episode =   10, score = 3189.527\n",
      "Episode =   11, score = 3224.877\n",
      "Episode =   12, score = 3259.138\n",
      "Episode =   13, score = 3186.074\n",
      "Episode =   14, score = 3175.870\n",
      "Episode =   15, score = 3148.021\n",
      "Episode =   16, score = 3134.475\n",
      "Episode =   17, score = 3218.387\n",
      "Episode =   18, score = 2672.522\n",
      "Episode =   19, score = 3110.580\n",
      "Episode =   20, score = 3172.938\n",
      "Episode =   21, score = 3222.098\n",
      "Episode =   22, score = 3215.250\n",
      "Episode =   23, score = 3140.604\n",
      "Episode =   24, score = 3156.392\n",
      "Episode =   25, score = 3218.391\n",
      "Episode =   26, score = 3259.126\n",
      "Episode =   27, score = 3186.072\n",
      "Episode =   28, score = 3172.936\n",
      "Episode =   29, score = 2578.430\n",
      "Episode =   30, score = 3182.008\n",
      "Episode =   31, score = 3082.613\n",
      "Episode =   32, score = 3177.636\n",
      "Episode =   33, score = 3221.304\n",
      "Episode =   34, score = 3218.381\n",
      "Episode =   35, score = 3177.639\n",
      "Episode =   36, score = 3218.390\n",
      "Episode =   37, score = 3257.566\n",
      "Episode =   38, score = 3222.095\n",
      "Episode =   39, score = 3227.461\n",
      "Episode =   40, score = 3172.944\n",
      "Episode =   41, score = 3177.637\n",
      "Episode =   42, score = 3079.027\n",
      "Episode =   43, score = 3211.870\n",
      "Episode =   44, score = 3189.866\n",
      "Episode =   45, score = 3177.641\n",
      "Episode =   46, score = 3087.853\n",
      "Episode =   47, score = 3182.004\n",
      "Episode =   48, score = 3156.540\n",
      "Episode =   49, score = 3140.603\n",
      "Episode =   50, score = 3149.843\n",
      "Episode =   51, score = 3151.597\n",
      "Episode =   52, score = 3146.299\n",
      "Episode =   53, score = 3224.005\n",
      "Episode =   54, score = 3175.870\n",
      "Episode =   55, score = 3140.608\n",
      "Episode =   56, score = 3078.437\n",
      "Episode =   57, score = 3175.877\n",
      "Episode =   58, score = 3219.106\n",
      "Episode =   59, score = 3177.630\n",
      "Episode =   60, score = 3181.348\n",
      "Episode =   61, score = 3177.645\n",
      "Episode =   62, score = 3095.857\n",
      "Episode =   63, score = 3219.105\n",
      "Episode =   64, score = 3117.100\n",
      "Episode =   65, score = 3257.565\n",
      "Episode =   66, score = 3172.937\n",
      "Episode =   67, score = 3260.587\n",
      "Episode =   68, score = 3192.983\n",
      "Episode =   69, score = 3146.295\n",
      "Episode =   70, score = 3224.013\n",
      "Episode =   71, score = 3103.560\n",
      "Episode =   72, score = 3059.893\n",
      "Episode =   73, score = 3227.460\n",
      "Episode =   74, score = 3063.497\n",
      "Episode =   75, score = 3107.281\n",
      "Episode =   76, score = 3221.300\n",
      "Episode =   77, score = 3215.249\n",
      "Episode =   78, score = 3257.570\n",
      "Episode =   79, score = 3215.254\n",
      "Episode =   80, score = 3181.353\n",
      "Episode =   81, score = 3222.095\n",
      "Episode =   82, score = 3177.639\n",
      "Episode =   83, score = 3156.535\n",
      "Episode =   84, score = 3227.459\n",
      "Episode =   85, score = 3257.572\n",
      "Episode =   86, score = 3221.294\n",
      "Episode =   87, score = 3086.074\n",
      "Episode =   88, score = 3140.608\n",
      "Episode =   89, score = 3211.867\n",
      "Episode =   90, score = 3156.371\n",
      "Episode =   91, score = 3177.645\n",
      "Episode =   92, score = 3161.154\n",
      "Episode =   93, score = 3103.552\n",
      "Episode =   94, score = 3177.645\n",
      "Episode =   95, score = 3107.281\n",
      "Episode =   96, score = 3215.255\n",
      "Episode =   97, score = 3127.865\n",
      "Episode =   98, score = 3222.104\n",
      "Episode =   99, score = 3140.602\n",
      "Episode =  100, score = 3259.141\n",
      "Episode =  101, score = 3257.573\n",
      "Episode =  102, score = 3087.363\n",
      "Episode =  103, score = 3172.945\n",
      "Episode =  104, score = 3261.952\n",
      "Episode =  105, score = 3218.392\n",
      "Episode =  106, score = 3257.570\n",
      "Episode =  107, score = 3185.594\n",
      "Episode =  108, score = 3215.250\n",
      "Episode =  109, score = 3182.004\n",
      "Episode =  110, score = 3186.077\n",
      "Episode =  111, score = 3181.358\n",
      "Episode =  112, score = 3255.878\n",
      "Episode =  113, score = 3146.289\n",
      "Episode =  114, score = 3255.868\n",
      "Episode =  115, score = 3156.532\n",
      "Episode =  116, score = 3186.073\n",
      "Episode =  117, score = 3257.568\n",
      "Episode =  118, score = 3148.027\n",
      "Episode =  119, score = 3185.588\n",
      "Episode =  120, score = 3166.528\n",
      "Episode =  121, score = 2832.009\n",
      "Episode =  122, score = 3202.914\n",
      "Episode =  123, score = 2123.033\n",
      "Episode =  124, score = 3177.641\n",
      "Episode =  125, score = 3103.558\n",
      "Episode =  126, score = 3180.638\n",
      "Episode =  127, score = 3226.542\n",
      "Episode =  128, score = 3255.876\n",
      "Episode =  129, score = 3166.525\n",
      "Episode =  130, score = 3219.105\n",
      "Episode =  131, score = 3172.944\n",
      "Episode =  132, score = 3193.417\n",
      "Episode =  133, score = 3151.599\n",
      "Episode =  134, score = 3140.613\n",
      "Episode =  135, score = 2931.997\n",
      "Episode =  136, score = 3189.533\n",
      "Episode =  137, score = 3185.581\n",
      "Episode =  138, score = 3218.391\n",
      "Episode =  139, score = 3212.370\n",
      "Episode =  140, score = 3149.875\n",
      "Episode =  141, score = 3047.848\n",
      "Episode =  142, score = 3172.941\n",
      "Episode =  143, score = 3219.109\n",
      "Episode =  144, score = 3181.351\n",
      "Episode =  145, score = 3110.578\n",
      "Episode =  146, score = 3257.571\n",
      "Episode =  147, score = 3215.254\n",
      "Episode =  148, score = 3123.996\n",
      "Episode =  149, score = 3259.130\n",
      "Episode =  150, score = 3193.205\n",
      "Episode =  151, score = 3055.035\n",
      "Episode =  152, score = 2296.367\n",
      "Episode =  153, score = 3074.864\n",
      "Episode =  154, score = 3224.880\n",
      "Episode =  155, score = 3177.646\n",
      "Episode =  156, score = 3117.253\n",
      "Episode =  157, score = 3110.582\n",
      "Episode =  158, score = 2824.864\n",
      "Episode =  159, score = 3177.645\n",
      "Episode =  160, score = 3257.565\n",
      "Episode =  161, score = 3176.790\n",
      "Episode =  162, score = 3121.617\n",
      "Episode =  163, score = 3182.003\n",
      "Episode =  164, score = 3182.009\n",
      "Episode =  165, score = 3153.448\n",
      "Episode =  166, score = 3149.871\n",
      "Episode =  167, score = 3259.140\n",
      "Episode =  168, score = 3221.301\n",
      "Episode =  169, score = 3182.010\n",
      "Episode =  170, score = 3034.844\n",
      "Episode =  171, score = 3224.012\n",
      "Episode =  172, score = 3186.072\n",
      "Episode =  173, score = 3048.141\n",
      "Episode =  174, score = 2249.808\n",
      "Episode =  175, score = 3134.475\n",
      "Episode =  176, score = 3019.081\n",
      "Episode =  177, score = 3215.253\n",
      "Episode =  178, score = 3257.575\n",
      "Episode =  179, score = 3224.882\n",
      "Episode =  180, score = 3047.852\n",
      "Episode =  181, score = 3259.140\n",
      "Episode =  182, score = 3208.578\n",
      "Episode =  183, score = 3215.256\n",
      "Episode =  184, score = 2958.189\n",
      "Episode =  185, score = 3222.095\n",
      "Episode =  186, score = 3218.389\n",
      "Episode =  187, score = 3218.390\n",
      "Episode =  188, score = 3149.867\n",
      "Episode =  189, score = 3218.390\n",
      "Episode =  190, score = 3134.478\n",
      "Episode =  191, score = 3083.204\n",
      "Episode =  192, score = 3255.872\n",
      "Episode =  193, score = 3224.015\n",
      "Episode =  194, score = -12706306240715356635136.000\n",
      "Episode =  195, score = -52056.099\n",
      "Episode =  196, score = 3177.645\n",
      "Episode =  197, score = 3117.508\n",
      "Episode =  198, score = 2569.270\n",
      "Episode =  199, score = 3112.929\n",
      "Episode =  200, score = 3259.144\n",
      "Episode =  201, score = 3224.008\n",
      "Episode =  202, score = 3176.789\n",
      "Episode =  203, score = 2912.369\n",
      "Episode =  204, score = 3189.865\n",
      "Episode =  205, score = 3086.071\n",
      "Episode =  206, score = 3259.138\n",
      "Episode =  207, score = -1440825473750976.250\n",
      "Episode =  208, score = 3135.860\n",
      "Episode =  209, score = 3260.585\n",
      "Episode =  210, score = 3096.648\n",
      "Episode =  211, score = 3176.789\n",
      "Episode =  212, score = 3146.295\n",
      "Episode =  213, score = 3140.599\n",
      "Episode =  214, score = 3175.873\n",
      "Episode =  215, score = 3156.542\n",
      "Episode =  216, score = 3146.296\n",
      "Episode =  217, score = 3106.148\n",
      "Episode =  218, score = 3236.251\n",
      "Episode =  219, score = 3219.103\n",
      "Episode =  220, score = 3087.860\n",
      "Episode =  221, score = 3110.580\n",
      "Episode =  222, score = 3257.572\n",
      "Episode =  223, score = 3172.940\n",
      "Episode =  224, score = 3058.950\n",
      "Episode =  225, score = 3065.258\n",
      "Episode =  226, score = 3196.659\n",
      "Episode =  227, score = 3177.650\n",
      "Episode =  228, score = 3083.184\n",
      "Episode =  229, score = 3172.935\n",
      "Episode =  230, score = 3057.554\n",
      "Episode =  231, score = 3182.009\n",
      "Episode =  232, score = 3227.470\n",
      "Episode =  233, score = 3189.521\n",
      "Episode =  234, score = 3221.296\n",
      "Episode =  235, score = 3127.852\n",
      "Episode =  236, score = 3172.943\n",
      "Episode =  237, score = 3039.151\n",
      "Episode =  238, score = 2748.372\n",
      "Episode =  239, score = 3257.570\n",
      "Episode =  240, score = 3259.140\n",
      "Episode =  241, score = 3161.168\n",
      "Episode =  242, score = 3176.792\n",
      "Episode =  243, score = 3146.295\n",
      "Episode =  244, score = 3140.605\n",
      "Episode =  245, score = 3222.108\n",
      "Episode =  246, score = 3134.476\n",
      "Episode =  247, score = 3185.589\n",
      "Episode =  248, score = 3259.143\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode =  249, score = 3259.136\n",
      "Episode =  250, score = 3177.650\n",
      "Episode =  251, score = 3181.349\n",
      "Episode =  252, score = 3177.642\n",
      "Episode =  253, score = 3177.647\n",
      "Episode =  254, score = 3215.863\n",
      "Episode =  255, score = 3227.465\n",
      "Episode =  256, score = 3144.317\n",
      "Episode =  257, score = 3196.546\n",
      "Episode =  258, score = 3155.052\n",
      "Episode =  259, score = 3189.161\n",
      "Episode =  260, score = 3176.792\n",
      "Episode =  261, score = 3103.566\n",
      "Episode =  262, score = 3185.588\n",
      "Episode =  263, score = 3249.936\n",
      "Episode =  264, score = 3226.539\n",
      "Episode =  265, score = 3259.142\n",
      "Episode =  266, score = 3128.902\n",
      "Episode =  267, score = 3215.876\n",
      "Episode =  268, score = 3193.417\n",
      "Episode =  269, score = 3117.116\n",
      "Episode =  270, score = 3224.010\n",
      "Episode =  271, score = 3181.353\n",
      "Episode =  272, score = 3140.604\n",
      "Episode =  273, score = 2955.421\n",
      "Episode =  274, score = -29311461580259475292348819781451776.000\n",
      "Episode =  275, score = 3170.708\n",
      "Episode =  276, score = 3218.388\n",
      "Episode =  277, score = 2896.290\n",
      "Episode =  278, score = 3182.018\n",
      "Episode =  279, score = 3176.801\n",
      "Episode =  280, score = 3177.646\n",
      "Episode =  281, score = -321380864403081.438\n",
      "Episode =  282, score = 3215.263\n",
      "Episode =  283, score = 3259.144\n",
      "Episode =  284, score = 2992.454\n",
      "Episode =  285, score = 3134.474\n",
      "Episode =  286, score = 3181.352\n",
      "Episode =  287, score = 3134.482\n",
      "Episode =  288, score = 3155.046\n",
      "Episode =  289, score = 3218.392\n",
      "Episode =  290, score = 3221.305\n",
      "Episode =  291, score = 3103.572\n",
      "Episode =  292, score = 3182.010\n",
      "Episode =  293, score = 3177.638\n",
      "Episode =  294, score = 3181.356\n",
      "Episode =  295, score = 3186.078\n",
      "Episode =  296, score = 3151.603\n",
      "Episode =  297, score = 3222.104\n",
      "Episode =  298, score = 3215.249\n",
      "Episode =  299, score = 2860.580\n",
      "Episode =  300, score = 3134.838\n",
      "Episode =  301, score = 3189.533\n",
      "Episode =  302, score = 3177.638\n",
      "Episode =  303, score = 3181.352\n",
      "Episode =  304, score = 3155.050\n",
      "Episode =  305, score = 3135.875\n",
      "Episode =  306, score = 3255.875\n",
      "Episode =  307, score = 3221.300\n",
      "Episode =  308, score = 2789.137\n",
      "Episode =  309, score = 3259.136\n",
      "Episode =  310, score = 3215.252\n",
      "Episode =  311, score = 3181.353\n",
      "Episode =  312, score = 3259.133\n",
      "Episode =  313, score = 3182.011\n",
      "Episode =  314, score = 3161.155\n",
      "Episode =  315, score = 2841.243\n",
      "Episode =  316, score = -349924202094389697971434980406186444062720.000\n",
      "Episode =  317, score = 3186.071\n",
      "Episode =  318, score = 2941.816\n",
      "Episode =  319, score = 3185.583\n",
      "Episode =  320, score = 3033.190\n",
      "Episode =  321, score = 3140.599\n",
      "Episode =  322, score = 3084.846\n",
      "Episode =  323, score = 3255.873\n",
      "Episode =  324, score = 3218.389\n",
      "Episode =  325, score = 3177.644\n",
      "Episode =  326, score = 3185.589\n",
      "Episode =  327, score = 3074.867\n",
      "Episode =  328, score = 3218.388\n",
      "Episode =  329, score = 3255.876\n",
      "Episode =  330, score = 3186.075\n",
      "Episode =  331, score = 3177.645\n",
      "Episode =  332, score = 3196.650\n",
      "Episode =  333, score = 3222.099\n",
      "Episode =  334, score = 3224.017\n",
      "Episode =  335, score = 3176.347\n",
      "Episode =  336, score = 3259.138\n",
      "Episode =  337, score = 3110.578\n",
      "Episode =  338, score = 3259.130\n",
      "Episode =  339, score = 3146.294\n",
      "Episode =  340, score = 3149.877\n",
      "Episode =  341, score = 3158.493\n",
      "Episode =  342, score = 3185.591\n",
      "Episode =  343, score = 3091.869\n",
      "Episode =  344, score = 3215.879\n",
      "Episode =  345, score = 3127.862\n",
      "Episode =  346, score = 3140.598\n",
      "Episode =  347, score = 3227.469\n",
      "Episode =  348, score = 3189.528\n",
      "Episode =  349, score = 3219.101\n",
      "Episode =  350, score = 2992.455\n",
      "Episode =  351, score = 3015.495\n",
      "Episode =  352, score = 3254.046\n",
      "Episode =  353, score = 3259.144\n",
      "Episode =  354, score = 3181.353\n",
      "Episode =  355, score = 3117.106\n",
      "Episode =  356, score = 3003.433\n",
      "Episode =  357, score = 3185.054\n",
      "Episode =  358, score = 3221.309\n",
      "Episode =  359, score = 3257.564\n",
      "Episode =  360, score = 3193.205\n",
      "Episode =  361, score = 3146.299\n",
      "Episode =  362, score = 3259.137\n",
      "Episode =  363, score = 3196.651\n",
      "Episode =  364, score = -3385599138240895061347685892096.000\n",
      "Episode =  365, score = 3255.867\n",
      "Episode =  366, score = 3120.568\n",
      "Episode =  367, score = 3257.563\n",
      "Episode =  368, score = 3181.354\n",
      "Episode =  369, score = 3215.258\n",
      "Episode =  370, score = 3155.050\n",
      "Episode =  371, score = 3255.878\n",
      "Episode =  372, score = 3224.876\n",
      "Episode =  373, score = 3193.420\n",
      "Episode =  374, score = 3177.642\n",
      "Episode =  375, score = 3218.390\n",
      "Episode =  376, score = 3227.467\n",
      "Episode =  377, score = 3257.571\n",
      "Episode =  378, score = 3185.582\n",
      "Episode =  379, score = 3215.256\n",
      "Episode =  380, score = 3095.120\n",
      "Episode =  381, score = 3093.195\n",
      "Episode =  382, score = 3140.605\n",
      "Episode =  383, score = 3172.936\n",
      "Episode =  384, score = -37203506216.669\n",
      "Episode =  385, score = 3158.962\n",
      "Episode =  386, score = 3182.005\n",
      "Episode =  387, score = 3127.864\n",
      "Episode =  388, score = 3108.962\n",
      "Episode =  389, score = 3146.290\n",
      "Episode =  390, score = 3199.884\n",
      "Episode =  391, score = 3259.136\n",
      "Episode =  392, score = 3140.603\n",
      "Episode =  393, score = 3259.138\n",
      "Episode =  394, score = 3172.945\n",
      "Episode =  395, score = 3131.862\n",
      "Episode =  396, score = 3177.649\n",
      "Episode =  397, score = 3172.935\n",
      "Episode =  398, score = 3182.013\n",
      "Episode =  399, score = 3177.653\n",
      "Episode =  400, score = 3146.299\n",
      "Episode =  401, score = 3218.387\n",
      "Episode =  402, score = 3189.534\n",
      "Episode =  403, score = 3177.646\n",
      "Episode =  404, score = 3261.951\n",
      "Episode =  405, score = 3164.404\n",
      "Episode =  406, score = 3182.012\n",
      "Episode =  407, score = 3140.598\n",
      "Episode =  408, score = 3193.419\n",
      "Episode =  409, score = 3218.397\n",
      "Episode =  410, score = 3139.873\n",
      "Episode =  411, score = 3186.075\n",
      "Episode =  412, score = 2903.700\n",
      "Episode =  413, score = 3208.569\n",
      "Episode =  414, score = 3211.875\n",
      "Episode =  415, score = 3159.876\n",
      "Episode =  416, score = 3227.469\n",
      "Episode =  417, score = 3144.303\n",
      "Episode =  418, score = 3257.561\n",
      "Episode =  419, score = 3257.573\n",
      "Episode =  420, score = 3219.104\n",
      "Episode =  421, score = 3186.078\n",
      "Episode =  422, score = 3186.080\n",
      "Episode =  423, score = 3215.256\n",
      "Episode =  424, score = 3257.575\n",
      "Episode =  425, score = 3221.296\n",
      "Episode =  426, score = 3260.593\n",
      "Episode =  427, score = 3096.011\n",
      "Episode =  428, score = 3224.004\n",
      "Episode =  429, score = 3219.103\n",
      "Episode =  430, score = 3259.141\n",
      "Episode =  431, score = 3221.302\n",
      "Episode =  432, score = 3222.105\n",
      "Episode =  433, score = 3134.474\n",
      "Episode =  434, score = 3257.566\n",
      "Episode =  435, score = 3260.592\n",
      "Episode =  436, score = 3140.604\n",
      "Episode =  437, score = 3112.910\n",
      "Episode =  438, score = 3257.571\n",
      "Episode =  439, score = 3224.879\n",
      "Episode =  440, score = 3140.605\n",
      "Episode =  441, score = 3203.009\n",
      "Episode =  442, score = 3257.569\n",
      "Episode =  443, score = 3163.190\n",
      "Episode =  444, score = 3260.596\n",
      "Episode =  445, score = 3222.100\n",
      "Episode =  446, score = 3057.552\n",
      "Episode =  447, score = 3224.021\n",
      "Episode =  448, score = 2962.859\n",
      "Episode =  449, score = 3146.292\n",
      "Episode =  450, score = 3162.365\n",
      "Episode =  451, score = 3218.382\n",
      "Episode =  452, score = 3127.857\n",
      "Episode =  453, score = 3257.560\n",
      "Episode =  454, score = 3186.079\n",
      "Episode =  455, score = 3195.094\n",
      "Episode =  456, score = 3156.373\n",
      "Episode =  457, score = 3185.581\n",
      "Episode =  458, score = 3218.393\n",
      "Episode =  459, score = 3221.305\n",
      "Episode =  460, score = 3224.010\n",
      "Episode =  461, score = 3160.740\n",
      "Episode =  462, score = 2727.544\n",
      "Episode =  463, score = 3052.277\n",
      "Episode =  464, score = 3257.571\n",
      "Episode =  465, score = 3089.864\n",
      "Episode =  466, score = 3124.863\n",
      "Episode =  467, score = 3117.738\n",
      "Episode =  468, score = 3140.604\n",
      "Episode =  469, score = 3026.350\n",
      "Episode =  470, score = 1554.220\n",
      "Episode =  471, score = 3156.538\n",
      "Episode =  472, score = 3140.610\n",
      "Episode =  473, score = 3257.574\n",
      "Episode =  474, score = 3182.015\n",
      "Episode =  475, score = 3215.264\n",
      "Episode =  476, score = 3172.943\n",
      "Episode =  477, score = 3261.956\n",
      "Episode =  478, score = 3229.880\n",
      "Episode =  479, score = 3219.108\n",
      "Episode =  480, score = 3232.143\n",
      "Episode =  481, score = 2789.886\n",
      "Episode =  482, score = 3167.857\n",
      "Episode =  483, score = 3221.307\n",
      "Episode =  484, score = 3193.433\n",
      "Episode =  485, score = 3095.087\n",
      "Episode =  486, score = 3221.295\n",
      "Episode =  487, score = 3007.014\n",
      "Episode =  488, score = 3184.085\n",
      "Episode =  489, score = 3193.208\n",
      "Episode =  490, score = 3263.217\n",
      "Episode =  491, score = 3176.790\n",
      "Episode =  492, score = 3170.522\n",
      "Episode =  493, score = 3259.141\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode =  494, score = 3177.645\n",
      "Episode =  495, score = 3215.250\n",
      "Episode =  496, score = 3196.557\n",
      "Episode =  497, score = 3177.644\n",
      "Episode =  498, score = 3057.551\n",
      "Episode =  499, score = 3215.871\n",
      "Episode =  500, score = 3176.402\n",
      "Episode =  501, score = 3087.373\n",
      "Episode =  502, score = 3172.939\n",
      "Episode =  503, score = 3255.890\n",
      "Episode =  504, score = 3134.478\n",
      "Episode =  505, score = 3181.348\n",
      "Episode =  506, score = 3257.570\n",
      "Episode =  507, score = 3142.187\n",
      "Episode =  508, score = 3083.210\n",
      "Episode =  509, score = 3185.592\n",
      "Episode =  510, score = 3215.249\n",
      "Episode =  511, score = 3215.257\n",
      "Episode =  512, score = 3215.258\n",
      "Episode =  513, score = 3189.536\n",
      "Episode =  514, score = 3167.861\n",
      "Episode =  515, score = 3091.851\n",
      "Episode =  516, score = 3181.346\n",
      "Episode =  517, score = 3175.866\n",
      "Episode =  518, score = -786513819542997238345079847575879680.000\n",
      "Episode =  519, score = 3215.257\n",
      "Episode =  520, score = 3181.359\n",
      "Episode =  521, score = 3211.869\n",
      "Episode =  522, score = 3222.099\n",
      "Episode =  523, score = 3257.567\n",
      "Episode =  524, score = 2819.295\n",
      "Episode =  525, score = 3172.935\n",
      "Episode =  526, score = 3146.294\n",
      "Episode =  527, score = 3152.243\n",
      "Episode =  528, score = 3177.635\n",
      "Episode =  529, score = 3127.863\n",
      "Episode =  530, score = 3172.939\n",
      "Episode =  531, score = 3257.570\n",
      "Episode =  532, score = 3215.257\n",
      "Episode =  533, score = 3255.878\n",
      "Episode =  534, score = 3211.864\n",
      "Episode =  535, score = 3261.950\n",
      "Episode =  536, score = 3069.461\n",
      "Episode =  537, score = 3215.252\n",
      "Episode =  538, score = 3193.207\n",
      "Episode =  539, score = 3227.467\n",
      "Episode =  540, score = 3189.533\n",
      "Episode =  541, score = 3185.588\n",
      "Episode =  542, score = 3134.497\n",
      "Episode =  543, score = 3252.043\n",
      "Episode =  544, score = 3182.014\n",
      "Episode =  545, score = 3167.867\n",
      "Episode =  546, score = 3224.876\n",
      "Episode =  547, score = 3103.574\n",
      "Episode =  548, score = 3185.593\n",
      "Episode =  549, score = 3146.291\n",
      "Episode =  550, score = 3255.867\n",
      "Episode =  551, score = 3182.006\n",
      "Episode =  552, score = 3224.874\n",
      "Episode =  553, score = 3260.593\n",
      "Episode =  554, score = 3218.391\n",
      "Episode =  555, score = 3129.049\n",
      "Episode =  556, score = 3257.563\n",
      "Episode =  557, score = 3218.387\n",
      "Episode =  558, score = 3134.473\n",
      "Episode =  559, score = 2877.028\n",
      "Episode =  560, score = 3219.112\n",
      "Episode =  561, score = 3013.524\n",
      "Episode =  562, score = 3193.210\n",
      "Episode =  563, score = 3228.922\n",
      "Episode =  564, score = 3265.506\n",
      "Episode =  565, score = 3172.940\n",
      "Episode =  566, score = 3224.881\n",
      "Episode =  567, score = 3263.217\n",
      "Episode =  568, score = 3123.193\n",
      "Episode =  569, score = 3193.210\n",
      "Episode =  570, score = 3235.186\n",
      "Episode =  571, score = 2982.467\n",
      "Episode =  572, score = 3131.864\n",
      "Episode =  573, score = 2923.210\n",
      "Episode =  574, score = 3257.562\n",
      "Episode =  575, score = 3261.956\n",
      "Episode =  576, score = 3202.906\n",
      "Episode =  577, score = 3123.206\n",
      "Episode =  578, score = 3185.584\n",
      "Episode =  579, score = 3142.294\n",
      "Episode =  580, score = 3255.878\n",
      "Episode =  581, score = 3172.936\n",
      "Episode =  582, score = 3172.939\n",
      "Episode =  583, score = 3219.098\n",
      "Episode =  584, score = 3096.550\n",
      "Episode =  585, score = 3127.863\n",
      "Episode =  586, score = 3211.871\n",
      "Episode =  587, score = 3177.643\n",
      "Episode =  588, score = 3165.506\n",
      "Episode =  589, score = 3227.459\n",
      "Episode =  590, score = 3182.008\n",
      "Episode =  591, score = 3163.222\n",
      "Episode =  592, score = 3229.887\n",
      "Episode =  593, score = 3215.249\n",
      "Episode =  594, score = 3166.525\n",
      "Episode =  595, score = 3182.005\n",
      "Episode =  596, score = 3196.553\n",
      "Episode =  597, score = 3186.074\n",
      "Episode =  598, score = 3182.016\n",
      "Episode =  599, score = 3039.142\n",
      "Episode =  600, score = 3186.079\n",
      "Episode =  601, score = 3263.220\n",
      "Episode =  602, score = 2740.599\n",
      "Episode =  603, score = 3215.875\n",
      "Episode =  604, score = 3036.923\n",
      "Episode =  605, score = 3051.864\n",
      "Episode =  606, score = 3212.368\n",
      "Episode =  607, score = 3221.301\n",
      "Episode =  608, score = 3134.478\n",
      "Episode =  609, score = 3185.589\n",
      "Episode =  610, score = 3260.602\n",
      "Episode =  611, score = 3218.386\n",
      "Episode =  612, score = 3259.143\n",
      "Episode =  613, score = 3123.204\n",
      "Episode =  614, score = -572792105943189507648500483200385024.000\n",
      "Episode =  615, score = 3185.585\n",
      "Episode =  616, score = 3149.864\n",
      "Episode =  617, score = 3255.870\n",
      "Episode =  618, score = 2989.873\n",
      "Episode =  619, score = 3099.867\n",
      "Episode =  620, score = 3182.011\n",
      "Episode =  621, score = 3215.259\n",
      "Episode =  622, score = 3189.869\n",
      "Episode =  623, score = 2902.899\n",
      "Episode =  624, score = 3259.142\n",
      "Episode =  625, score = 3186.081\n",
      "Episode =  626, score = 2553.345\n",
      "Episode =  627, score = 3189.163\n",
      "Episode =  628, score = 3202.827\n",
      "Episode =  629, score = 3180.645\n",
      "Episode =  630, score = 3107.273\n",
      "Episode =  631, score = 3146.297\n",
      "Episode =  632, score = 3232.127\n",
      "Episode =  633, score = 3254.046\n",
      "Episode =  634, score = 3163.210\n",
      "Episode =  635, score = 3127.862\n",
      "Episode =  636, score = 3222.103\n",
      "Episode =  637, score = 3260.601\n",
      "Episode =  638, score = 3026.354\n",
      "Episode =  639, score = 3234.257\n",
      "Episode =  640, score = 3151.592\n",
      "Episode =  641, score = 3227.474\n",
      "Episode =  642, score = 3146.294\n",
      "Episode =  643, score = 3224.018\n",
      "Episode =  644, score = 3140.599\n",
      "Episode =  645, score = 3167.860\n",
      "Episode =  646, score = 3070.228\n",
      "Episode =  647, score = 3222.107\n",
      "Episode =  648, score = 3134.239\n",
      "Episode =  649, score = 3211.871\n",
      "Episode =  650, score = 3215.251\n",
      "Episode =  651, score = 3103.572\n",
      "Episode =  652, score = 3140.612\n",
      "Episode =  653, score = 3182.006\n",
      "Episode =  654, score = 3151.595\n",
      "Episode =  655, score = 3026.537\n",
      "Episode =  656, score = 3215.255\n",
      "Episode =  657, score = 3144.321\n",
      "Episode =  658, score = 3202.914\n",
      "Episode =  659, score = 3257.569\n",
      "Episode =  660, score = 3189.161\n",
      "Episode =  661, score = 3212.364\n",
      "Episode =  662, score = 3208.563\n",
      "Episode =  663, score = 3215.258\n",
      "Episode =  664, score = 3186.068\n",
      "Episode =  665, score = 3227.468\n",
      "Episode =  666, score = 3260.594\n",
      "Episode =  667, score = 3069.424\n",
      "Episode =  668, score = 3215.254\n",
      "Episode =  669, score = 3180.629\n",
      "Episode =  670, score = 3003.437\n",
      "Episode =  671, score = 3172.945\n",
      "Episode =  672, score = 3172.937\n",
      "Episode =  673, score = 3091.855\n",
      "Episode =  674, score = 3226.544\n",
      "Episode =  675, score = 3196.653\n",
      "Episode =  676, score = 3102.990\n",
      "Episode =  677, score = 3156.545\n",
      "Episode =  678, score = 3193.202\n",
      "Episode =  679, score = 3189.869\n",
      "Episode =  680, score = 3082.024\n",
      "Episode =  681, score = 3255.887\n",
      "Episode =  682, score = 3218.390\n",
      "Episode =  683, score = 3203.009\n",
      "Episode =  684, score = 3229.877\n",
      "Episode =  685, score = 3215.872\n",
      "Episode =  686, score = 3181.354\n",
      "Episode =  687, score = 3193.420\n",
      "Episode =  688, score = 3057.571\n",
      "Episode =  689, score = 3255.868\n",
      "Episode =  690, score = 3185.589\n",
      "Episode =  691, score = 3185.590\n",
      "Episode =  692, score = 3128.905\n",
      "Episode =  693, score = 3193.209\n",
      "Episode =  694, score = 3215.251\n",
      "Episode =  695, score = 3193.206\n",
      "Episode =  696, score = 3177.644\n",
      "Episode =  697, score = 3185.055\n",
      "Episode =  698, score = 3177.649\n",
      "Episode =  699, score = 3221.301\n",
      "Episode =  700, score = 3127.858\n",
      "Episode =  701, score = 3182.012\n",
      "Episode =  702, score = 3186.075\n",
      "Episode =  703, score = 3177.639\n",
      "Episode =  704, score = 3120.690\n",
      "Episode =  705, score = 3156.537\n",
      "Episode =  706, score = 3039.891\n",
      "Episode =  707, score = 3108.949\n",
      "Episode =  708, score = 3221.297\n",
      "Episode =  709, score = -66136267434604072.000\n",
      "Episode =  710, score = 3061.390\n",
      "Episode =  711, score = 3255.873\n",
      "Episode =  712, score = 3169.572\n",
      "Episode =  713, score = 3103.105\n",
      "Episode =  714, score = 3186.082\n",
      "Episode =  715, score = 3193.438\n",
      "Episode =  716, score = 3176.791\n",
      "Episode =  717, score = 3151.597\n",
      "Episode =  718, score = 3181.354\n",
      "Episode =  719, score = 3156.531\n",
      "Episode =  720, score = 3172.949\n",
      "Episode =  721, score = 3127.860\n",
      "Episode =  722, score = 3161.160\n",
      "Episode =  723, score = 3041.511\n",
      "Episode =  724, score = 1928.224\n",
      "Episode =  725, score = 3078.438\n",
      "Episode =  726, score = 3215.250\n",
      "Episode =  727, score = 3117.103\n",
      "Episode =  728, score = 3224.886\n",
      "Episode =  729, score = 3019.075\n",
      "Episode =  730, score = 3185.064\n",
      "Episode =  731, score = 3082.633\n",
      "Episode =  732, score = 3151.593\n",
      "Episode =  733, score = 3196.660\n",
      "Episode =  734, score = 3199.878\n",
      "Episode =  735, score = 3255.883\n",
      "Episode =  736, score = 3224.880\n",
      "Episode =  737, score = 3120.576\n",
      "Episode =  738, score = 3222.102\n",
      "Episode =  739, score = 3117.245\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode =  740, score = 3263.214\n",
      "Episode =  741, score = 3140.602\n",
      "Episode =  742, score = 3172.938\n",
      "Episode =  743, score = 3211.872\n",
      "Episode =  744, score = 3156.542\n",
      "Episode =  745, score = 3177.636\n",
      "Episode =  746, score = 3224.014\n",
      "Episode =  747, score = 3177.640\n",
      "Episode =  748, score = 3175.647\n",
      "Episode =  749, score = 3146.293\n",
      "Episode =  750, score = 3128.886\n",
      "Episode =  751, score = 2938.586\n",
      "Episode =  752, score = 3215.247\n",
      "Episode =  753, score = 3151.610\n",
      "Episode =  754, score = 3215.873\n",
      "Episode =  755, score = 2987.745\n",
      "Episode =  756, score = 3222.101\n",
      "Episode =  757, score = 3117.109\n",
      "Episode =  758, score = 2907.550\n",
      "Episode =  759, score = 3257.565\n",
      "Episode =  760, score = 3177.652\n",
      "Episode =  761, score = 3129.889\n",
      "Episode =  762, score = 3238.122\n",
      "Episode =  763, score = 3211.872\n",
      "Episode =  764, score = 3164.413\n",
      "Episode =  765, score = 3212.363\n",
      "Episode =  766, score = 3171.871\n",
      "Episode =  767, score = 3167.867\n",
      "Episode =  768, score = 3196.546\n",
      "Episode =  769, score = 3212.368\n",
      "Episode =  770, score = 3189.534\n",
      "Episode =  771, score = 3257.564\n",
      "Episode =  772, score = 3224.014\n",
      "Episode =  773, score = 3260.599\n",
      "Episode =  774, score = 3181.353\n",
      "Episode =  775, score = 3218.384\n",
      "Episode =  776, score = 3219.107\n",
      "Episode =  777, score = 3221.304\n",
      "Episode =  778, score = 3177.636\n",
      "Episode =  779, score = 3185.585\n",
      "Episode =  780, score = 3172.388\n",
      "Episode =  781, score = 3149.860\n",
      "Episode =  782, score = 3257.578\n",
      "Episode =  783, score = 3221.297\n",
      "Episode =  784, score = 3218.385\n",
      "Episode =  785, score = 3215.871\n",
      "Episode =  786, score = 2856.512\n",
      "Episode =  787, score = 2870.835\n",
      "Episode =  788, score = 3161.165\n",
      "Episode =  789, score = 3199.880\n",
      "Episode =  790, score = 3172.935\n",
      "Episode =  791, score = 3124.858\n",
      "Episode =  792, score = 3087.863\n",
      "Episode =  793, score = 3177.644\n",
      "Episode =  794, score = 3255.881\n",
      "Episode =  795, score = 3181.348\n",
      "Episode =  796, score = 3215.252\n",
      "Episode =  797, score = 3161.151\n",
      "Episode =  798, score = 3254.043\n",
      "Episode =  799, score = 3124.866\n",
      "Episode =  800, score = 3189.535\n",
      "Episode =  801, score = 3156.531\n",
      "Episode =  802, score = 3224.881\n",
      "Episode =  803, score = 3040.496\n",
      "Episode =  804, score = 3146.289\n",
      "Episode =  805, score = 3131.860\n",
      "Episode =  806, score = 3255.879\n",
      "Episode =  807, score = 3177.639\n",
      "Episode =  808, score = 3257.568\n",
      "Episode =  809, score = 3134.470\n",
      "Episode =  810, score = 3218.395\n",
      "Episode =  811, score = 3153.438\n",
      "Episode =  812, score = 3149.861\n",
      "Episode =  813, score = 3221.307\n",
      "Episode =  814, score = 3182.020\n",
      "Episode =  815, score = 3185.582\n",
      "Episode =  816, score = 3255.873\n",
      "Episode =  817, score = 3096.014\n",
      "Episode =  818, score = 3215.259\n",
      "Episode =  819, score = 3259.140\n",
      "Episode =  820, score = 3193.215\n",
      "Episode =  821, score = 3224.019\n",
      "Episode =  822, score = 3158.503\n",
      "Episode =  823, score = 3087.846\n",
      "Episode =  824, score = 3182.003\n",
      "Episode =  825, score = 3167.864\n",
      "Episode =  826, score = 3164.401\n",
      "Episode =  827, score = 3134.463\n",
      "Episode =  828, score = 3189.863\n",
      "Episode =  829, score = 3087.859\n",
      "Episode =  830, score = 3224.891\n",
      "Episode =  831, score = 3181.360\n",
      "Episode =  832, score = 3156.541\n",
      "Episode =  833, score = 3185.589\n",
      "Episode =  834, score = 3211.870\n",
      "Episode =  835, score = 3004.647\n",
      "Episode =  836, score = 3215.254\n",
      "Episode =  837, score = 3127.861\n",
      "Episode =  838, score = 3196.647\n",
      "Episode =  839, score = 3119.879\n",
      "Episode =  840, score = 3192.981\n",
      "Episode =  841, score = 3264.402\n",
      "Episode =  842, score = 3189.163\n",
      "Episode =  843, score = 3164.398\n",
      "Episode =  844, score = 3147.468\n",
      "Episode =  845, score = 3257.567\n",
      "Episode =  846, score = 3134.478\n",
      "Episode =  847, score = 3151.591\n",
      "Episode =  848, score = 2912.336\n",
      "Episode =  849, score = 3215.262\n",
      "Episode =  850, score = 3218.384\n",
      "Episode =  851, score = 3192.979\n",
      "Episode =  852, score = 3260.595\n",
      "Episode =  853, score = 3128.898\n",
      "Episode =  854, score = 3255.876\n",
      "Episode =  855, score = 3181.349\n",
      "Episode =  856, score = 3175.872\n",
      "Episode =  857, score = 3131.846\n",
      "Episode =  858, score = 3222.104\n",
      "Episode =  859, score = 3129.039\n",
      "Episode =  860, score = 3203.011\n",
      "Episode =  861, score = 3146.289\n",
      "Episode =  862, score = 3261.953\n",
      "Episode =  863, score = 3215.250\n",
      "Episode =  864, score = 2890.506\n",
      "Episode =  865, score = 3226.539\n",
      "Episode =  866, score = 2954.023\n",
      "Episode =  867, score = 3229.886\n",
      "Episode =  868, score = 2931.993\n",
      "Episode =  869, score = 3215.258\n",
      "Episode =  870, score = 3167.869\n",
      "Episode =  871, score = 3215.261\n",
      "Episode =  872, score = 3218.396\n",
      "Episode =  873, score = 3215.863\n",
      "Episode =  874, score = 3177.640\n",
      "Episode =  875, score = 3208.553\n",
      "Episode =  876, score = 3138.321\n",
      "Episode =  877, score = 3177.646\n",
      "Episode =  878, score = 3142.308\n",
      "Episode =  879, score = 3255.873\n",
      "Episode =  880, score = 3172.936\n",
      "Episode =  881, score = 3140.610\n",
      "Episode =  882, score = 3229.887\n",
      "Episode =  883, score = 3219.100\n",
      "Episode =  884, score = 3219.112\n",
      "Episode =  885, score = 3259.137\n",
      "Episode =  886, score = 3263.217\n",
      "Episode =  887, score = 3215.873\n",
      "Episode =  888, score = 3260.595\n",
      "Episode =  889, score = 3151.595\n",
      "Episode =  890, score = 3156.538\n",
      "Episode =  891, score = 3146.292\n",
      "Episode =  892, score = 3259.139\n",
      "Episode =  893, score = 3180.659\n",
      "Episode =  894, score = 3196.541\n",
      "Episode =  895, score = 3128.911\n",
      "Episode =  896, score = 3227.462\n",
      "Episode =  897, score = 3260.594\n",
      "Episode =  898, score = 3177.652\n",
      "Episode =  899, score = 3196.662\n",
      "Episode =  900, score = 3134.473\n",
      "Episode =  901, score = 3139.859\n",
      "Episode =  902, score = 3138.318\n",
      "Episode =  903, score = 3257.565\n",
      "Episode =  904, score = 3182.015\n",
      "Episode =  905, score = 3215.259\n",
      "Episode =  906, score = 3106.131\n",
      "Episode =  907, score = 3105.302\n",
      "Episode =  908, score = 3177.644\n",
      "Episode =  909, score = 3177.644\n",
      "Episode =  910, score = 3181.350\n",
      "Episode =  911, score = 3260.590\n",
      "Episode =  912, score = 3261.952\n",
      "Episode =  913, score = 3215.867\n",
      "Episode =  914, score = 3181.357\n",
      "Episode =  915, score = 3227.468\n",
      "Episode =  916, score = 3096.017\n",
      "Episode =  917, score = 3088.119\n",
      "Episode =  918, score = 3096.009\n",
      "Episode =  919, score = 3087.854\n",
      "Episode =  920, score = 3259.142\n",
      "Episode =  921, score = 3155.049\n",
      "Episode =  922, score = 3181.353\n",
      "Episode =  923, score = 3215.253\n",
      "Episode =  924, score = 3221.306\n",
      "Episode =  925, score = 3176.780\n",
      "Episode =  926, score = 3224.883\n",
      "Episode =  927, score = 3134.249\n",
      "Episode =  928, score = 3232.138\n",
      "Episode =  929, score = 3134.481\n",
      "Episode =  930, score = 3199.880\n",
      "Episode =  931, score = 3134.478\n",
      "Episode =  932, score = 3146.298\n",
      "Episode =  933, score = 3140.603\n",
      "Episode =  934, score = 3185.610\n",
      "Episode =  935, score = 3083.197\n",
      "Episode =  936, score = 3171.863\n",
      "Episode =  937, score = 3057.561\n",
      "Episode =  938, score = 3095.868\n",
      "Episode =  939, score = 3215.253\n",
      "Episode =  940, score = 3166.514\n",
      "Episode =  941, score = 3257.575\n",
      "Episode =  942, score = 3140.613\n",
      "Episode =  943, score = 3261.957\n",
      "Episode =  944, score = 3004.064\n",
      "Episode =  945, score = 3221.308\n",
      "Episode =  946, score = 3110.579\n",
      "Episode =  947, score = 3189.527\n",
      "Episode =  948, score = 3236.250\n",
      "Episode =  949, score = 3186.074\n",
      "Episode =  950, score = 3219.108\n",
      "Episode =  951, score = 3185.580\n",
      "Episode =  952, score = 3199.880\n",
      "Episode =  953, score = 3110.579\n",
      "Episode =  954, score = 3151.594\n",
      "Episode =  955, score = 3146.292\n",
      "Episode =  956, score = 2967.852\n",
      "Episode =  957, score = 3228.908\n",
      "Episode =  958, score = 2836.203\n",
      "Episode =  959, score = 3185.586\n",
      "Episode =  960, score = 3167.858\n",
      "Episode =  961, score = 3180.638\n",
      "Episode =  962, score = 3172.939\n",
      "Episode =  963, score = 3019.082\n",
      "Episode =  964, score = 3193.421\n",
      "Episode =  965, score = 3203.021\n",
      "Episode =  966, score = 3181.352\n",
      "Episode =  967, score = 3176.792\n",
      "Episode =  968, score = 3257.567\n",
      "Episode =  969, score = 3261.957\n",
      "Episode =  970, score = 3224.879\n",
      "Episode =  971, score = 3259.143\n",
      "Episode =  972, score = 3259.137\n",
      "Episode =  973, score = 3119.889\n",
      "Episode =  974, score = 3181.355\n",
      "Episode =  975, score = 3149.850\n",
      "Episode =  976, score = 3151.596\n",
      "Episode =  977, score = 3159.882\n",
      "Episode =  978, score = 3218.390\n",
      "Episode =  979, score = 3047.847\n",
      "Episode =  980, score = 3131.457\n",
      "Episode =  981, score = 3110.585\n",
      "Episode =  982, score = 3226.536\n",
      "Episode =  983, score = 3180.637\n",
      "Episode =  984, score = 3257.566\n",
      "Episode =  985, score = 3103.564\n",
      "Episode =  986, score = 3140.601\n",
      "Episode =  987, score = 3196.750\n",
      "Episode =  988, score = 3261.953\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode =  989, score = 3172.933\n",
      "Episode =  990, score = 3132.128\n",
      "Episode =  991, score = 3129.035\n",
      "Episode =  992, score = 3222.103\n",
      "Episode =  993, score = 3103.563\n",
      "Episode =  994, score = 3172.939\n",
      "Episode =  995, score = 2891.302\n",
      "Episode =  996, score = 3127.861\n",
      "Episode =  997, score = 3259.137\n",
      "Episode =  998, score = 3260.599\n",
      "Episode =  999, score = 3078.113\n",
      "Episode = 1000, score = 3192.982\n"
     ]
    }
   ],
   "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}\".format(\n",
    "                i_episode, agent.score))# [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": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ep=   1, score=2640.293 (best=2640.293) pos=-0.55 -0.0 0.0\n",
      "\n",
      "Ep=   2, score=2640.293 (best=2640.293) pos=-0.55 -0.0 0.0\n",
      "\n",
      "Ep=   3, score=2640.293 (best=2640.293) pos=-0.55 -0.0 0.0\n",
      "\n",
      "Ep=   4, score=2640.293 (best=2640.293) pos=-0.55 -0.0 0.0\n",
      "\n",
      "Ep=   5, score=719.825 (best=2640.293) pos=-0.0 0.0 0.0\n",
      "\n",
      "Ep=   6, score=719.825 (best=2640.293) pos=-0.03 0.0 0.0\n",
      "\n",
      "Ep=   7, score=719.825 (best=2640.293) pos=-0.03 0.0 0.0\n",
      "\n",
      "Ep=   8, score=719.825 (best=2640.293) pos=-0.03 0.0 0.0\n",
      "\n",
      "Ep=   9, score=719.825 (best=2640.293) pos=-0.03 0.0 0.0\n",
      "\n",
      "Ep=  10, score=719.825 (best=2640.293) pos=-0.03 0.0 0.0\n",
      "\n",
      "Ep=  11, score=719.825 (best=2640.293) pos=-0.0 0.0 0.0\n",
      "\n",
      "Ep=  12, score=719.825 (best=2640.293) pos=-0.03 -0.0 0.0\n",
      "\n",
      "Ep=  13, score=959.805 (best=2640.293) pos=0.0 -0.0 0.0\n",
      "\n",
      "Ep=  14, score=2160.039 (best=2640.293) pos=-0.33 -0.01 0.0\n",
      "\n",
      "Ep=  15, score=1199.761 (best=2640.293) pos=-0.13 -0.0 0.0\n",
      "\n",
      "Ep=  16, score=959.795 (best=2640.293) pos=-0.07 0.02 0.0\n",
      "\n",
      "Ep=  17, score=719.842 (best=2640.293) pos=-0.06 0.01 0.0\n",
      "\n",
      "Ep=  18, score=719.833 (best=2640.293) pos=-0.06 -0.01 0.0\n",
      "\n",
      "Ep=  19, score=719.830 (best=2640.293) pos=-0.03 -0.0 0.0\n",
      "\n",
      "Ep=  20, score=719.832 (best=2640.293) pos=-0.04 -0.0 0.0\n",
      "\n",
      "Ep=  21, score=719.842 (best=2640.293) pos=-0.07 -0.04 0.0\n",
      "\n",
      "Ep=  22, score=2160.106 (best=2640.293) pos=-2.15 0.33 0.0\n",
      "\n",
      "Ep=  23, score=959.831 (best=2640.293) pos=-0.56 0.16 0.0\n",
      "\n",
      "Ep=  24, score=479.892 (best=2640.293) pos=-0.08 0.06 0.0\n",
      "\n",
      "Ep=  25, score=479.889 (best=2640.293) pos=-0.08 0.06 0.0\n",
      "\n",
      "Ep=  26, score=479.888 (best=2640.293) pos=-0.09 0.05 0.0\n",
      "\n",
      "Ep=  27, score=479.886 (best=2640.293) pos=-0.1 0.05 0.0\n",
      "\n",
      "Ep=  28, score=479.885 (best=2640.293) pos=-0.1 0.04 0.0\n",
      "\n",
      "Ep=  29, score=479.884 (best=2640.293) pos=-0.1 0.04 0.0\n",
      "\n",
      "Ep=  30, score=479.883 (best=2640.293) pos=-0.11 0.04 0.0\n",
      "\n",
      "Ep=  31, score=479.883 (best=2640.293) pos=-0.11 0.04 0.0\n",
      "\n",
      "Ep=  32, score=479.883 (best=2640.293) pos=-0.11 0.04 0.0\n",
      "\n",
      "Ep=  33, score=479.883 (best=2640.293) pos=-0.11 0.03 0.0\n",
      "\n",
      "Ep=  34, score=479.883 (best=2640.293) pos=-0.1 0.03 0.0\n",
      "\n",
      "Ep=  35, score=479.884 (best=2640.293) pos=-0.1 0.03 0.0\n",
      "\n",
      "Ep=  36, score=479.885 (best=2640.293) pos=-0.09 0.02 0.0\n",
      "\n",
      "Ep=  37, score=479.887 (best=2640.293) pos=-0.08 0.03 0.0\n",
      "\n",
      "Ep=  38, score=479.889 (best=2640.293) pos=-0.07 0.07 0.0\n",
      "\n",
      "Ep=  39, score=479.891 (best=2640.293) pos=-0.06 0.04 0.0\n",
      "\n",
      "Ep=  40, score=479.896 (best=2640.293) pos=-0.06 0.08 0.0\n",
      "\n",
      "Ep=  41, score=479.902 (best=2640.293) pos=-0.05 0.11 0.0\n",
      "\n",
      "Ep=  42, score=479.911 (best=2640.293) pos=-0.04 0.13 0.0\n",
      "\n",
      "Ep=  43, score=719.871 (best=2640.293) pos=-0.12 0.41 0.0\n",
      "\n",
      "Ep=  44, score=959.888 (best=2640.293) pos=-0.24 0.89 0.0\n",
      "\n",
      "Ep=  45, score=1680.604 (best=2640.293) pos=-0.89 3.1 0.0\n",
      "\n",
      "Ep=  46, score=5067.668 (best=5067.668) pos=-10.55 22.6 0.0\n",
      "\n",
      "Ep=  47, score=15017.006 (best=15017.006) pos=-48.35 150.0 80.32\n",
      "\n",
      "Ep=  48, score=45296.542 (best=45296.542) pos=-72.54 150.0 170.42\n",
      "\n",
      "Ep=  49, score=77897.054 (best=77897.054) pos=-18.59 25.89 265.77\n",
      "\n",
      "Ep=  50, score=77820.320 (best=77897.054) pos=-10.89 10.55 267.73\n",
      "\n",
      "Ep=  51, score=77800.645 (best=77897.054) pos=-5.72 2.1 268.26\n",
      "\n",
      "Ep=  52, score=77800.123 (best=77897.054) pos=-3.4 4.35 268.27\n",
      "\n",
      "Ep=  53, score=78796.551 (best=78796.551) pos=-0.94 -0.93 268.35\n",
      "\n",
      "Ep=  54, score=77805.892 (best=78796.551) pos=-9.19 1.27 268.12\n",
      "\n",
      "Ep=  55, score=82358.200 (best=82358.200) pos=-20.52 0.48 123.56\n",
      "\n",
      "Ep=  56, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  57, score=2160.721 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  58, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  59, score=2160.737 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  60, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  61, score=2160.733 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  62, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  63, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  64, score=2160.724 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  65, score=2160.726 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  66, score=2160.713 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  67, score=2160.712 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  68, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  69, score=2160.717 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  70, score=2160.737 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  71, score=2160.717 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  72, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  73, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  74, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  75, score=2160.737 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  76, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  77, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  78, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  79, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  80, score=2160.716 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  81, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  82, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  83, score=2160.727 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  84, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  85, score=2160.717 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  86, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  87, score=2160.723 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  88, score=2160.735 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  89, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  90, score=2160.731 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  91, score=2160.718 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  92, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  93, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  94, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  95, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  96, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  97, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep=  98, score=2160.724 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep=  99, score=2160.737 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 100, score=2160.705 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 101, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 102, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 103, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 104, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 105, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 106, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 107, score=2160.727 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 108, score=2160.705 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 109, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 110, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 111, score=2160.705 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 112, score=2160.713 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 113, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 114, score=2160.713 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 115, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 116, score=2160.709 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 117, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 118, score=2160.718 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 119, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 120, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 121, score=2160.726 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 122, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 123, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 124, score=2160.716 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 125, score=2160.735 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 126, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 127, score=2160.704 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 128, score=2160.721 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 129, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 130, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 131, score=2160.704 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 132, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 133, score=2160.705 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 134, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 135, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 136, score=2160.712 (best=82358.200) pos=-0.63 0.0 0.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ep= 137, score=2160.709 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 138, score=2160.708 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 139, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 140, score=2160.737 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 141, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 142, score=2160.717 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 143, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 144, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 145, score=2160.737 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 146, score=2160.725 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 147, score=2160.728 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 148, score=2160.738 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 149, score=2160.738 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 150, score=2160.727 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
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      "\n",
      "Ep= 152, score=2160.712 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 153, score=2160.715 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
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      "\n",
      "Ep= 155, score=2160.739 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 156, score=2160.739 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 157, score=2160.739 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 158, score=2160.718 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 159, score=2160.707 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 160, score=2160.723 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 161, score=2160.708 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 162, score=2160.741 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 163, score=2160.742 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 164, score=2160.742 (best=82358.200) pos=-0.63 -0.0 0.0\n",
      "\n",
      "Ep= 165, score=2160.710 (best=82358.200) pos=-0.63 0.0 0.0\n",
      "\n",
      "Ep= 166, score=2160.728 (best=82358.200) pos=-0.62 0.0 0.0\n",
      "\n",
      "Ep= 167, score=2160.720 (best=82358.200) pos=-0.62 -0.0 0.0\n",
      "\n",
      "Ep= 168, score=2160.745 (best=82358.200) pos=-0.62 -0.0 0.0\n",
      "\n",
      "Ep= 169, score=2160.744 (best=82358.200) pos=-0.62 -0.0 0.0\n",
      "\n",
      "Ep= 170, score=2160.741 (best=82358.200) pos=-0.62 -0.0 0.0\n",
      "\n",
      "Ep= 171, score=2160.747 (best=82358.200) pos=-0.62 -0.0 0.0\n",
      "\n",
      "Ep= 172, score=2160.731 (best=82358.200) pos=-0.62 -0.0 0.0\n",
      "\n",
      "Ep= 173, score=2160.717 (best=82358.200) pos=-0.62 0.0 0.0\n",
      "\n",
      "Ep= 174, score=2160.719 (best=82358.200) pos=-0.62 -0.0 0.0\n",
      "\n",
      "Ep= 175, score=2160.721 (best=82358.200) pos=-0.62 0.0 0.0\n",
      "\n",
      "Ep= 176, score=2160.725 (best=82358.200) pos=-0.62 0.0 0.0\n",
      "\n",
      "Ep= 177, score=2160.729 (best=82358.200) pos=-0.62 0.0 0.0\n",
      "\n",
      "Ep= 178, score=2160.734 (best=82358.200) pos=-0.62 0.0 0.0\n",
      "\n",
      "Ep= 179, score=2160.752 (best=82358.200) pos=-0.61 -0.0 0.0\n",
      "\n",
      "Ep= 180, score=2160.750 (best=82358.200) pos=-0.61 0.0 0.0\n",
      "\n",
      "Ep= 181, score=2160.763 (best=82358.200) pos=-0.6 0.0 0.0\n",
      "\n",
      "Ep= 182, score=2160.785 (best=82358.200) pos=-0.6 0.0 0.0\n",
      "\n",
      "Ep= 183, score=2160.813 (best=82358.200) pos=-0.59 0.0 0.0\n",
      "\n",
      "Ep= 184, score=2160.870 (best=82358.200) pos=-0.57 -0.0 0.0\n",
      "\n",
      "Ep= 185, score=2160.899 (best=82358.200) pos=-0.55 0.0 0.0\n",
      "\n",
      "Ep= 186, score=2160.988 (best=82358.200) pos=-0.51 0.0 0.0\n",
      "\n",
      "Ep= 187, score=2161.153 (best=82358.200) pos=-0.44 -0.0 0.0\n",
      "\n",
      "Ep= 188, score=2161.267 (best=82358.200) pos=-0.34 0.0 0.0\n",
      "\n",
      "Ep= 189, score=2161.418 (best=82358.200) pos=-0.18 -0.0 0.0\n",
      "\n",
      "Ep= 190, score=2161.306 (best=82358.200) pos=0.11 -0.0 0.0\n",
      "\n",
      "Ep= 191, score=1680.500 (best=82358.200) pos=0.45 0.0 0.0\n",
      "\n",
      "Ep= 192, score=959.900 (best=82358.200) pos=0.3 0.0 0.0\n",
      "\n",
      "Ep= 193, score=719.863 (best=82358.200) pos=0.14 0.0 0.0\n",
      "\n",
      "Ep= 194, score=479.902 (best=82358.200) pos=0.06 0.0 0.0\n",
      "\n",
      "Ep= 195, score=479.893 (best=82358.200) pos=0.04 0.0 0.0\n",
      "\n",
      "Ep= 196, score=479.887 (best=82358.200) pos=0.03 -0.0 0.0\n",
      "\n",
      "Ep= 197, score=1440.224 (best=82358.200) pos=-1.26 0.0 0.0\n",
      "\n",
      "Ep= 198, score=479.880 (best=82358.200) pos=0.04 0.0 0.0\n",
      "\n",
      "Ep= 199, score=479.880 (best=82358.200) pos=0.05 0.0 0.0\n",
      "\n",
      "Ep= 200, score=479.880 (best=82358.200) pos=0.06 0.0 0.0\n",
      "\n",
      "Ep= 201, score=479.880 (best=82358.200) pos=0.05 0.0 0.0\n",
      "\n",
      "Ep= 202, score=479.880 (best=82358.200) pos=0.05 0.0 0.0\n",
      "\n",
      "Ep= 203, score=479.880 (best=82358.200) pos=0.04 0.0 0.0\n",
      "\n",
      "Ep= 204, score=479.880 (best=82358.200) pos=0.03 0.0 0.0\n",
      "\n",
      "Ep= 205, score=479.880 (best=82358.200) pos=0.01 0.0 0.0\n",
      "\n",
      "Ep= 206, score=479.880 (best=82358.200) pos=-0.0 0.0 0.0\n",
      "\n",
      "Ep= 207, score=479.880 (best=82358.200) pos=-0.03 0.0 0.0\n",
      "\n",
      "Ep= 208, score=479.881 (best=82358.200) pos=-0.05 0.0 0.0\n",
      "\n",
      "Ep= 209, score=479.882 (best=82358.200) pos=-0.06 0.0 0.0\n",
      "\n",
      "Ep= 210, score=479.882 (best=82358.200) pos=-0.07 0.0 0.0\n",
      "\n",
      "Ep= 211, score=479.883 (best=82358.200) pos=-0.07 -0.0 0.0\n",
      "\n",
      "Ep= 212, score=719.836 (best=82358.200) pos=-0.3 -0.0 0.0\n",
      "\n",
      "Ep= 213, score=1680.310 (best=82358.200) pos=-2.56 0.0 0.0\n",
      "\n",
      "Ep= 214, score=4106.858 (best=82358.200) pos=-11.23 0.0 0.0\n",
      "\n",
      "Ep= 215, score=479.894 (best=82358.200) pos=-0.02 0.0 0.0\n",
      "\n",
      "Ep= 216, score=479.897 (best=82358.200) pos=-0.04 0.0 0.0\n",
      "\n",
      "Ep= 217, score=479.899 (best=82358.200) pos=-0.05 0.0 0.0\n",
      "\n",
      "Ep= 218, score=479.902 (best=82358.200) pos=-0.07 0.0 0.0\n",
      "\n",
      "Ep= 219, score=479.904 (best=82358.200) pos=-0.08 0.0 0.0\n",
      "\n",
      "Ep= 220, score=479.907 (best=82358.200) pos=-0.1 0.0 0.0\n",
      "\n",
      "Ep= 221, score=479.909 (best=82358.200) pos=-0.11 0.0 0.0\n",
      "\n",
      "Ep= 222, score=479.911 (best=82358.200) pos=-0.12 0.0 0.0\n",
      "\n",
      "Ep= 223, score=479.915 (best=82358.200) pos=-0.13 0.0 0.0\n",
      "\n",
      "Ep= 224, score=479.918 (best=82358.200) pos=-0.13 0.0 0.0\n",
      "\n",
      "Ep= 225, score=479.922 (best=82358.200) pos=-0.14 0.0 0.0\n",
      "\n",
      "Ep= 226, score=479.926 (best=82358.200) pos=-0.15 0.0 0.0\n",
      "\n",
      "Ep= 227, score=479.930 (best=82358.200) pos=-0.16 0.0 0.0\n",
      "\n",
      "Ep= 228, score=479.934 (best=82358.200) pos=-0.16 0.0 0.0\n",
      "\n",
      "Ep= 229, score=719.878 (best=82358.200) pos=-0.38 0.0 0.0\n",
      "\n",
      "Ep= 230, score=719.889 (best=82358.200) pos=-0.41 0.0 0.0\n",
      "\n",
      "Ep= 231, score=719.900 (best=82358.200) pos=-0.44 0.0 0.0\n",
      "\n",
      "Ep= 232, score=719.914 (best=82358.200) pos=-0.46 0.0 0.0\n",
      "\n",
      "Ep= 233, score=719.931 (best=82358.200) pos=-0.48 0.0 0.0\n",
      "\n",
      "Ep= 234, score=719.947 (best=82358.200) pos=-0.49 0.0 0.0\n",
      "\n",
      "Ep= 235, score=719.970 (best=82358.200) pos=-0.5 0.0 0.0\n",
      "\n",
      "Ep= 236, score=719.989 (best=82358.200) pos=-0.51 0.0 0.0\n",
      "\n",
      "Ep= 237, score=959.957 (best=82358.200) pos=-0.97 0.0 0.0\n",
      "\n",
      "Ep= 238, score=960.016 (best=82358.200) pos=-0.99 0.0 0.0\n",
      "\n",
      "Ep= 239, score=960.085 (best=82358.200) pos=-1.0 0.0 0.0\n",
      "\n",
      "Ep= 240, score=1200.108 (best=82358.200) pos=-1.63 0.0 0.0\n",
      "\n",
      "Ep= 241, score=1200.232 (best=82358.200) pos=-1.64 0.0 0.0\n",
      "\n",
      "Ep= 242, score=1440.351 (best=82358.200) pos=-2.42 0.0 0.0\n",
      "\n",
      "Ep= 243, score=1440.606 (best=82358.200) pos=-2.42 0.0 0.0\n",
      "\n",
      "Ep= 244, score=1440.597 (best=82358.200) pos=-2.42 0.0 0.0\n",
      "\n",
      "Ep= 245, score=1440.720 (best=82358.200) pos=-2.36 0.01 0.0\n",
      "\n",
      "Ep= 246, score=1440.422 (best=82358.200) pos=-2.23 0.04 0.0\n",
      "\n",
      "Ep= 247, score=1680.517 (best=82358.200) pos=-3.21 0.55 0.0\n",
      "\n",
      "Ep= 248, score=959.780 (best=82358.200) pos=-0.73 0.04 0.0\n",
      "\n",
      "Ep= 249, score=2160.213 (best=82358.200) pos=-3.54 -0.23 0.0\n",
      "\n",
      "Ep= 250, score=2160.760 (best=82358.200) pos=-2.97 1.1 0.0\n",
      "\n",
      "Ep= 251, score=1920.553 (best=82358.200) pos=-2.23 1.01 0.0\n",
      "\n",
      "Ep= 252, score=1920.681 (best=82358.200) pos=-1.34 1.21 0.0\n",
      "\n",
      "Ep= 253, score=1920.707 (best=82358.200) pos=-1.11 1.24 0.0\n",
      "\n",
      "Ep= 254, score=1920.718 (best=82358.200) pos=-1.02 1.25 0.0\n",
      "\n",
      "Ep= 255, score=2160.685 (best=82358.200) pos=-1.12 1.54 0.0\n",
      "\n",
      "Ep= 256, score=2160.713 (best=82358.200) pos=-0.79 1.56 0.0\n",
      "\n",
      "Ep= 257, score=2160.732 (best=82358.200) pos=-0.53 1.58 0.0\n",
      "\n",
      "Ep= 258, score=2160.763 (best=82358.200) pos=-0.17 1.59 0.0\n",
      "\n",
      "Ep= 259, score=2160.773 (best=82358.200) pos=-0.08 1.59 0.0\n",
      "\n",
      "Ep= 260, score=2160.776 (best=82358.200) pos=-0.06 1.59 0.0\n",
      "\n",
      "Ep= 261, score=2160.776 (best=82358.200) pos=-0.05 1.59 0.0\n",
      "\n",
      "Ep= 262, score=2160.777 (best=82358.200) pos=-0.05 1.59 0.0\n",
      "\n",
      "Ep= 263, score=2160.778 (best=82358.200) pos=-0.05 1.59 0.0\n",
      "\n",
      "Ep= 264, score=2160.778 (best=82358.200) pos=-0.04 1.59 0.0\n",
      "\n",
      "Ep= 265, score=2160.778 (best=82358.200) pos=-0.04 1.59 0.0\n",
      "\n",
      "Ep= 266, score=2160.778 (best=82358.200) pos=-0.04 1.59 0.0\n",
      "\n",
      "Ep= 267, score=2160.779 (best=82358.200) pos=-0.04 1.59 0.0\n",
      "\n",
      "Ep= 268, score=2160.779 (best=82358.200) pos=-0.03 1.59 0.0\n",
      "\n",
      "Ep= 269, score=2160.779 (best=82358.200) pos=-0.03 1.59 0.0\n",
      "\n",
      "Ep= 270, score=2160.780 (best=82358.200) pos=-0.03 1.59 0.0\n",
      "\n",
      "Ep= 271, score=2160.780 (best=82358.200) pos=-0.03 1.59 0.0\n",
      "\n",
      "Ep= 272, score=2160.780 (best=82358.200) pos=-0.03 1.59 0.0\n",
      "\n",
      "Ep= 273, score=2160.780 (best=82358.200) pos=-0.03 1.59 0.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ep= 274, score=2160.780 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 275, score=2160.779 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 276, score=2160.780 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 277, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 278, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 279, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 280, score=2160.780 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 281, score=2160.780 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 282, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 283, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 284, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 285, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 286, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 287, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 288, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 289, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 290, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 291, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 292, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 293, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 294, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 295, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 296, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 297, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 298, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 299, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 300, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 301, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 302, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 303, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 304, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 305, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 306, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 307, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 308, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 309, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 310, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 311, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 312, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 313, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 314, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 315, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 316, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 317, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 318, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 319, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 320, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 321, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 322, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 323, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 324, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 325, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 326, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 327, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 328, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 329, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 330, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 331, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 332, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 333, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 334, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 335, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 336, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 337, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 338, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 339, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 340, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 341, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 342, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 343, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 344, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 345, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 346, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 347, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 348, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 349, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 350, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 351, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 352, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 353, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 354, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 355, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 356, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 357, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 358, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 359, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 360, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 361, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 362, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 363, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 364, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 365, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 366, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 367, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 368, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 369, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 370, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 371, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 372, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 373, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 374, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 375, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 376, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 377, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 378, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 379, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 380, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 381, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 382, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 383, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 384, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 385, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 386, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 387, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 388, score=2160.783 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 389, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 390, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 391, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 392, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 393, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 394, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 395, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 396, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 397, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 398, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 399, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 400, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 401, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 402, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 403, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 404, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 405, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 406, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 407, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 408, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ep= 409, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 410, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 411, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 412, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 413, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 414, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 415, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 416, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 417, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 418, score=2160.782 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 419, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 420, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 421, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 422, score=2160.781 (best=82358.200) pos=-0.01 1.59 0.0\n",
      "\n",
      "Ep= 423, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 424, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 425, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 426, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 427, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 428, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 429, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 430, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 431, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 432, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 433, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 434, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 435, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 436, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 437, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 438, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 439, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 440, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 441, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 442, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 443, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 444, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 445, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 446, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 447, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 448, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 449, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 450, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 451, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 452, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 453, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 454, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 455, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 456, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 457, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 458, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 459, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 460, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 461, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 462, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 463, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 464, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 465, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 466, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 467, score=2160.781 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 468, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 469, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 470, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 471, score=2160.782 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 472, score=2160.783 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 473, score=2160.783 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 474, score=2160.786 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 475, score=2160.791 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 476, score=2160.801 (best=82358.200) pos=-0.02 1.59 0.0\n",
      "\n",
      "Ep= 477, score=2160.824 (best=82358.200) pos=-0.02 1.58 0.0\n",
      "\n",
      "Ep= 478, score=2160.879 (best=82358.200) pos=-0.02 1.57 0.0\n",
      "\n",
      "Ep= 479, score=2161.028 (best=82358.200) pos=-0.02 1.53 0.0\n",
      "\n",
      "Ep= 480, score=2401.494 (best=82358.200) pos=-0.03 1.67 0.0\n",
      "\n",
      "Ep= 481, score=3363.634 (best=82358.200) pos=-0.08 -0.15 0.0\n",
      "\n",
      "Ep= 482, score=79052.698 (best=82358.200) pos=-29.01 -0.55 156.15\n",
      "\n",
      "Ep= 483, score=105037.160 (best=105037.160) pos=0.44 0.0 158.62\n",
      "\n",
      "Ep= 484, score=105037.030 (best=105037.160) pos=-0.63 -0.0 158.62\n",
      "\n",
      "Ep= 485, score=105037.196 (best=105037.196) pos=-0.21 -0.0 158.62\n",
      "\n",
      "Ep= 486, score=105037.738 (best=105037.738) pos=-1.43 0.0 158.61\n",
      "\n",
      "Ep= 487, score=105037.065 (best=105037.738) pos=-0.19 -0.0 158.62\n",
      "\n",
      "Ep= 488, score=105037.004 (best=105037.738) pos=1.48 0.0 158.6\n",
      "\n",
      "Ep= 489, score=105036.561 (best=105037.738) pos=1.95 -0.0 158.6\n",
      "\n",
      "Ep= 490, score=105037.409 (best=105037.738) pos=-1.06 0.0 158.61\n",
      "\n",
      "Ep= 491, score=105036.761 (best=105037.738) pos=0.15 -0.0 158.62\n",
      "\n",
      "Ep= 492, score=105037.269 (best=105037.738) pos=-2.31 -0.0 158.59\n",
      "\n",
      "Ep= 493, score=105036.858 (best=105037.738) pos=0.05 -0.0 158.62\n",
      "\n",
      "Ep= 494, score=105036.556 (best=105037.738) pos=0.91 -0.0 158.61\n",
      "\n",
      "Ep= 495, score=105037.109 (best=105037.738) pos=-1.24 -0.0 158.61\n",
      "\n",
      "Ep= 496, score=105036.911 (best=105037.738) pos=0.36 0.0 158.62\n",
      "\n",
      "Ep= 497, score=105037.045 (best=105037.738) pos=0.46 0.0 158.62\n",
      "\n",
      "Ep= 498, score=105036.727 (best=105037.738) pos=0.89 -0.0 158.62\n",
      "\n",
      "Ep= 499, score=105036.740 (best=105037.738) pos=0.88 -0.0 158.62\n",
      "\n",
      "Ep= 500, score=105037.767 (best=105037.767) pos=-2.04 0.0 158.6\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import sys\n",
    "from agents.agent import Agent\n",
    "from task import Task\n",
    "\n",
    "#<-Number of episodies->#\n",
    "num_episodes = 500\n",
    "\n",
    "#<-Algorithm parameters->#\n",
    "gamma = 0.99 # Discount factor\n",
    "tau = 0.1 # For soft update of target parameters\n",
    "exploration_theta = 0.15 # Noise process -> same direction\n",
    "exploration_sigma = 0.001 # Noise process -> random noise\n",
    "\n",
    "#<-Variable position init->#\n",
    "init_pos = np.array([0., 0., 0., 0., 0., 0.])\n",
    "target_pos = np.array([0., 0., 100.])\n",
    "\n",
    "#<-Learning Agent in the environment to take off->#\n",
    "task = Task(init_pose=init_pos, target_pos=target_pos,runtime=10.)\n",
    "agent = Agent(task, gamma, tau, exploration_theta, exploration_sigma)\n",
    "\n",
    "#<-Save rewards for plotting->#\n",
    "rewards = []\n",
    "\n",
    "for i_episode in range(1, num_episodes+1):\n",
    "    state = agent.reset_episode() # start a new episode\n",
    "    step = 0\n",
    "    while True:\n",
    "        step +=1\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",
    "        if done:\n",
    "            rewards.append(agent.score)\n",
    "            print(\"\\r\\nEp={:4d}, score={:7.3f} (best={:7.3f}) pos={} {} {}\".format(\n",
    "                i_episode,\n",
    "                agent.score,\n",
    "                agent.best_score,\n",
    "                round(task.sim.pose[:3][0],2),\n",
    "                round(task.sim.pose[:3][1],2),\n",
    "                round(task.sim.pose[:3][2],2)))  # [debug]\n",
    "            break\n",
    "    sys.stdout.flush()\n"
   ]
  },
  {
   "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": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3581c3a7b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(rewards)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "runtime = 5.                                     # time limit of the episode\n",
    "init_pose = np.array([0., 0., 0., 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",
    "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",
    "    state = agent.reset_episode() # start a new episode\n",
    "    while True:\n",
    "        rotor_speeds = agent.act(state)\n",
    "        next_state, reward, 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",
    "        state = next_state\n",
    "        if done:\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next code cell visualizes the position of the quadcopter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3583beeba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f35840d1080>"
      ]
     },
     "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": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f35840c5668>"
      ]
     },
     "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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f35841e6358>"
      ]
     },
     "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f358904b748>"
      ]
     },
     "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": [
    "The cell below shows 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": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -3.49467134e-02  -7.69231612e-08   1.58616331e+02   4.56550353e-09\n",
      "   6.28307016e+00   0.00000000e+00]\n",
      "[  2.53681555e-02   3.73727343e-08   1.72330495e+01]\n",
      "[  1.50788525e-09   1.25321625e-03   0.00000000e+00]\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": {
    "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**: The task I have specified in `task.py` is for learning my agent to take off. For this, I defined the initial position in z=0 and target position in z=100. To get that my agent learns to take off, I have designed a reward function that penalizes in function of the distance to target pose, the sum of Euler angles and difference between velocities. These penalties are pondered by a factor to reduce its impact, and the rewards are multiple from a constant so that if the quadcopter is flying it obtains a reward of `reward_constant^2` and an extra bonus (`reward_constant^3`) if quadcopter is flying near to the target pose."
   ]
  },
  {
   "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**: The algorithm that I used is similar that the suggested DDPG algorithm in this lesson because I cheked in the [paper](https://arxiv.org/pdf/1509.02971.pdf) that it could be a very good choice.\n",
    "\n",
    "This algorithm is an Actor-Critic algorithm composed by:\n",
    "- An Actor that is composed of 3 dense hidden layers of size 32 - 64 - 32 with relu activation and a final layer with sigmoid activation, which is scaled. As optimizer is used Adam optimizer.\n",
    "- A critic with one part for the state and one for the action, both using 2 dense hidden layers with 32 and 64 nodes and relu activation function. These parts are joined and then, these are passed with a relu activation layer. Then, the last node is a node that produces the Q-values. As optimizer is used Adam optimizer like actor algorithm.\n",
    "\n",
    "I tried to modify it with different neural network architecture but the performance was not better, so I decided not to change it. So, after trying a lot of combinations of different and hyperparameters, I decided to use the original neural network architecture and looking for the hyperparameter to obtain the best performance. \n",
    "\n",
    "That hyperparameters have been:\n",
    "- gamma = 0.99\n",
    "- tau = 0.1\n",
    "- theta = 0.15 \n",
    "- sigma = 0.001 "
   ]
  },
  {
   "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**: It was a very hard task because it is very difficult to define a good model for doing that task with good behaviour, and especially I think that build a good reward function is very difficult.\n",
    "\n",
    "I was a gradual learning curve by moments, It seemed that the agent was learning correctly but suddenly the system changes the behaviour drastically, so finally, I would say that it was a combination of a gradual learning curve and aha moment.\n",
    "\n",
    "The final performance of the agent is good for taking off, but it always moves above the target position. So, definitely, for taking off task the agent behaves correctly. The mean over the last 10 episodes was 105037.01385099419."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105037.01385099419"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(rewards[-11:])"
   ]
  },
  {
   "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**: The hardest part of the project was specifying the task, more concretely, for finding a good reward function. I tried a lot of different rewards functions until finding a good choice. I think that the key was the simplicity of the reward function architecture.\n",
    "\n",
    "If we look the rotor revolutions/seconds plot, we can see that rotor 3 and 4 are moving with a very high angular velocity but 1 and 2 rotors is moving with an angular velocity near to zero. And if we see the angular velocity of the quadcopter measure in Euler angles, we can see that the quadcopter take off with a big pitch both positive and negative."
   ]
  }
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