notebooks/nb2_enemy_noise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enemy Noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "import pelita\n",
    "from pelita.utils import setup_test_game\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we define some helper functions. They are described in detail in `nb1_matplotlib_and_numpy.ipynb`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "def draw_coords(coords, ax, params={}):\n",
    "    \"\"\"\n",
    "    Takes a list of coordinates and draws them as scatter elements onto a pyplot ax object\n",
    "\n",
    "    Input:\n",
    "    - coords: list of tuples of x,y coordinates\n",
    "    - ax: a pyplot ax object\n",
    "    - params: parameters to be passed to the scatter function (e.g. shape, size, color, etc.)\n",
    "    Returns:\n",
    "    - ax: a pyplot ax object\n",
    "    \"\"\"\n",
    "    # coords is an iterable of (x,y) tuples: [(x1, y1), (x2, y2), ..., (xn, yn)]\n",
    "    # the zip(*) trick transposes this iterable:\n",
    "    # ([x1, x2, ..., xn], [y1, y2, ..., yn])\n",
    "    coords_x, coords_y = zip(*coords)\n",
    "    ax.scatter(coords_x, coords_y, **params)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "def draw_game(bot, ax):\n",
    "    \"\"\"\n",
    "    Draws game state as scatter elements given a bot object.\n",
    "\n",
    "    Input:\n",
    "    - bot : pelita bot object\n",
    "    - ax : a pyplot axis object\n",
    "    Returns:\n",
    "    -ax : a pyplot axis object\n",
    "    \"\"\"\n",
    "    # Depending which team we are, we need to color the board appropriately\n",
    "    if bot.is_blue:\n",
    "        col_home_food = \"blue\"\n",
    "        col_home_b1 = \"green\"\n",
    "        col_home_b2 = \"darkgreen\"\n",
    "        col_enemy_food = \"red\"\n",
    "        col_enemy_b1 = \"orange\"\n",
    "        col_enemy_b2 = \"darkred\"\n",
    "    else:\n",
    "        col_home_food = \"red\"\n",
    "        col_home_b1 = \"orange\"\n",
    "        col_home_b2 = \"darkred\"\n",
    "        col_enemy_food = \"blue\"\n",
    "        col_enemy_b1 = \"green\"\n",
    "        col_enemy_b2 = \"darkgreen\"\n",
    "\n",
    "    # Then we call the draw_coords function for each type of element we want to draw.\n",
    "    # Each can be customized with whichever color, shape and size you want.\n",
    "    ax = draw_coords([bot.position], ax, {\"c\": col_home_b1, \"marker\": \"*\", \"s\": 400})\n",
    "    ax = draw_coords(bot.walls, ax, {\"c\": \"black\", \"marker\": \"s\", \"s\": 250})\n",
    "    ax = draw_coords([bot.other.position], ax, {\"c\": col_home_b2, \"marker\": \"*\", \"s\": 400})\n",
    "    ax = draw_coords(bot.food, ax, {\"c\": col_home_food, \"s\": 100})\n",
    "    ax = draw_coords(bot.enemy[0].food, ax, {\"c\": col_enemy_food, \"s\": 100})\n",
    "    ax = draw_coords([bot.enemy[0].position], ax, {\"c\": col_enemy_b1, \"marker\": \"*\", \"s\": 400})\n",
    "    ax = draw_coords([bot.enemy[1].position], ax, {\"c\": col_enemy_b2, \"marker\": \"*\", \"s\": 400})\n",
    "    ax.axvline(np.sum(ax.get_xlim()) / 2, c=\"black\")\n",
    "    # This is to get the coordinate system to have it's origin in the top left\n",
    "    yd, yu = ax.get_ylim()\n",
    "    if yd < yu:\n",
    "        ax.invert_yaxis()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enemy Noise\n",
    "When they are more than 5 fields away, the position given by `bot.enemy.position` is noisy. As the documentation says \n",
    "\n",
    "> your bot has a sight-radius of 5 squares. This means that when an enemy bot is located more than 5 squares away from your bot, `bot.enemy[0].position` will not be exact and `bot.enemy[0].is_noisy` will be True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layout_str=\"\"\"\n",
    "################################\n",
    "#   ...    #. .         . #    #\n",
    "# #####.## ..# #######.##   y  #\n",
    "#.  a .x.  # # ##    .  # #    #\n",
    "#  ###### ## #        #.#.     #\n",
    "#  #       #           .# #.## #\n",
    "#.     #  ..     ..###### # .# #\n",
    "##..######  #####  ..    .#.   #\n",
    "#   .#.    ..  #####  ######..##\n",
    "# #. # ######..     ..  #     .#\n",
    "# ##.# #.           #       #  #\n",
    "#     .#.#        # ## ######  #\n",
    "#    # #  .    ## # #  . .    .#\n",
    "#      ##.####### #.. ##.##### #\n",
    "#b   # .         . .#    ...   #\n",
    "################################\n",
    "\"\"\"\n",
    "bot = setup_test_game(layout=layout_str, is_blue=True, is_noisy={\"y\": True})\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "ax = draw_game(bot, ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the noisiness of the bots like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enemy 0 is noisy:  False\n",
      "enemy 1 is noisy:  True\n"
     ]
    }
   ],
   "source": [
    "print(\"enemy 0 is noisy: \", bot.enemy[0].is_noisy)\n",
    "print(\"enemy 1 is noisy: \", bot.enemy[1].is_noisy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find all the fields the enemy could be on. Any field that is 5 horizontal or vertical steps away is potentially the true enemy position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "maze_width, maze_height = bot.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "This gives us a an array where each field holds the number of horizontal or \n",
    "vertical fields it is away from enemy\\[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "yy, xx = np.mgrid[0:maze_height, 0:maze_width]\n",
    "fields_away = abs(xx - bot.enemy[1].position[0]) + abs(yy - bot.enemy[1].position[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "One approach is making each cell hold the probability of an enemy being in this location. For this we need to normalize our array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "enemy_prob = fields_away.copy()\n",
    "# First we transform it into a binary array (enemy could be here vs. not)\n",
    "enemy_prob[enemy_prob<=5] = 1 \n",
    "enemy_prob[enemy_prob>5] = 0\n",
    "# Then just turn into a probability density\n",
    "enemy_prob = enemy_prob/np.sum(enemy_prob)\n",
    "# For visualization purposes we add a small eps value to the zeros, \n",
    "# so we can display log-values. Otherwise we can't see small activations\n",
    "enemy_prob[enemy_prob==0] = np.finfo(float).eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11956d9d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(enemy_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "For enemy\\[0] we know exactly where it is, so we can just add that position with a probability of 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "enemy_prob[bot.enemy[0].position[1], bot.enemy[0].position[0]] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the Probabilities using pyplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZJ8cee2y++tWvuicoFNCyZcvykY985B3vz58/P4sXL065XM6iRYvyzW9+M+vXr8+xxx6bO+64IwceeGAV0gJv190YvvPOOzNv3rw8/vjjWb9+fZqamnLSSSflK1/5SsaOHVuFtMD2SqVSl+9/61vfytlnn50k2bRpUy677LLcfffdef311zNnzpzccccdbnMBBbCzMfzcc8/lM5/5TFatWpX29vZMmDAhp556ar70pS+loaGhj9MC2zvnnHPy4IMP5qWXXkpjY2OmT5+eK6+8MieeeGIS82+lDLhmBgAAAAAAUFsGzDMzAAAAAACA2qSZAQAAAAAAFJpmBgAAAAAAUGiaGQAAAAAAQKFpZgAAAAAAAIWmmQEAAAAAABSaZgYAAAAAAFBomhkAAAAAAEChaWYAAAAAAACFppkBAAAAAAAUmmYGAAAAAABQaP8P/Kluq8sAh5MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "ax.imshow(enemy_prob, norm=matplotlib.colors.LogNorm(), cmap=\"Reds\")\n",
    "ax = draw_game(bot, ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the enemy is closer but just out of sight (e.g. 6 fields away), the noise makes it possible for the reported position to be right next to you. This example is shown below using the same procedure as above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layout_str=\"\"\"\n",
    "################################\n",
    "#   ...    #. .         . #    #\n",
    "# #####.## ..# #######.##      #\n",
    "#.  a .y.  # # ##    .  # #    #\n",
    "#  ###### ## #        #.#.     #\n",
    "#  #       #           .# #.## #\n",
    "#.     #  ..     ..###### # .# #\n",
    "##..######  #####  ..    .#.   #\n",
    "#   .#.    ..  #####  ######..##\n",
    "# #. # ######..     ..  #     .#\n",
    "# ##.# #.           #       #  #\n",
    "#    x.#.#        # ## ######  #\n",
    "#    # #  .    ## # #  . .    .#\n",
    "#      ##.####### #.. ##.##### #\n",
    "#b   # .         . .#    ...   #\n",
    "################################\n",
    "\"\"\"\n",
    "bot = pelita.utils.setup_test_game(layout=layout_str, is_blue=True, is_noisy={\"y\": True})\n",
    "maze_width, maze_height = bot.shape\n",
    "yy, xx = np.mgrid[0:maze_height, 0:maze_width]\n",
    "fields_away = abs(xx - bot.enemy[1].position[0]) + abs(yy - bot.enemy[1].position[1])\n",
    "enemy_prob = fields_away.copy()\n",
    "enemy_prob[enemy_prob<=5] = 1 \n",
    "enemy_prob[enemy_prob>5] = 0\n",
    "enemy_prob = enemy_prob/np.sum(enemy_prob)\n",
    "enemy_prob[enemy_prob==0] = np.finfo(float).eps\n",
    "\n",
    "enemy_prob[bot.enemy[0].position[1], bot.enemy[0].position[0]] = 1\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "ax.imshow(enemy_prob, norm=matplotlib.colors.LogNorm(), cmap=\"Reds\")\n",
    "ax = draw_game(bot, ax)"
   ]
  }
 ],
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