fig-motor-hist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Generate a figure showing motor velocity for different policies.\n",
    "\n",
    "Nb: To generate this figure, you require the raw data files (which are quite large) in the benchmarking folder.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import scipy.io as sio\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Retrieve data\n",
    "# mat_random = sio.loadmat(os.path.join('benchmarks', 'observations3_random_normalized.mat'))\n",
    "# data_random = mat_random['observations']\n",
    "\n",
    "mat_learning = sio.loadmat(os.path.join('benchmarks', 'observations4_move_in_direction_normalized.mat'))\n",
    "data_learning = mat_learning['observations']\n",
    "\n",
    "mat_failed = sio.loadmat(os.path.join('benchmarks', 'observations6_move_in_direction_failed_normalized.mat'))\n",
    "data_failed = mat_failed['observations']\n",
    "\n",
    "# Keep the same number of points\n",
    "#N_random = len(data_random)\n",
    "N_learning = len(data_learning)\n",
    "N_failed = len(data_failed)\n",
    "\n",
    "N_min = min([\n",
    "    #N_random,\n",
    "    N_learning,\n",
    "    N_failed\n",
    "])\n",
    "#data_random = data_random[:N_min]\n",
    "data_learning = data_learning[:N_min]\n",
    "data_failed = data_failed[:N_min]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sns.set()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2700x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(\n",
    "    figsize=(9, 6),\n",
    "    dpi=300\n",
    ")\n",
    "\n",
    "kwargs = dict(\n",
    "    bins=np.linspace(-1.0, 1.0, 70),\n",
    "    kde=False,\n",
    "    hist=True\n",
    ")\n",
    "\n",
    "# sns.distplot(data_random.transpose()[14],\n",
    "#     label=\"Random Policy\",\n",
    "#     **kwargs,\n",
    "#     color='b',\n",
    "# )\n",
    "\n",
    "sns.distplot(\n",
    "    data_learning.transpose()[14],\n",
    "    label=\"DDPG — Successful\",\n",
    "    **kwargs,\n",
    "    color='C2',\n",
    ")\n",
    "\n",
    "sns.distplot(\n",
    "    data_failed.transpose()[14],\n",
    "    label=\"DDPG — Failed to learn the task\",\n",
    "    **kwargs,\n",
    "    color='C1',\n",
    ")\n",
    "\n",
    "plt.xlabel(\"Motor Velocity\")\n",
    "plt.ylabel(\"Number of Observations\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\n",
    "    os.path.join(\"manuscript\", \"figures\", \"fig-motor-hist.pdf\"),\n",
    "    dpi=300,\n",
    "    bbox_inches='tight',\n",
    "    pad_inches=0,\n",
    ")\n",
    "plt.show()\n",
    "plt.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The green data was collected by recording the motor velocity of a DDPG Jitterbug agent successfully learning to perform the move_in_direction task.\n",
    "\n",
    "The orange data was collected by recording the motor velocity of a DDPG Jitterbug agent failing to learn to perform the move_in_direction task.\n"
   ]
  }
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