Week_9/Day_3/Find_Best_Path_for_Jerry_Question.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "points_list = [(0,1), (0,3), (1,2), (1,4), (1,5), (1,8), (2,7), (5,6), (5,9), (7,9), (4,7), (3,8), (3,4), (8,9)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mouse = [3,5]\n",
    "cat = [4,6,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cheese = 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PHz/OhQsXmDBhQqnbaNmyJRcuXECv15swssrPHOWFxw0aNIiNGzcyZMgQduzYYdZrCUJlZHFJd9asWXz00UdYW5d+/VtHR0e8vb0JDQ01YWSVnznLC4/q3r07f/zxB+PHj8+zi7AgCBaWdP/++29CQ0MZN25cmdsSJYYnlUdP96F27drx559/8v7777Ns2bJyuaYgVAYWlXRnzZrFjBkzTLJYjUi6Tyqvnu5DzZo1IyQkhC+//JL58+dT2DKiglBdWEzSPXz4MDdv3uSVV14xSXuBgYHiLvpjzH0jLT9+fn6EhITwyy+/MHXqVJF4hWrPYpLurFmzmDlzJiqVyiTtBQQEcP78eQwGg0naqwrKs7zwqBo1anDo0CH+/vtvJkyYIG5wCtWaRSTdgwcPEhERwejRo03WppOTE56enmKw/iPKu7zwKBcXF/bv38+9e/cYNmwYWVlZFRKHIFS0Ck+6kiQxc+ZMZs2ahVJp2jXVRV03r4rq6T5ka2vLH3/8gUwmo1+/fqSlpVVYLIJQUSo86e7fv5+4uDhGjBhh8rZF0s2rImq6j7O2tiYoKIhatWrRo0cPEhISKjQeQShvFZp0JUli1qxZzJo1C4VCYfL2W7VqJW6mPaIiywuPUiqVrFy5kmeeeYYuXboQHR1d0SEJQrmp0KS7Z88ekpOTGTp0qFnab9WqFefOnRNby/y/ii4vPEomk7Fw4UJGjBjBM888w61btyo6JEEoFxWWdB/WcmfPnm2WXi7k3Lxxc3Pj+vXrZmm/MtHpdOh0ulKvZ2EOMpmMDz/8kKlTp9K5c2cuXbpU0SEJgtlVWNLdsWMHWq2WwYMHm/U6oq6b4+EuwJa4i++bb77JwoUL6d69O8eOHavocATBrCok6T6s5c6ZMwe53LwhiLpuDku4iVaYESNGsGrVKvr378/+/fsrOhxBMJsKSbrbtm1DkiQGDhxo9muJnm4OS7mJVpjnn3+ezZs3M3LkSDZv3lzR4QiCWZh2YGwxGI1GZs2axdy5c8vlo25gYCBnz57FaDSavVdtySzpJlphOnXqxJ49e+jbty9JSUmMHz++okMSBJMq96S7ZcsWlEol/fv3L5frubq64uzsTFhYGA0bNiyXa1qiytDTfSggIIDg4GB69epFUlIS7777bkWHJAgmU65dv/Lu5T4k6rqVp6f7UMOGDQkJCWHFihV8/PHHYqEcocowWU83MT2bwzfiOHsvictRyWTrjdhaK2nh40RLXyc6N3Bn25bfsLOzo0+fPqa6bLE8rOsOHz68XK9rSSz9Rlp+fH19CQkJoXfv3iQmJrJkyZJqXSISqoYyJ93b8el8sfca+67GoFLIyMgy8Gif5NitB9hYKTAYIfvaOT6fMafchy0FBgby+eefl+s1LU1lKi88yt3dnYMHD9K/f3/GjBnD6tWrTbYSnSBUhFJ3G4xGiRWHb9JnyWF2XYomW28k/bGEC2CUIC3LQKbOgKHO08w+BTsvlu+0z4flher8EbWylRce5eDgwO7du0lJSWHQoEFkZmZWdEiCUGqlSroGo8TbQWdZvP8GWp0RY3FzmVxJapae9349z9d/lt8sMQ8PD+zt7bl582a5XdPSVMbywqM0Gg2bN2/GycmJ3r17k5ycXNEhCUKplCrpfrjlIgdCY8nUlW6B8Eydge+Cb7H679ulOr80qvtOEpW1vPAolUrFmjVraN68Od26dSMuLq7Q47P1Ri5FJvPLqXusPJLz+/bn1RhiU7TlFLEgPKnENd2D12LZdj6STF3ZFpHJ1Bn4bHconRq4U8/drkxtFcfDm2nmWlzH0lXm8sKj5HI5S5cuZdasWXTq1Im9e/dSq1atPMdcikxm+eGb7Lkcg0opQ5JAZzAil8mwUsjJMhjxtLfmjc51GRTgg611uY+cFKqxEvV0s/VG3vvlfJkT7qPtTfnlnEnaKkp1n5lWFXq6D8lkMubOncsbb7xBp06dcncHSdXqePeXcwxZ/jc7LkaTbci5z5CRbUBnkMjSG0nN0pOtN3IvMZNPd4bS6fODhNwovMcsCKZU7Lf4rl270qxLP7JoZrKLGyW4EZPG1egUmtQwb0J49GaaJS76Ym5Vpaf7qClTpuDk5MSzzz7LyqCtzD6SQnKmjix98ToFGToDGToDr689zdgOdXj/uUbV8ndDKF8l6umG3IgjPbv4dVzJ+OSxj/8s22BkzdHwkoRRKl5eXmg0GsLDzX8tS1TZb6QVZNy4ccz/8hve3HKLuNSsYifcR2XqDKz+O5yFe8V+eoL5FbunK0kQnazFBsgIO0HS4bXok2OxcvPF5bnJWHn4ARCxbDz2rfqSfvkQuoQIak5YRtTy13Hp8w7Jf21E6eCB3FqD2i8Qh9b9MRgl/r75AH9/f+bMmcOgQYPM9VxzSwx+fn5mu4alqkrlhUcZjRKb49xQqFMoS9ErU2dg1V/hdKznRsf6biaLTxAeV+yerlZnQCGXkX3/Jg92fo1r77fw/dcG7Fr2Ifa3eUh6Xe6x6VeC8XhpFr7//hmZLGeB8qy7F6k54Vs8hs3Ftll30i8fzD3+1rXLREZG8vzzz5vwqT2pOtd1q2J5AWDNsXBuxqWXKeE+pNUZeDvoLBnZYot4wXyKnXR1RiNyGaSe3419y95Y12yETK7Arnl3ZEoVWVGhucc6BPZH6eCOXGWd+zPHTqOQW6mRq6yxadAOfUIkuoRIANIvH+SFF4dgZWVlwqf2pFatWlXbpFsVe7p6g5Gv9t8odOji/fX/IfX8nmK3qdUZ2Hou0hThCUK+SjxWRp8cS/rFA6Sc3v7PDw16DGn/7OqqcHB/8kL2/3xkkymtsGnSifTLh3B8ZgQpl4MZ9on51099OFa3ut1My8rKQpIkrK2tiz7YzK5evcrkyZM5ffo07u7uLFy4sNQlpQPXYsnWZZEUspH0K4cwpCUg1ziiru2PU8cRKJ08S9xmRraB5cG3GNm2dqliEoSiFDvpWinkGCVQOrij7jAUxw7DCj44v4T22M/smncn/o8vsfZ5CrnSmu5dnil20KVVs2ZNVCoVd+/epXbt6vOfylJuoun1el544QUmTpzIvn37CA4Opn///pw9e7ZUy27uvxpDeNB8DKkPcBswDSvPekjZWtIvHyTzznnsnXqVKs6o5EwS0rNxsTXvJy+heip2eUGtUiBJEnYtniP17C6yoq4hSRLGbC0ZYScxZmWU6MLW3k1AJiPxwA/4tO2FUlE+q0dVx7qupZQWQkNDiYqKYsqUKSgUCrp160bHjh1Zu3Ztqdrbt28/2vBzuA+egXWNhsjkCuRqW+wD+2Hf4p+Ea0iO5f7aadz98iVigj7GkPHPFOKsyFDur53K3cXDiPrhLbR3LqBWKrgYmUxycjKvvvoqNWrUwNvbmxkzZmAw5JQywsLC6NKlC46Ojri5uTFs2D+dkNDQUHr27ImLiwuNGjXil19+KeUrJlRFJcp0vi62WNdogGuft0nY+x33vhpO1PLXSL9Yuj2tbJt1QxcXjqd3HU6cOFEuC9JUx6RryTfRJEkq9S7A9y4dx6pGQ5T5lLMelX4lGNfn/43P2+uQDDpSTmwBQJ8aT+yvc3DsMAzff2/EudurxG35L5mpidxLzGDs2LEolUrCwsI4e/Yse/fuZeXKlQB8/PHH9OrVi8TERCIiInj77bdzrpWeTs+ePRk5ciSxsbEEBQUxadIkrly5UqrnKFQ9xU66KSkp9GvTEFsrBZq6gdQYu5haU37G5+21uA/6ALm1DQA+k1ahqdMy9zylkye1/7MdmfzJbdaVDu6ofZ6ig481I0eOpHHjxsyfP9+sY2mr4800SykvNGrUCA8PDxYuXIhOp2Pv3r0EBweTkVGyT0kPGTJSUdg5F3mcbfMeqFy8kaussW3SieyYWwCkXz6Epl5rNPXaIJPJ0fgFYFWjPmk3TpIQF8vOnTv56quvsLW1xcPDgylTphAUFATkrANx584doqKiUKvVPPNMTnls+/bt1KlTh3HjxqFUKgkICGDw4MH8+uuvpXqOQtVTrKR7+fJlrl69yph+XXHUmGYtU6NOS9rZnQT0GsKiOf/hxo0brF69mujoaNq0aUOnTp1YsWIFiYmJJrneQ4/eTKsuHm6/XtFUKhVbt25lx44deHl5sWjRIoYOHYqPj0++xzdt2hQ7Ozvs7OwICQl54nFrO0cMaUX/fjyamGVKayRdzoI3+uRY0kOPcHfxsNyvrIgrSOmJaBPvo9PpqFGjBk5OTjg5OfHGG28QGxsLwOeff44kSbRt25amTZuyatUqAO7cucPx48dzz3FycmL9+vXcv3+/xK+XUDUVeSNt+vTprFu3jgULFlDXrw5LRzgy6odjaMuw/kLmrdPEbfkUW78Afvl8GpAzn759+/a0b9+exYsXs2vXLtauXcu0adPo2bMnY8aMoU+fPmUeVubt7Y1MJiMiIgJfX98ytVVZWEpPF8Df35/g4ODcv3fo0IFXXnkl32MvX75caFvN2nTkwNEt6FPiUTqUfEKD0sENu2bP4trnnTw/t7VS0N6/HtbW1sTHx6NUPvnfxMvLi++//x6AI0eO0KNHDzp37oyvry9dunRh3759JY5HqB6K7OkuWLCAyMhI3nkn5xczsLYzrz1TF43qyXJBcWnqBtLwP1sI+nUTPq5PrjBmZWXFCy+8wG+//UZ4eDjPPfccixYtombNmkyaNImjR4+Wuqcqk8mqXV3XUm6kAVy4cAGtVktGRgZffPEF0dHRjB07tlRtDRnQFxu/AOI2f0LW/TAkowFjVgapZ3eSdn5vkefbNn2WjLATZN46jWQ0IOmz0d65QGZSHO2b1aNXr1689957pKSkYDQauXnzZu4bxq+//kpERAQAzs7OyGQy5HI5/fr14/r166xduxadTodOp+PkyZNcvXq1VM9RqHpKNWTg3Z4NGdmuVqkTr1olZ86AZjzvX7PIY52dnXnttdc4fPgwJ0+epGbNmowdO5YGDRowe/ZswsLCSnz96lbXtaQbaWvXrqVGjRp4eHjw559/sm/fvlKPHx7Y0puaQz5EU6818VsXcG/xUKJ+mEx2dBjqR+4rFETp4I7H4I9JPvorEUtGEfHNWFJObKZXY3eUCjlr1qwhOzubp556CmdnZ4YMGUJ0dM6uJydPnqRdu3bY2dkxYMAAvv76a+rWrYu9vT179+4lKCiImjVr4uXlxfTp08nKyirVcxSqHllhPcbWrVtLp06dKvDxHRejmb7pAtl6I9mGossNapUcR42K/41oRZs6LqUKGHLueJ86dYq1a9cSFBRE/fr1GTNmDEOHDsXV1bXI87ds2cLKlSvZsWNHqWOoTGbOnIlCoWDWrFkVHYrJjVt9gsM34jEUe/uSwqlVcn6f9AyNvCzjTUqonGQy2WlJklrn91iZBsc+37wGwVO78uozftirldhZK7F6bLytRqXA1kqBu701U3o05NB7z5Yp4UJOiaBNmzYsWbKEyMhIPvzwQ4KDg6lbty4DBw5k06ZNhfYsHpYXqsvNNEu5kWYO815o9sTvXKnps+la20YkXMGsytTTfVS23sjZe4lcjEzmSlQKWp0Be7WK5j6O+Hs70qymI3K5eafeJicns2nTJtauXcuFCxcYMmQIo0ePpmPHjnm27pYkCQ8PD86dO4e3t7dZY7IEr776Ku3bt2fChAkVHYpZBJ24y5ztV0q9fRSAQibDTqEnauWbfDp3NhMmTKhWU8UF0yqsp2uyfUqslHLa+bnSzq/oj/fm4ujoyPjx4xk/fjx3795lw4YNTJw4kYyMDEaPHs2YMWNo2LAhMpkst65bHZKuJd1IM4fhbWsRlazl+5BbpUq8Cjk426jYOqkraaP+ZOjQoRw4cIDly5dX6ddNqBjlM/e2AtSqVYv//Oc/XLp0iU2bNpGWlkbnzp1p164dS5cupUmTJtXmZpol3Ugzl3d7NmR2/6fQqORIhuIvzahRKWjp48yOtzvh42xD48aNOX78OA4ODgQGBnL27FkzRi1UR1U26T70sFe7ePFiIiIimDNnDseOHeP777/nu+++45dffiEzM7OiwzQrSxqna07D2tSiL2dwyb6PlVKOtbLgX29bKwVudlbM7PcUv01sj6eDOvcxjUbD8uXLmTdvHr169eKbb76pNvV/wfxMVtOtbC5dukSnTp1o3bo1p0+fZtCgQYwZM4bOnTvnqf9WBc2bN2f9+vX4+/tXdChmFRUVRfPmzTlz5gwaZ09+Px/JX2EPuHo/hYwsA3K5jBqOatrUcaF7Yw86N3Av8j7DjRs3GDZsGHXr1mXlypU4OTmVz5MRKrXCarrVNulKkoSbmxuXLl3CaDSyYcMG1q5dS3JyMqNGjWL06NE89dRTFR2mSdSuXZvg4GDq1KlT0aGY1YQJE3B1dWXBggUmbVer1TJt2jR27NhBUFAQbdu2NWn7QtVjtiFjldnjN9OmTZvGhQsX+OOPP9DpdPTs2ZPAwEC++uorYmJiKjrcMqkO5YWLFy/yxx9/8MEHH5i8bbVazdKlS/niiy/o168fixcvFuUGodSqbdKFfxa/eZS/vz8LFy7k7t27fPbZZ5w9e5ZGjRrRp08fNmzYUOoVsSqKJElVepzuQ++//z4fffSRWT/+v/jiixw/fpygoCAGDBjAgwcPzHYtoeqq9km3oBEMCoWCnj178tNPPxEZGcno0aNZs2YN3t7ejB07lj///DN3QWtLlpmZiUqlQqUyzepwlmjfvn2EhYUxceJEs1/Lz8+PkJAQGjVqRKtWrfjrr7/Mfk2hipEkqcCvwMBAqSoLCwuTvL29S3ROdHS09OWXX0oBAQGSt7e3NG3aNOnChQtmirDs7t+/L7m7u1d0GGaj1+slf39/6bfffiv3a2/btk3y8PCQ/vvf/0oGg6Hcry9YLuCUVEBerdY93bp165Kenl6imq2XlxdTpkzhzJkz7NmzB7lcTt++fWnZsiWLFi0iKirKjBGXXFWv565duxY7OztefPHFcr92//79OXXqFNu3b6dv3765a+0KQmGqddJ9eDPt8bpucTVt2pTPPvuMO3fusHjxYi5fvkzTpk3p1asXa9euJS0tzcQRl1xVTroZGRnMmDGDRYsWVdiUXV9fXw4ePEirVq1o1apVnrWCBSE/1Trpgmn2TJPL5Tz77LOsWrWKqKgoXn31VX7++Wd8fHwYPXo0e/bsQa8v/iwpU5AkidgULWH3k9G41jTZKlyWZPHixXTo0IGnn366QuNQqVR8+umn/PDDDwwfPpy5c+dWinq/UDGq7Tjdh4KCgvj555/ZsmWLydt+uDHh2rVriYyMZMSIEYwePZqWLVuapWem1RnYfiGaDSfuEHo/FYNRQjIa0On1KFVW+LnZMrClN8Pb1Kr024vHxMTQtGlTjh8/Tr169So6nFxRUVGMGjUKuVzO+vXr8fLyquiQhAogJkcU4saNG3Tv3p27d++a9TqhoaGsW7eOdevWYWdnx5gxYxg1alSB+4OVhCRJBJ24x/ydOTvOpmcX3MtSK+UYgVFtazG9d2PUZdgBpCJNmjQJa2trFi9eXNGhPMFgMDB37ly+//571qxZQ48ePSo6JKGciaRbCKPRiLOzM2FhYbi7F76Vt6mud+TIEdatW8emTZto2bIlo0ePZvDgwaWqvSZn6nhtzUkuRqaUaIUttUqOi40Va8a3pb5H5RrDGxoaSqdOnQgNDS3WovUV5cCBA4wZM4Zx48Yxe/bsfPdaE6omMSOtEHK5vEw300pzvc6dO7NixQoiIyN588032bp1K76+vowYMYKdO3ei0+mK1VaKVsegb//i7L2kEi9pqNUZiU7WMujbv7kRk1qap1Jhpk+fzvTp0y064QJ069aNM2fOcPz4cbp165a7p5pQvVX7pKs3GKkb2Jn1R2+zYHco83dcYemBG/x5NYa4VPPua6VWqxkyZAi///47N2/epFOnTsybNw8fHx/+9a9/cerUqQKnm0qSxMR1p4lIyERnKN1NMglI0+oZsfIYaVnle6OvtA4dOsSFCxd46623KjqUYvH09GTPnj0899xztG7dmp07d1Z0SEIFq7blhQdpWaw+Gs6ao3fIzMpGr9djlOfM2lLIQGOlJNtgpLm3I292qUf3xh7lNizpxo0bufVfKyur3Ppv7dq1c4/ZdCaCj3+/REYh9dvislbKGdCiJguHtChzW+ZkNBpp27YtU6dOZfjw4RUdTomFhIQwcuRIRowYwSeffFKsWYJ6g5HQ+6lcjEzmZlwa2XojDhoVTWs64O/jhLeTphwiF0pK1HQfIUkS285H8eHWi+gNEln6ojfUtLFS0Nzbka+HBeDlqC7yeFORJImjR4+ydu1afv31V5o2bcqYMWN4YdCL9PjmFMmZxStDFIe1Us6ef3Wmjputydo0tQ0bNvDVV19x/PjxSruVTnx8PC+//DKJiYkEBQXleSN9VEJ6Nqv/vs1PR++gNxoxGiUydTm/qzLA1lqBziBR182WN7vWp1/zGmbfDksoPpF0/5/RKPHh1ov8fi6qxDVQpVyGWqVg/avtaOHrZJ4AC5GVlcWuXbtYu3YtwbeSceg1GaO84GFfKaf/IP3in2THhWPbpAtu/aYU2r5KLmPU07WZ3b+pqUM3Ca1WS+PGjVmzZg2dO3eu6HDKxGg0smjRIhYuXMiKFSsYOHBgnscf7rKtMxiL3Snwc7PlmxGtLPpNszoRSff/fbT1IpvPRJZpA0NbKwWb3+xYoTvGjv7+L47cSir0mIxrf4NMRubtM0i67CKTLuTsE3b2414mitK0Fi5cyF9//cXWrVsrOhSTOXr0KCNGjGDgwIEsWLAAKysrZv1xmV9PRZT4d1QuA7VKwcqXW9OhnpuZIhaKS4xeAPZfjWHzmZL/Mj8uPdvAa2tPkV2MHoi5hMYWvbykTaMO2DRsj1xT/GFoaVl6HqSZ9+ZhacTHx/P555+bfHHyita+fXvOnDlDeHg4HTt2ZOqGo6VKuABGCTKyDbz60ynO3E00Q7SCqVSLpJuWpee9X8/l1sTKKi41iyV/3jBJWyWl1RlISM82S9tqlYIbsRW/XsTj5s2bx7Bhw2jUqFFFh2JyLi4ubNmyhc5DX+e3s/fL3CnI1Bl4fe0p0ivJaJTqqEqN1r5x4wbNmzdnyJAhrFu3Lvfnm89EkK0v/rAqY1YGSSHryLh+FGNmKgpbJzT12+LYYRgKG0cydQZW/X2bt7rVf2JG1+rVq1m5ciVHjhwx2fN6VJbeiEIuw1jKYWKFtp2VxZY/dnDbQ4W9vT12dnbY2dnl+d7Ozg6Fovxmsd24cYP169dz9erVcrtmecvSGzmU7YdMZZo301Stnv/uusr8gc1N0p5gWlUq6U6ePJk2bdo88fMVIbeK3YOQDDpiNn6EXG2Lx9A5qFx9MGakkHpuF9nR19HUy2lfBuy8GM2Lrco+jRdybhTFxsYSGxtLTExMwX8+SMR69DfI5KZPfEajkRuhV7hzNJy0tDRSU1Pz/JmWlkZ6ejrW1tZPJOOivi/scRsbmwJHI3zwwQe899575TJbsDS6du3KsWPHcmebeXt7c+3atRK1seNiNFk6A9o7F4jZ+CGaBk/jMXhG7uPZMbeI/vEdrH2b4TXqsyLby9Ib+fV0BNOea4yjpuouXl9ZVZmkGxQUhJOTEx06dCAsLCz35wnp2cSkaIvdTtqlA+hT4vAe+Slyq5wxkApbJ5w6jsg9Jvnor0Se38Pwz1Oo71ebTz75hEGDBnH16lUmTpyITqfL7RGePHmy8CT6/39mZmbi4eGBp6dnnj+9vb0JCAjI8/O+Ky+SmGG64WIPyZXWfPf5bDzsCx4WZzQayczMfCIZF/T9/fv3Cz0mNTUVrVaLra3tE8lYp9Nx/vx57Ozs+Pe//13sJG5nZ4e1tXW5DSv73//+x4QJE0p9/vLDN3PXy5DbOJIVFYohMwXF/9fj0y79idLFu0RtymUyNp+JYFxHv1LHJZhHlUi6KSkpzJw5kwMHDrBy5co8j12MTEatVKAzFK/GpQ0/h6Zuq9yEmx+lcw08Ry3A1dkB//BNDBs2jEGDBpGWloaXlxfR0dHodDrkcjnPP//8E8m0WbNmTyRXJyenYieJZjXvEhIWX+gxktEAD78kI5I+G+SKQnvIapW80IQLOdOYbW1tsbW1xdPTs1jxFsVgMJCenp4nKaekpDBp0iReeeUVAgICcn/+4MED7ty5U2BP/OH3RqMxT0mkLD3xh9+bY8ujjGw9t+LSc/8uUyjR1GtDxpXD2Af2QzIayLgagl3LPmjvnM89ThtxlcT9K9AlRqJy9sa5x+uofZoAELFsPK5932F/LSfGdfRj9uzZhIWFsW7dOrRaLRMmTGDXrl0YDAYaNGjA9u3b8fT0JDk5mXfffZedO3cil8sZN24cc+bMKddyUnVQJZLuxx9/zKuvvprvil33kzPRl2AtWWNmKlZe9Qs9xrbxMwCkZutxc3PDzc0NDw8PxowZw8mTJ9m5cyeHDx9GozHPbKEhgT6cuZtY6GpiyX8FkfzXxty/p18+iGPHETh1GpXv8Qo59G1ew+SxFodCocDBwSHPgj+//fYbarWa//3vf8jlJb/fm52dnZuIi+qVR0dHc+PGjUKTeFpaGkql8olkHBoaytGjR3nrrbdwdnama9euPPXUU8VK4nZ2dlyJSkGtUuSZhm3brDuJf36PfWA/tLfPoHKvjcLeJfdxQ2Yqcb/OxrnnG9g+1YWM0CPE/TqbmhO/z+0dA1yJTnnidfnpp59ITk7m3r17WFtbc+7cudzf07Fjx+Lh4UFYWBjp6en069cPX19f3njjjRK//kLBKn3SPXfuHPv37+fs2bP5Pv74/aa7i4bkfl9zwjKUjh55Hpdr7DGkJRR6zbSLf5JyciuG5Fh+VCtJS0ujZcuW9OvXj/j4eKytrc2WcAF6N/Pio62XCj3GqdOoAhNsflQKOa9ayEfR7Oxspk+fzooVK0qVcAGsrKxwcXHBxcWl6IOLQZIktFrtE8n41KlTuLi4oNVqCQ4O5pdffsHFxYUHDx4UWE55tD7u5N8N++4TkT3yyUrt0wSjNhXdgwjSLh3Atlm3nE8q/y/z5kmULjWxa9YNANunupByahuZN05g5//PMpJJ+cxYVKlUPHjwgLCwMPz9/QkMDARy1ifeuXMnSUlJaDQabG1tmTJlCitWrBBJ18QqfdI9dOgQ4eHh1KpVC4C0tDQMBgNXrlzhzJkzONuoUDwyPbLWe78V2p66TkuSDq/FmK1FbvXkR219ciwPdi/Fc/gn1Gjoz9mZvWnZsmXuwjTlUUe0Vip4/7lGfLYrlIwyDjECsFLI6dbIgwaelrHE47Jly2jcuDHdu3ev6FByyWQyNBoNGo0mz029Dh065H4/YcIE4uLiaNy4MW+//fYTbdjZ2eV+f+XKFXx8fPjtZDizd14jIzvvcEbbpt1IPb0d7Z2LuPb9F+lX/tkGyJCWgNIhb2dB6eiBIS3vlvCSxBMLJo0ZM4Z79+4xfPhwkpKSGD16NJ988gl37txBp9NRo8Y/n3aMRiO+vr7FeXmqhKikTM7dS+JiZDIJ6dkoFTL83Gzx93bC38fRZGtPV/qk+/rrr+dZ/OSLL74gPDycb7/9FoCmNR1LtFWNXdNupJ3dTdyWT3Hp8RpKF2+M2jTSzu7GytMPhaMnIENh40iTGg78+OOPXLr0T6/T09OTiIgIsrOzsbIy3+4Mo9vVZvPZSC5FJpeofJIfGysF/33R30SRlU1iYiKffvopBw8erOhQSkUmkxW4Mlx+e+bVcLFHIZMDjyXdZs8Stfx1bJt1Q67K++avsHNBn5J3E0xDShwKv5xeq9xKjaTLQtJp6dChA/Hx8ajVan788Ufq1q3L+PHjmTFjBvfu3aNv3740atSIvn37Ym1tTXx8fLVa91eSJPZciWHZoTCu3U9FpZCTnqXn4b+gtVKOSiHHYJQYEujD653r4utsU6ZrVvrJETY2Nnh5eeV+2dnZoVarc3sjvs6aPD3dosiUKjyHz0fl6kNM0Mfc+3Io9396F0NmMlY1G2HlVguHtoO4v3Yq26b24+LFi3Ts2DH3/G7dutG0aVO8vLxwczPfdEy5XMbKl1vjYW+NsgwLndhYKVgzvq3FDC369NNPGThwIE2bWuYaEI9KSkpiz549aLVa9Ho969ev5/Dhw/Tu3bvYbTSt6YhW/+SnFZWTF54j/4tT5zFPPKap1xpdQhTplw8hGQ2kXz1Mdvw9NPVzhjOqPPxIv3qYJjUcePXVV7l//z4ymYxDhw7x1ltvERgYiI2NDd27d+fevXusX7+e9evX07x5c15++WUiIiIwGo3cvHmzSm+0GZ2cybAVx3j3l3NciEgmS28k7ZGEC+T+LFNnYOOJu/RcHMyqv25jLENHp1qsvTB/xxV+Ohpe6nVn82OllHPk/WeLvNtvbvFpWYz+4Th3HmSUaDaTtVKOrZWSn8a3pbm3oxkjLL7w8HACAwO5dOlSno+5liouLo6+ffsSGhqKQqGgcePGzJs3j549e5aonbaf7ic2NQvtnQvEb1+Ez+Sfnjgm9fwe0i8dzB2nq713OWf0QlI0KqcaOaMXfHPeqHRJ93mw7XOkhAh6dOtKvXr1SEhIYN26dWzcuJHZs2cTERGBRqOhQ4cOdOvWjTt37nDt2jWOHz9OYmLONGKNRkOLFi3o0qULdevWzf3y9fWt9L3hi5HJjPj+GFqdocSfFDUqBR3qufLtqECslPn3W6v9gjcRiRn0+DIYrYnWS1DIoHsTT1aMyfc1LXcGo8SKwzf56s8byGWyQpOvSiFDLpPRt1kN5r7QFHu1ZfRwAUaOHEmjRo2YNWtWRYdSrv538Ab/OxBmst9PyHlT3f9ul1J9FJYkibi4OG7dupXvV0xMDD4+PnkScd26dfHz86Nu3bo4Oztb9NKbYbGpDFz2d5kW7ler5HRu4M7y0YH5PtfCkm7lfrsqJh9nG97oXK9EM9MKo8/KxO/BCYzGVqW+u25KCrmMN7vWZ2S72vx6+h4bT9zlbkIG2dpM7GxtkMnkaHUG3O2teb55DV7pUKfMdSlTO3nyJMHBwaxYsaKiQyl3I9rU4puDN03WnkIuo00dl1L/G8tkMjw8PPDw8Mh3e/usrCzu3LnD7du3cxPxiRMnuHXrFjdv3kQulz+RkB9+1apVy6z3OoqiMxh5fe3pMq9NodUZCbkRzy+n7jGsTa0SnVsterqQ82IP+OYIYbFpZSozqFVy3uvowarZb2Fra8uPP/5okXd4s/UGnGs15MDhv3CwtaG2qw02Vpb5HitJEl27dmXMmDFlmtlVmf34120+33PNJJ0CjUrBvimd8amAN1ZJkkhISMjTM340OUdGRlKjRo3cXvHjX25ubmbtJf/v4A2+OXjTJK8z5NwTOTztWdzsrPP8vNr3dCFnHOrGCU8z+Lu/uZeYWaqlGdUqOdOfa8y4jn6M6x7C559/TmBgIF9++SWjRo2yqI9UmelpyFNjaVffq6JDKdK2bdtISEhg3LhxFR1KhXmlfR12XbrP+YikYi1cXhCNSs6cAU0rJOFCTi/Z1dUVV1fXfNdB0el03Lt3L09S3rJlS+73Op2uwLJFnTp1UKuLvocSFBTEnDlzuHv3Ll5eXqxevZpOnTqRrTey/HDJPu1KBh3Jf/9C+pVDGNISkGscUdf2x6njCJROnhiMEuuP3eFfPRoW/zWqLj3dh1K1Oj7ccpF9V2PQFnOpR5UiZ9eIL19qSc+n8k59PXv2LGPGjKFx48Z89913Zh2xUBLXr1+nb9++edahsEQ6nY7mzZuzePFi+vTpU9HhVKj0LD0jvj/G9ZjUUtV3NSo573RvwJtdCp9RacmSkpLy9Iwf/bp79y7u7u4Fli48PT3Zv38/EyZM4Oeff6Zt27ZER0cDOQsR7boUzdRfzxc6k/Nxsb/NxZD6AJfek7HyrIeUrSX98kFQWmHfImfBfycbFWdn9MzT6RI93UfYq1UsHdGK4OtxfLbrKrcfpGMwSvmWHGytFBgkiUEtvXn/ucY42z5ZiwoICODUqVN8/PHHtGjRguXLl9OvX7/yeCqFiomJMdnaCOb0/fff4+vrW6JhVlWVrbWSX95oz7wdV9h0JqJEnQJrpYKFQ/zp08zyR30UxsnJiYCAAAICAp54zGAwEBERkScR79y5M/f79PR0JEmiQYMGbNy4kePHj+cmZGdnZw5diytRws0MP4c2/Bw1X1+O0iFnCKpMbYt94D//v9Mu7CP6+GbsFyTi4eHO9OnTi5zBV+16uo+7Gp1C8PU4jt9+wO34nARsr1bS0teZtnVc6PmUJ7bWxXtvOnz4MK+88go9evTgyy+/xN6+4mZ4/fbbb2zYsIHNmzdXWAxFSUlJoWHDhuzevZuWLVtWdDgW5WR4Ap/tCuVSVDJAviWHh52CgS29mdqr0RN1xeomKSkJd3d3hg8fzq5du9Bqtbi6umJtbc29e/dwH7sEuVPNYreXeGg1WZGhhS6nmRF2EqcatVn0ai8cksLo06cPR44cITAwUPR0C9KkhgNNajgwsUu9MrfVuXNnzp8/z7vvvkuLFi346aef6NSpkwmiLLnY2Fg8PDyKPrACLViwgN69e4uEm482dVzY9GYHbsenE3w9lhO3E7gRm4beKGFrpSCgljOtazvTvYkndsXsFFR1GRkZ6PV6rl69ysWLF1GpVLzwwgt07dqVefPm0XLuXlKyit/TNWamoLBzLvQYm/ptkBQyYlKz6N+lC7169SIkJKTQc8S/lok5ODiwcuVKtm3bxrBhwxg9ejTz5s3D2rp8eyGWXl64d+8e3333HefPny/64GrMz80WPzc/xnawjMWILNnDRabefvvt3Mk17777LvPnz+eTTz5BJpcD/yTdqJWT0CfnTKf2GDobtW+zPO3JNQ7oEqIKvWbmzVPc/2sj/1pyn6nynMTfvHnhO3ZU/CDTKmrAgAGcP3+esLAwWrduzblz58r1+paedD/++GMmTpyY73KcglAazs7O+Pj4PH5DK/d7B3XePmbNCcuo9d5v1HrvtycSLuQsfpUdfR19Sv5rV0t6HXFb/otbh8H8sO8sSUlJ9O3bt8C1Nx4SSdeM3N3d2bRpE9OmTaNnz57897//xWAwzfjAolhy0j137hy7d+9m+vTpFR2KUMWMGzeOpUuXEhsbS2JiIosXL869sd3Cx6lEbWnqtERdpyVxmz8h634YktGAMSuD1LM7STu/F8mgQzLoUNo40qKWC7t27WLv3r1FtiuSrpnJZDJefvllTp8+zf79++ncuXO5DOOy1KQrSRJTp05l5syZeRYtFwRT+Pjjj2nTpg0NGzakSZMmBAQE8NFHHwHQqYEbNiVcntF90Ado6rUmfusC7i0eStQPk8mODkNdpyVyaxtcerxOxKb/0r5JbTZs2MCAAQOKbLPaj14oT0ajkaVLlzJv3jzmz5/PG2+8YbYJFfXq1WP37t00aNDALO2X1q5du5gyZUrujQ5BKC+pWh1tPt1f7KF4xaGUyxjexveJnZcLG6crerrlSC6X869//YuQkBBWrlxJ3759iYoqvFBfWpbY09Xr9UybNo0FCxaIhCuUO3u1ikEB3lgXsDJYaSgVMsaXcMcVkXQrQJMmTTh69Cjt2rUjICCAn3/+2aTtp6enYzAYKnSccH5Wr16Nq6trsT6CCYI5fNCnCTZWptkBQqOSM66DH3Xd7Yo++BEi6VYQlUrF7Nmz2b59O7NmzWLkyJEkJBS+N1txPezlWtJaEGlpacyaNYsvvvjCouISqheH/5+RqlaVLfWpFDJ8XWyYUoI1Fx4SSbeCtWnThjNnzuDu7k6LFi2KdfezKJZYWli0aBFdunTJdxEUQShPz9R3Y+Fg/1InXiulHB9nG4Jea1/gIuaFEUnXAtjY2PD111+zevVqXnvtNSZPnkx6enqp27O0pBsdHc2SJUv49NNPKzoUQQCgfwtv1oxrh7u9dYlqvGqVnN5Nvdg2uSMu+azFUhwi6VqQ7t27c/78eVJTU2nZsiXHjh0rVTuWlnRnzZrF+PHjqVOnTkWHIgi52vq5EDy1K6929MPOWomdtYL8thu0VspRK+U093ZgxejWLBkeUKYdV8Q0YAvj5OTEmjVr2LRpEwMHDmTChAnMnDmzRKvtW1LSvXz5Mlu3buXatWsVHYogPMHGSsn7vRvz7x4NOXwjjtN3Ejl1J4GkDB0Kec4W7G3ruNCpgRv1PUxzY1okXQs1ePBgOnbsyGuvvcbTTz/N2rVri71DbkxMDI0bNzZzhMXz/vvv8+GHH+LsXPjCIYJQkayUcno08aRHE/N3VkR5wYJ5eXmxbds2Jk+eTNeuXVm0aFGxphFbSk93//79XLt2jUmTJlV0KIJgMUTStXAymYxXX32V48eP8/vvv9OtWzfCw8MLPccSkq7RaGTatGl89tlnFboRoSBYGpF0K4m6dety8OBB+vfvT5s2bVi1alWBqxlZQtJdt24dGo2GwYMHV2gcgmBpxNoLldDFixcZM2YMtWvXZsWKFU8kWEdHR8LDwyusjpqZmUmjRo0ICgqiQ4cOFRKDIFQksfZCFdO8eXNOnDhBs2bNaNGiBVu2bMl9TKvVotVqcXJyqrD4vvrqK9q2bSsSriDkQ4xeqKSsrKz45JNPeP7553n55ZfZunUrS5YsISkpCQ8PjwqbahsbG8uiRYtKPcZYEKo60dOt5Dp06MC5c+ewtbXF39+fHTt2VGg9d+7cuYwaNYr69SvvNuCCYE6ip1sF2NnZsWzZMnbv3s3o0aOxt7cnMzMzd8+o8nLt2jV+/vlnrl69Wq7XFYTKRPR0q5DevXvz8ccfA9CqVSvK+ybo9OnTef/993FzcyvX6wpCZSKSbhWTnp7OsGHDmDVrFs8//zxz5sxBp9OZ/bqHDx/m3LlzvP3222a/liBUZiLpVjEPx+gOHz6cM2fOcPToUTp27EhoaKjZrmk0Gpk6dSqffvoparXabNcRhKpAJN0q5tGJEd7e3uzatYtx48bRqVMnli5ditFYsv2hJEkiKimTA6ExbDsfxc6L0VyOSkZn+KedX375BaPRyPDhw036XAShKhI30qqYx2ejyWQy3nzzTXr06MHLL7/M77//zo8//oivr2+h7dx5kM6Pf4ez6UwE2XojKoUcCQkZMiQksvVGWtd2YXx7X/7zwYes/nEVcrl4DxeEooikW8UUNAW4QYMGhISE8PnnnxMYGMiiRYsYPXr0E+N5tToDn+26ysaT9zAaJXTGnBmLWfone8hHbz3g1K1Y1APn4PNUvpNvBEF4jOiaVDGFrbugVCr58MMP2bNnDwsWLOCll14iPj4+9/HIpEy6fxlM0Kl7ZOmNuQm3MDoUZKld6Pe/EDafiTDZ8xCEqkok3SpEp9ORkpKCq6troccFBARw6tQp/Pz88Pf3Z/v27dxP1jLgf0e4n6xFqyth3RfQ6ox8uPWiSLyCUARRXqhCYmNjcXNzK1ZtVa1Ws3DhQvr378/Lr4zFfvBcMqycMRTduS3Qw8Tr7+NoslX2BaGqET3dKqQ0Szp27tyZ91fuIENhX6aE+1CW3sib689gKEZpQhCqI5F0q5DSJN1svZFvj9zFIDfNhx5JyqkNH74eZ5L2BKGqEeWFKqQ0SXfvlfsYi+iV3l00JM/fJX029gF9cek1Md/jM7INfHv4Js829ihRLIJQHYikW4WUJunuuBhNenbh+67Veu+33O+N2ZlELB2DTeNnCj3nzJ1EsvVGrJTiw5QgPEr8j6hCSpN0z99LKtHxGdf+RmHjiLVv4TsTq1VyrseklqhtQagORNKtQkqTdGNSs0p0fNrFP7Ft1q1Yi6TffpBeorYFoToQSbcKKU3SLaqe+yh9cixZ9y5h27x7kcdKEugNJRvvKwjVgUi6VUhpkm5Jaq5plw5g7fMUKievIo+Vy2TYWotbBoLwOJF0q5DSJF0/N9tiH5t+6QB2zboV61i90UgTL4cSxSII1YFIulWEXq8nMTGxxLs2tPNzQV6MPSy1EVcxpD0octTCQ3KZDB/n8t0uSBAqA5F0q4j4+HicnZ1RKkv2kf6lQF+slYoij0u/9Cc2DTsgt7Yp8lilXMbAAO8K25FYECyZKLpVEaUpLQA083bE10XD9Zi0Qo9z7f1WsdtUyGWM6+BX4lgEoToQPd0qorRJF+DzwS1Qq0zzq6BWyRncyof6HnYmaU8QqhqRdKuIsiTdlr5OjG5XG00ZE69cBk4aFTOeb1KmdgShKhNJt4qIjY3Fw6P0ax180KcJXRp6lDrxKmQyHDUqfnmjAzZWomolCAURSbeKKEtPF3LqsN+MbMWodrVLXGrQqBQ09LLjj7eeoZZL0TfaBKE6E0m3iihr0oWcxDvj+aeY1soKkqPQqOSFDieztVJgZ61kSo8GbH+rEz7OIuEKQlHE58AqwhRJF3K2XF+5YAbzJ0+mxbMd+PnkXU6EJxAen0G2wYhcBu721vh7O9HPvwa9m3kVa8iZIAg5RNKtIkyVdP/44w/S0tIYMWIECoWC5t7Ncx+TJEmMvRWEMhLlhSrCFEnXYDDw0Ucf8cknn6BQPNl7FQlXEMpOJN0qwGg0EhcXV6bRCwAbN27E3t6e/v37mygyQRAeJ8oLVUBCQgL29vZYWVmVuo3s7GxmzpzJqlWrRI9WEMxI9HSrAFOUFlauXEmDBg3o2rWraYISBCFfoqdbBZQ16aanpzN//nz++OMPE0YlCEJ+RE+3Cihr0l26dCkdO3YkMDDQhFEJgpAf0dOtAsqSdBMTE1m0aBEhISEmjkoQhPyInm4VUJaku3DhQgYMGEDjxo1NHJUgCPkRPd0qICYmhnr16pX4vPv377N8+XLOnj1rhqgEQciP6OlWAaXt6c6fP5+XX36ZWrVqmSEqQRDyI3q6VUBpku7t27fZuHEjoaGhZopKEIT8iJ5uFVCapDtr1izeeust3N3dzRSVIAj5ET3dSk6SJGJjY0uUdC9dusSePXu4ceOGGSMTBCE/oqdbySUlJaFWq1Gr1cU+Z8aMGbz//vs4ODiYMTJBEPIjerqVXElLC0ePHuX06dNs3LjRjFEJglAQ0dOt5EqSdCVJ4sMPP2TWrFloNBozRyYIQn5E0q3kSpJ09+3bR1RUFGPHjjVvUIIgFEgk3UquuEn3YS933rx5KJWiqiQIFUUk3UquuFuvb9q0CaPRyJAhQ8ohKkEQCiKSbiVXnJ6uXq9nxowZfPrpp8jl4p9cECqS+B9YyRUn6a5ZswZPT0+ee+65copKEISCiOJeJVdU0tVqtcyePZugoCCxDY8gWADR063kikq63333HS1atKBDhw7lGJUgCAURPd1KTJKkQpNuamoqn332GXv37i3nyARBKIjo6VZiaWlpyGQy7Ozs8n188eLFdO/eHX9//3KOTBCEgoiebiVWWC83Pj6eJUuWcPz48XKOShCEwoiebiVWWNL97LPPeOmll0q1o4QgCOYjerqVWEFJNyIiglWrVnHp0qUKiEoQhMKInm4lVlDSnTt3Lq+99ho1a9asgKgEQSiM6OlWYvkl3evXr7N582auX79eQVEJglAYkXQrCaNR4tjtB5wMT+RkeALxaVlEpjfE007JyiO36FDPjadqODBz5kymTJmCi4tLRYcsCEI+RNK1cDqDkdV/32b54Vtk6gxk6YzojVLOg0pHUrWwcM815LLruGlk3ApP5fvv36nYoAVBKJBIuhbs2v1U3lx/muhkLZk6Q4HHZemNANzTgebZibz121UWvdQCNzvr8gpVEIRiEjfSLNSxWw8Y9O1f3I5PLzThPk6PnL9vxtN3SQgRiRlmjFAQhNIQSdcChd5PYfxPJ8nINiCV4nydQeJBWjaDv/ubFK3O5PEJglB6IulaGJ3ByMR1p8nMLn7vNj8GSSIpQ8fHW8VYXUGwJCLpWpgfjtwmJiWrVD3cx2Xpjey9EsPJ8AQTtCYIgimIG2kWxGCUWH74ZqE1XEmv48HeZWjDz2HUpqF08sK5yyto6rXO93itzsCyQ2H8OLatucIWBKEERE/XgvwVFk+2wVjoMZLRgNLeDa+Rn+E75WecOo8h7vcF6JNi8j8e+PvmAxLSs80QsSAIJSWSrgU5EZ5QZC1XbqXGqdMolE6eyGRybOq3RenoSdb9sALPsVLKOR+RZOJoBUEoDZF0LcjJ8ASMJSzmGtIT0SVEYuVeq8BjMrMNXIpILmN0giCYgki6FiQ+LatEx0sGPfHbvsCueXdUrr4FHqc3SsSkassaniAIJiCSrgUpycaRkmQkfvsiUChx6TmxyOPlYlNKQbAIIulakJqO6mIdJ0kSD3YuwZCehPugD5EpCh+EYqWQ4e2sMUWIgiCUkUi6FuTpuq6oFEX3SBP2fIPuwT08hsxErip6fQVrpQJ/HycTRCgIQlmJcboWJCfpytEZCh7BoE+OJe3cblCoiFg6JvfnLr0nY9f02XzPyTYY8fd2NHm8giCUnEi6FiTA1wlXWysysjMLPEbp6EHt/2wvdpsKuYznm9fA1lr8UwuCJRDlBQsik8mY0qMhGiuFydpUKWS82UVsTikIlkIkXQszKMCb5jUdUJjgX0ajUjDhmbo08LQve2OCIJiESLoWRiaTsWR4KxzVVpRllJeVUk59Dzve6dbAdMEJglBmIulaIC9HNZve7ICLjVWxRjM8TqOS08jTng0T2mGlFP/EgmBJxP9IC+XnZsu+KV3o2tAdjap4NV65DNRKOeM6+LFpYgfs1SozRykIQknJJKngyf4ymSwOuFN+4QiCIFQJtSVJcs/vgUKTriAIgmBaorwgCIJQjkTSFQRBKEci6QqCIJQjkXQFQRDKkUi6giAI5ej/ALdFRok/jSYAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "G=nx.Graph()\n",
    "G.add_edges_from(points_list)\n",
    "mapping={0:'Jerry', 1:'1', 2:'2', 3:'3 - Mouse',\n",
    "    4:'4 - Cat', 5:'5 - Mouse', 6:'6 - Cat', 7:'7', 8:'8 - Cat', 9:'9 - Cheese'}\n",
    "H=nx.relabel_nodes(G,mapping)\n",
    "pos = nx.spring_layout(H)\n",
    "nx.draw_networkx_nodes(H,pos,\n",
    "    node_size=[500,500,500,500,500,500,500,500,500,500])\n",
    "nx.draw_networkx_edges(H,pos)\n",
    "nx.draw_networkx_labels(H,pos)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The map shows the cell to cell route that Jerry has to scout while dealing with other mouse and avoiding cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "MATRIX_SIZE = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Initial_Cheese_Path_Matrix = np.matrix(np.ones(shape=(MATRIX_SIZE, MATRIX_SIZE)))\n",
    "Initial_Cheese_Path_Matrix *= -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question: Define values of viable path = 0 and for path leading to cheese = 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 1)\n",
      "(0, 3)\n",
      "(1, 2)\n",
      "(1, 4)\n",
      "(1, 5)\n",
      "(1, 8)\n",
      "(2, 7)\n",
      "(5, 6)\n",
      "(5, 9)\n",
      "(7, 9)\n",
      "(4, 7)\n",
      "(3, 8)\n",
      "(3, 4)\n",
      "(8, 9)\n"
     ]
    }
   ],
   "source": [
    "# Your code here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],\n",
       "        [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],\n",
       "        [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],\n",
       "        [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],\n",
       "        [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],\n",
       "        [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],\n",
       "        [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],\n",
       "        [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],\n",
       "        [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],\n",
       "        [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Initial_Cheese_Path_Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To read the above matrix, the vertical values in the first column are the states or where Jerry is currently located, and the horizontal values are your possible next actions. We will use this matrix to update the values as Jerry learns which path it should take and which one to avoid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We have assigned nodes 3 & 5 as mouse with which Jerry has to race and nodes 5,6,8 as cat that Jerry has to avoid. Jerry doesn’t know yet that there are other mouse or cats nor does it know that cats are bad as they will eat Jerry. Jerry needs to explore the environment, but this time it needs to deal with environment also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = np.matrix(np.zeros([MATRIX_SIZE,MATRIX_SIZE]))\n",
    "gamma = 0.8\n",
    "mouse_present = np.matrix(np.zeros([MATRIX_SIZE,MATRIX_SIZE]))\n",
    "cat_present = np.matrix(np.zeros([MATRIX_SIZE,MATRIX_SIZE]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_state = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'a' cannot be empty unless no samples are taken",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-70e09573552f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mavailable_act\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavailable_actions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minitial_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_next_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mavailable_act\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-15-70e09573552f>\u001b[0m in \u001b[0;36msample_next_action\u001b[0;34m(available_actions_range)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msample_next_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mavailable_actions_range\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mnext_action\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mavailable_act\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnext_action\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: 'a' cannot be empty unless no samples are taken"
     ]
    }
   ],
   "source": [
    "def available_actions(state):\n",
    "    current_state_row = Initial_Cheese_Path_Matrix[state,]\n",
    "    av_act = np.where(current_state_row >= 0)[1]\n",
    "    return av_act\n",
    "\n",
    "def sample_next_action(available_actions_range):\n",
    "    next_action = int(np.random.choice(available_act,1))\n",
    "    return next_action\n",
    "\n",
    "def collect_environmental_data(action):\n",
    "    found = []\n",
    "    if action in mouse:\n",
    "        found.append('m')\n",
    "\n",
    "    if action in cat:\n",
    "        found.append('c')\n",
    "    return (found)\n",
    "\n",
    "available_act = available_actions(initial_state)\n",
    "\n",
    "action = sample_next_action(available_act)\n",
    "\n",
    "def update(current_state, action, gamma):\n",
    "    max_index = np.where(matrix[action,] == np.max(matrix[action,]))[1]\n",
    "\n",
    "    if max_index.shape[0] > 1:\n",
    "        max_index = int(np.random.choice(max_index, size = 1))\n",
    "    else:\n",
    "        max_index = int(max_index)\n",
    "    max_value = matrix[action, max_index]\n",
    "\n",
    "    matrix[current_state, action] = Initial_Cheese_Path_Matrix[current_state, action] + gamma * max_value\n",
    "    print('max_value', Initial_Cheese_Path_Matrix[current_state, action] + gamma * max_value)\n",
    "\n",
    "    environment = collect_environmental_data(action)\n",
    "    if 'm' in environment:\n",
    "        mouse_present[current_state, action] += 1\n",
    "\n",
    "    if 'c' in environment:\n",
    "        cat_present[current_state, action] += 1\n",
    "\n",
    "    if (np.max(matrix) > 0):\n",
    "        return(np.sum(matrix/np.max(matrix)*100))\n",
    "    else:\n",
    "        return (0)\n",
    "\n",
    "update(initial_state,action,gamma)\n",
    "\n",
    "scores = []\n",
    "for i in range(700):\n",
    "    current_state = np.random.randint(0, int(matrix.shape[0]))\n",
    "    available_act = available_actions(current_state)\n",
    "    action = sample_next_action(available_act)\n",
    "    score = update(current_state,action,gamma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Print where other mouse and cat were found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mouse Found\n",
      "[[ 0.  0.  0. 32.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0. 12.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0. 25.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0. 55.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0. 23.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0. 26.  0.  0.  0.  0.]]\n",
      "Cat Found\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0. 17.  0.  0.  0. 14.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0. 23.  0.  0.  0. 22.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0. 18.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0. 25.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0. 19.  0.]]\n"
     ]
    }
   ],
   "source": [
    "print('Mouse Found')\n",
    "print(mouse_present)\n",
    "print('Cat Found')\n",
    "print(cat_present)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Giving a negative value to cat so that Jerry avoids that route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "available_act = available_actions(initial_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "action = sample_next_action(available_act)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question: Update the initial cheese path matrix according to the path selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_value 316.31065118525817\n"
     ]
    }
   ],
   "source": [
    "def update(current_state, action, gamma):\n",
    "\n",
    "    ____________________________________________________\n",
    "\n",
    "    if max_index.shape[0] > 1:\n",
    "        max_index = int(np.random.choice(max_index, size = 1))\n",
    "    else:\n",
    "        max_index = int(max_index)\n",
    "    max_value = matrix[action, max_index]\n",
    "\n",
    "    matrix[current_state, action] = __________________________________________________________\n",
    "    print('max_value', Initial_Cheese_Path_Matrix[current_state, action] + gamma * max_value)\n",
    "\n",
    "    environment = collect_environmental_data(action)\n",
    "    if 'b' in environment:\n",
    "        enviro_matrix[current_state, action] += 1\n",
    "    if 's' in environment:\n",
    "        enviro_matrix[current_state, action] -= 1\n",
    "\n",
    "    return(np.sum(matrix/np.max(matrix)*100))\n",
    "\n",
    "update(initial_state,action,gamma)\n",
    "\n",
    "enviro_matrix_snap = enviro_matrix.copy()\n",
    "\n",
    "def available_actions_with_enviro_help(state):\n",
    "    current_state_row = Initial_Cheese_Path_Matrix[state,]\n",
    "    av_act = ________________________________\n",
    "    # Jerry will not take any route with even a single negative value as Jerry does not want to be in danger\n",
    "    env_pos_row = enviro_matrix_snap[state,av_act]\n",
    "    if (np.sum(env_pos_row < 0)):\n",
    "        temp_av_act = ________________________________\n",
    "        if len(temp_av_act) > 0:\n",
    "            print('going from:',av_act)\n",
    "            print('to:',temp_av_act)\n",
    "            av_act = temp_av_act\n",
    "    return av_act"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training for 1500 iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_value 313.58687413062455\n",
      "Score: 2096.7054267577437\n",
      "max_value 313.58687413062455\n",
      "Score: 2096.7054267577437\n",
      "max_value 494.86949930449964\n",
      "Score: 2092.259231714177\n",
      "going from: [0 4 8]\n",
      "to: [0]\n",
      "max_value 250.86949930449964\n",
      "Score: 2092.259231714177\n",
      "going from: [2 4 9]\n",
      "to: [2 9]\n",
      "max_value 313.58687413062455\n",
      "Score: 2092.988188197976\n",
      "max_value 313.58687413062455\n",
      "Score: 2092.988188197976\n",
      "max_value 313.58687413062455\n",
      "Score: 2093.717144681775\n",
      "max_value 313.58687413062455\n",
      "Score: 2094.446101165574\n",
      "max_value 313.58687413062455\n",
      "Score: 2094.446101165574\n",
      "max_value 313.58687413062455\n",
      "Score: 2094.446101165574\n",
      "max_value 313.58687413062455\n",
      "Score: 2094.446101165574\n",
      "max_value 313.58687413062455\n",
      "Score: 2094.446101165574\n",
      "max_value 313.58687413062455\n",
      "Score: 2094.446101165574\n",
      "going from: [0 2 4 5 8]\n",
      "to: [0 2 5]\n",
      "max_value 391.98359266328066\n",
      "Score: 2095.3572967703226\n",
      "going from: [0 4 8]\n",
      "to: [0]\n",
      "max_value 250.86949930449964\n",
      "Score: 2095.3572967703226\n",
      "max_value 313.58687413062455\n",
      "Score: 2095.3572967703226\n",
      "max_value 391.98359266328066\n",
      "Score: 2095.3572967703226\n",
      "max_value 391.98359266328066\n",
      "Score: 2095.3572967703226\n",
      "going from: [0 4 8]\n",
      "to: [0]\n",
      "max_value 250.86949930449964\n",
      "Score: 2095.3572967703226\n",
      "max_value 391.98359266328066\n",
      "Score: 2095.3572967703226\n",
      "going from: [1 6 9]\n",
      "to: [1 9]\n",
      "max_value 494.86949930449964\n",
      "Score: 2096.3454377816943\n",
      "max_value 313.58687413062455\n",
      "Score: 2096.3454377816943\n",
      "going from: [2 4 9]\n",
      "to: [2 9]\n",
      "max_value 313.58687413062455\n",
      "Score: 2096.3454377816943\n",
      "going from: [1 6 9]\n",
      "to: [1 9]\n",
      "max_value 494.86949930449964\n",
      "Score: 2096.3454377816943\n",
      "max_value 313.58687413062455\n",
      "Score: 2096.3454377816943\n",
      "going from: [2 4 9]\n",
      "to: [2 9]\n",
      "max_value 494.86949930449964\n",
      "Score: 2097.3335787930664\n",
      "max_value 313.58687413062455\n",
      "Score: 2098.062535276865\n",
      "going from: [2 4 9]\n",
      "to: [2 9]\n",
      "max_value 313.58687413062455\n",
      "Score: 2098.062535276865\n",
      "going from: [1 6 9]\n",
      "to: [1 9]\n",
      "max_value 494.86949930449964\n",
      "Score: 2098.062535276865\n",
      "max_value 313.58687413062455\n",
      "Score: 2098.062535276865\n",
      "max_value 395.89559944359974\n",
      "Score: 2099.7642436907117\n",
      "max_value 395.89559944359974\n",
      "Score: 2100.5547564998096\n",
      "max_value 395.89559944359974\n",
      "Score: 2100.5547564998096\n",
      "max_value 313.58687413062455\n",
      "Score: 2100.5547564998096\n",
      "max_value 395.89559944359974\n",
      "Score: 2100.5547564998096\n",
      "max_value 313.58687413062455\n",
      "Score: 2100.5547564998096\n",
      "max_value 313.58687413062455\n",
      "Score: 2100.5547564998096\n",
      "going from: [5 7 8 9]\n",
      "to: [5 7 9]\n",
      "max_value 494.86949930449964\n",
      "Score: 2100.8139410273825\n",
      "max_value 313.58687413062455\n",
      "Score: 2100.8139410273825\n",
      "max_value 313.58687413062455\n",
      "Score: 2100.8139410273825\n",
      "max_value 395.89559944359974\n",
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      "going from: [5 7 8 9]\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "max_value 499.9979188216685\n",
      "Score: 2102.4479579279728\n",
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      "max_value 255.99833505733483\n",
      "Score: 2102.4479579279728\n",
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      "Score: 2102.4480411754525\n",
      "going from: [0 2 4 5 8]\n",
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      "max_value 319.9983350573348\n",
      "Score: 2102.4480411754525\n",
      "max_value 399.9983350573348\n",
      "Score: 2102.4480411754525\n",
      "max_value 399.9983350573348\n",
      "Score: 2102.4480411754525\n",
      "max_value 313.58687413062455\n",
      "Score: 2102.4480411754525\n",
      "max_value 319.9979188216685\n",
      "Score: 2102.4481452348023\n",
      "max_value 319.9979188216685\n",
      "Score: 2102.4481452348023\n",
      "max_value 313.58687413062455\n",
      "Score: 2102.4481452348023\n"
     ]
    }
   ],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best path to get to cheese before the other mouse while avoiding the cats is:\n",
      "[0, 1, 5, 9]\n"
     ]
    }
   ],
   "source": [
    "current_state = 0\n",
    "steps = [current_state]\n",
    "\n",
    "while current_state != 9:\n",
    "\n",
    "# Your code here\n",
    "\n",
    "print(\"The best path to get to cheese before the other mouse while avoiding the cats is:\")\n",
    "print(steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1A4DPzZ3MVz9wfHhOsm0FMq0dICJdqcocotKZOHtCriSQSOQahg+lwqf3rqZ36FgS+N2abQB8/szJuXNqE23/rLUqCYhIF6qyJFDpTJxH4lAqk2uszbdpd9hjJ+wdFO7L1uN39fSetLY2gW37WvjR8+sAaGqsy52T37OoXiUBEelCVQaBtdv299pnXXnvIhat3VH0eGNdslNJoKtBXYlEGDRWb9nHZ+9eAMDffuLkdnX/NXkDzWprtISDiBRXlUHgqZVbeu2zNuw6yJwpTXxmzqROx+prE1wwfRyrN+8D8koCJapwkgnj569uBOBTp0/kE7MmtDueX5KoSxbuZSQiAlUaBA5GUyyPGlxX4swjdyiV4YSjhvKZMzoHgayoIJArCZRqzE0mLDfO4B8+eUqnieTySwKaL0hEulKVFcbZ7pPWCzUlh1IZBtV2/TSerQ7avOcQULox91AqLDH82YdOKDiT6OC6tox/7tSRFaVXRKpLVT4mHoieuHu6fdjdOZQOaCgVBKI8/42tYVtFoXmD8l06awJLN+zm/cePKXj8I6eMpyZpzJrcVPKzRaS6VV0Q+NHz6/jxwrcB6Ok+Qtv2tZIJnIbarjP1RF6R5LIzJpWc7fO2z57W5fHB9TUaJSwiZam66qDnon71vTGS9pGX1wNw1PCuZ/LMr9H51sdP6skkiYi0U3VBIHDnxKOG8tk5k/Aerg862BrW3V9y2tFdnmd5JQFV34hIb6q6IJAJwkzXrOerg1ozGZIJKzoFRFayN1qoRUQKqLog4O4kE9Ab2W5rOihr7p6EgoCI9JGqCwKBey7T7eneQS3poKy2B8UAEekrVRgEstVB1uNtAq3poGR3T6BgX38Rkd5QsouomU0CfgiMI6xGv8fdbzezTwPfAqYDc919cd57bgauBjLAl939iWj/hcDtQBL4vrvf2r1fp7SwJBBud0cIWLNlH+t3Hih47J2dB8oqCSgGiEhfKWecQBq40d1fMrOhwItm9iSwDLgUuDv/ZDObAVwGnAQcDTxlZsdHh78HfABYD7xgZo+5+4ru+SrlCdxJRg3D+VrTAS+s3cE5x42u6HqfvPM5dh9MFT1+6sTSyzpm2w1mTS68kIyISE8pGQTcfSOwMdrea2YrgQnu/iS0794YuRh4wN1bgLfMbA0wNzq2xt3fjN73QHRu7waBIK8hNq8o8Jc/W879C9/mVze8lxOPGlbWtdyd3QdTfPr0iXwubz7/fFNGNpa8zqgh9dx1+Wxmah1gEellFY0YNrNmYBawsIvTJgDP571eH+0DeKfD/jMr+fzukHHHDAxrVx30yvpdALRE8/KUda1ocZrJIxuZPbnpiNJ14czxR/R+EZHDUXbDsJkNAX4K3ODue3ouSWBm15rZYjNbvHXr1m69dthFtHN10IGWcD6hwfXlD9bKzuSp1btEZKAqK/cys1rCAHC/uz9c4vQNQP68yROjfcX2d+Lu97j7HHefM2ZM4UnSDlfg5HURDTPxTbsP8Wa00EwyUX6G3prJrhOsICAiA1PJ3MvCSv97gZXuflsZ13wMuMzM6s1sKjANWAS8AEwzs6lmVkfYePzY4Sf98AS56qC2JoEnV24+rGulMtnlINW9R0QGpnLaBM4BrgCWmtmSaN8tQD3wXWAM8HMzW+LuH3L35Wb2IGGDbxr4krtnAMzsT4AnCLuI/qu7L+/er1NaEISDxfKrg3YfaFsDuJKxA7nF4lUSEJEBqpzeQc9SfJaFR4q859vAtwvs/wXwi0oS2N0CD1fmCtMT7vvv1dtyxysZO5BSdZCIDHBVl3tlB4uZGR5l+fn9/MstCBxszfBfK8JqpFpVB4nIAFWFQSCaNiJv34HWTK50UG5Z4BdLN/LXj4dDHMYObejeRIqI9JKqW1ksbBMIt7NP/fta0gxtqGHXgVTZJYED0WL1T9zwPk44amgPpFREpOdVYUkgHCdAtJ7Aqk172LG/lSH1YTwst00gO0Zg3LD6nkmoiEgvqMogEFYHhcWBF9buBOCM5pFA+W0C6SBsFFbPIBEZyKouB8sfLIZDOurhM6e5smkfsiWBGk0BKiIDWBUGASdpRMtLem7+n9popLCXWSGUHSOg7qEiMpBVXQ6WXVnMCKt+2ub/qWy1sXQQYEZeryIRkYGn+oJA3kLzAJls3X62JFBmEGjNBCoFiMiAV3W5WMeVxXIlgWjAVyXVQbUqBYjIAFeVQSCZCHsHuYdtAuHU0pVVB23b19LWwCwiMkBVYRBoXx2UCoIoKFRm8dqdtGTKX4BGRKQ/qu4Rw0AmqtYpsExml4bU1zB6qAaKicjAVoUlgfa9g9JRdVBWJQ3Dk8tYP1hEpD+rwiAQdeuMnvxXbdpDIq86qNyG4ZZUhnotKykiA1zV5WLZlcWynn9zB6l0kNtXSUmgTkFARAa4qsvFciuL5e276aLp5M0kUZaWdECdxgmIyABXdblYtjoovzRwzOjBuQnlyl1esiUdUF9bdbdPRGKm6nKxjtVBQG5qaahkKmmVBERk4Ku6XMyjWUTzK4RqKhwn4O64a94gERn4qi4IZHJrDLftS1TYRTSaeFQjhkVkwKu6IBBOJd0+865pN1isdBTITj+tgoCIDHRVFQSy1TgdF5rP7y1UXkkgCgKKAiIywFVVEMivxskvDNQkraIuoq7qIBGJiSoLAmHu3bFTT03C8rqIln8dFQREZKCrqgnkspl3x8ni8ksG5YwTaAsCigIiMrBVVUmgWDVOTSJRURfRbLVSpTOPioj0N1UVBPJ79bTvItq2ffPDS/naQ690eR1XdZCIxETJIGBmk8zsGTNbYWbLzez6aP9IM3vSzFZHv5ui/U1m9oiZvWpmi8xsZt61vhJdY5mZ/cTMGnruq3XW1ibQuSSQLQq8uW0/Dy5eX+I64W9VB4nIQFdOSSAN3OjuM4CzgC+Z2QzgJmC+u08D5kevAW4Blrj7KcCVwO0AZjYB+DIwx91nAkngsu78MqUcTGWI0tJuxHAYA8rP0DVOQETiomQQcPeN7v5StL0XWAlMAC4G7otOuw+4JNqeATwdnb8KaDazcdGxGmCQmdUAjcC73fQ9yvI3j68EoLEuSUs6k9s/anB9p/mEuuIaJyAiMVFRm4CZNQOzgIXAOHffGB3aBGQz+leAS6Pz5wJTgInuvgH4J+BtYCOw293/6wjTX5EDrWHG/4lZE3JP81+54PiK1xhWdZCIxEXZQcDMhgA/BW5w9z35xzx8NM72rbwVGGFmS4DrgJeBTNRmcDEwFTgaGGxmlxf5rGvNbLGZLd66dWul36mowJ2TJwynoTZJOsrJa5KW/cyKrgOqDhKRga+sIGBmtYQB4H53fzjavdnMxkfHxwNbANx9j7tf5e6nEbYJjAHeBC4A3nL3re6eAh4Gzi70ee5+j7vPcfc5Y8aMOYKv11468FwVTrYkkG0kruShvth4AxGRgaac3kEG3AusdPfb8g49BsyLtucBj0bnjzCzumj/NcBvo5LD28BZZtYYXfN8wvaFXhMETvTgTzoTlQQO43Fe00aISFyUM2L4HOAKYGlUxQNhD6BbgQfN7GpgHfCZ6Nh04D4zc2A5cDWAuy80s4eAlwh7HL0M3NNdX6QcmcBzT/4dR/1W1iag6iARiYeSQcDdn6V4Hnl+gfMXAMcXudY3gW9WksDulPG2IJAOAiC/TaD866hhWETioupGDCeLtAlUUhbIvlcxQEQGuqoLAtmn945tAh0z9K4mknNNICciMVFVQSBwz2X6bSWB8BZ0zM67mkw0Wx2kNYZFZKCrqiCQzuS3CXQsCbTP0H/ywtt86s7neHLF5k7XUcOwiMRFVQWBwNuqg44bOwSAkYPrCp77s1feZfG6nTy9qngQ0DgBERnoqmpRmUzgud5A177vGM46ZhRzpjQBnauDWtNh76FC1UI/WrAOUJuAiAx8VRcEshl3Q22SuVNH5o51zM9bM4WDQCoT8MAL7wDQPKqx5xIrItILqqo6KH+cQEcdp5BLpcPcP+gQBbK9iv7XhScybdzQHkiliEjvqa4gEDjJIlU4mQ6Zfa4k0OG87CCz2qSqgkRk4Ku+IFCkJJCJMvesYm0C2ZKAuoeKSBwoCESymXtWW5tA+/2p3HQTVXXrRCSmqionC9yLrgaWHTyWlSsJFDnvcGYfFRHpb6oqCGQCL5p5d2wT2H0wBXQuCRzJFNQiIv1NVQWBdF4X0ULHCum4u+OKZCIiA1lVBYGgizaBoEAQGFpf07l3UNRWUJOoqlsnIjFVVTlZxotXBxUqCYwYXNu5Oig6T11ERSQOqisIBMUbhgvtrUsmuugiWlW3TkRiKrY52ZMrNvPuroPt9nU1WOy8E8fyhfcfw5fPn5bbZ2Y4xbqIqiQgIgNfbIPA//jhYm55ZGnutbsTePFBXjXJBDd/eDojG2tz+xLWebCYuoiKSJzENgiMGlzHwdZM7nXn5SQLy58e2rBOcwel1DAsIjES25zs2Gi9gKzsOIDSQSD8XZMwrKuSgKqDRCQGYhsEjPajfbNTA5VaAyB7NJGwqE2gPQ0WE5E4iW8Q6JBHZ2f/LJl5R2+sSYSTSxfrIqrqIBGJg3jnZHn5d64kUKo6KPqdtMLVQbnBYqoOEpEYiG0QMNp377zj6dUAlMq7syWIZNJIFKgOSmmwmIjESHyDQIc8+j9f3gDAGXlLShZ8X1QWyJYEOvYOyq47oMFiIhIHsc7JOlblfP7MyZx09PAu35MrCeTaBNofT6lhWERiJLZBwKx976Cu1hfOt78lDYTvLdQ7SF1ERSROSgYBM5tkZs+Y2QozW25m10f7R5rZk2a2OvrdFO1vMrNHzOxVM1tkZjPzrjXCzB4ys1VmttLM3tNTX6zjwvGZLqaRzrd1XwsAJ08YHjUMh5m+u/P1R5Zy77NvAeodJCLxUE5OlgZudPcZwFnAl8xsBnATMN/dpwHzo9cAtwBL3P0U4Erg9rxr3Q78yt1PBE4FVnbP1ygsv3tnV9NI58tOKX3m1JFhw3B0iX0tae5f+DYHWzNcdPJRjBxc1yNpFhHpTSWDgLtvdPeXou29hBn3BOBi4L7otPuAS6LtGcDT0fmrgGYzG2dmw4H3AfdGx1rdfVc3fpd2Drc6KOoBmmsTyDYMZ6uBrnnvVP7586droXkRiYWK6jTMrBmYBSwExrn7xujQJmBctP0KcGl0/lxgCjARmApsBX5gZi+b2ffNbHCRz7nWzBab2eKtW7dW9o2KCILSo4WhLdNPdpg2Qg3CIhJHZQcBMxsC/BS4wd335B/zsN4l++B9KzDCzJYA1wEvAxmgBpgN3Onus4D9tFUhtePu97j7HHefM2bMmAq/Uv512rYDd8rJv9O5LqBG4LDgze28sHZHXoOw2gJEJD7KytHMrJYwANzv7g9Huzeb2fjo+HhgC4C773H3q9z9NMI2gTHAm8B6YL27L4ze/xBhUOgR1uGp/3Cqg15ctxOAT9+1gEOpTG6/iEhclNM7yAjr8Ve6+215hx4D5kXb84BHo/NHmFm21fQa4LdRYNgEvGNmJ0THzgdWdMN3KCpbEHB33MurDsoNButwbks63K+RwiISJzVlnHMOcAWwNKrigbAH0K3Ag2Z2NbAO+Ex0bDpwn5k5sBy4Ou9a1wH3R0HiTeCqI/8KhRnk6oPKXUsgPJeC57amNVJYROKnZBBw92cpvAQvhE/zHc9fABxf5FpLgDmVJPBw5T/Il7uWAORPC9EhCGTC6qBaVQeJSIzE+rE2Wx1U7loCAFEnoE5BoCVVODiIiAxksQ0C+fP+tJUESr+vWEmgJZNtE4jtLRORKhTbHC2/d1C2TaCcksCnTp8IwGmTRrTb/9bW/YBKAiISL+U0DA9Y2fUEstNHlBMEzjtxHGtv/Uin/X/1eNiRaWhDrG+ZiFSZ+JYEyKsOqqB3UFc+csr4TiUEEZGBLL5BIMrvd+5v5R9+9RpQemnJjo4d035Wi7OPHdVpEJqIyEAW2yAAYUlg4Vvb+ffF7zB+eAMnHT2sovff90dz+eycSbnXtRojICIxE+NcLXxgmsWGAAALc0lEQVRiT0dVQT/8o7nMntxU0RUmNjW2W45SC8mISNzEOAiE4wRyPYMOsz0gv0eoJo8TkbiJba6WXRUsNzX0Ydbl5/coqlNJQERiJr5BIPqdzhxZz6D8IKAlJUUkbmKfq2VLAodfHZQXBFQSEJGYiW0QyK4KlpsVtBuqgzRlhIjETWxzNYsqhDK5ksDhXSe/ANFQG9vbJSJVKta5muMEwZE1DOdXB506UaOFRSReYhsE2qqDjqxhOLvA/AdnjFMXURGJndjmatkH/6CCBWUKmTlhGKdOGsG17zumu5ImItJvxHpKzPzBYocbBCY2NfLol87pxlSJiPQf8S0JdGwY1sRvIiKdxDYIQDhiOHOEg8VEROIsvkHAouqgI5w2QkQkzmIbBLJZfnCEE8iJiMRZbIMAwJtb93PH02v6OhkiIv1WbINA/gpgf/nxk/owJSIi/Vd8g0D0e2hDDfPObu7LpIiI9FuxDQJZNWoLEBEpKrZBIFsblNQaACIiRZXMIc1skpk9Y2YrzGy5mV0f7R9pZk+a2erod1O0v8nMHjGzV81skZnN7HC9pJm9bGaP98xXij4n+q2SgIhIceU8JqeBG919BnAW8CUzmwHcBMx392nA/Og1wC3AEnc/BbgSuL3D9a4HVnZH4suhQWIiIsWVDALuvtHdX4q29xJm4BOAi4H7otPuAy6JtmcAT0fnrwKazWwcgJlNBD4CfL8bv0NB2d5BCgIiIsVVVGFuZs3ALGAhMM7dN0aHNgHjou1XgEuj8+cCU4CJ0bHvAF8DgiNJdCVUHSQiUlzZQcDMhgA/BW5w9z35x9zdCWdpALgVGGFmS4DrgJeBjJl9FNji7i+W8VnXmtliM1u8devWcpPY/hrRb5UERESKKysImFktYQC4390fjnZvNrPx0fHxwBYAd9/j7le5+2mEbQJjgDeBc4CPm9la4AHgPDP7f4U+z93vcfc57j5nzJgxh/fNcr2DFARERIopp3eQAfcCK939trxDjwHzou15wKPR+SPMrC7afw3w2ygw3OzuE929GbgMeNrdL++m71FUTVJBQESkmHIWlTkHuAJYGlXxQNgD6FbgQTO7GlgHfCY6Nh24z8wcWA5c3b1JLk92PQGNExARKa5kEHD3Z2mrYu/o/ALnLwCOL3HNXwO/Lp28w5cdLKaGYRGR4mL/mKw2ARGR4mIbBHK9g7SYjIhIUfENAtnqIDUMi4gUFdsgkKXqIBGR4mIbBLK9g9QwLCJSXHyDgAaLiYiUFNsgkFWjcQIiIkXFNodUSUBEpLTYBoEstQmIiBQX4yCg9QREREqJbRBQdZCISGmxDQJZCgIiIsXFNghooXkRkdJiGwS27WsB4GAq08cpERHpv2IbBHYfTAFw2qSmPk6JiEj/FdsgEERL2Tc11vZtQkRE+rHYBoGMh+veq2FYRKS42AaBdBAGAU0lLSJSXGyDQBBkSwKx/YoiIkcstjlkriSg6iARkaJiGwSyJYGElpcUESkqtkEg2zCsNgERkeJiGwTa2gQUBEREioltEMiVBBQERESKim8QUElARKSk2AcBLS8pIlJcbHPIkYPrAEjG9huKiBy5mr5OQE/5m0tm8vBLG5gyanBfJ0VEpN8q+ZxsZpPM7BkzW2Fmy83s+mj/SDN70sxWR7+bov1NZvaImb1qZovMbGZX1+kpsyY38deXzKRWRQERkaLKySHTwI3uPgM4C/iSmc0AbgLmu/s0YH70GuAWYIm7nwJcCdxe4joiItJHSgYBd9/o7i9F23uBlcAE4GLgvui0+4BLou0ZwNPR+auAZjMb18V1RESkj1RUV2JmzcAsYCEwzt03Roc2AeOi7VeAS6Pz5wJTgIldXEdERPpI2UHAzIYAPwVucPc9+cfc3QGPXt4KjDCzJcB1wMtAppzr5J1zrZktNrPFW7dureT7iIhIBcrqHWRmtYQZ9/3u/nC0e7OZjXf3jWY2HtgCEGXsV0XvM+At4M0urtOJu98D3AMwZ84cL3aeiIgcmXJ6BxlwL7DS3W/LO/QYMC/angc8Gp0/wszqov3XAL919z1dXEdERPpIOdVB5wBXAOeZ2ZLo5yLCap8PmNlq4ILoNcB0YJmZvQZ8GLi+xHVERKSPlKwOcvdngWIT8Jxf4PwFwPEVXkdERPqAuffvKncz2wqsO8y3jwa2dWNyult/Tx8ojd2hv6cP+n8a+3v6oH+lcYq7jynnxH4fBI6EmS129zl9nY5i+nv6QGnsDv09fdD/09jf0wcDI42FaE4FEZEqpiAgIlLF4h4E7unrBJTQ39MHSmN36O/pg/6fxv6ePhgYaewk1m0CIiLStbiXBEREpAuxDAJmdqGZvWZma8zsptLv6LF0VLoWg5nZHVG6XzWz2b2UzqSZvWxmj0evp5rZwigd/54dAW5m9dHrNdHx5l5K3wgze8jMVpnZSjN7T3+6h2b2lejfd5mZ/cTMGvr6HprZv5rZFjNblrev4ntmZvOi81eb2bxCn9XNafzH6N/5VQvXJRmRd+zmKI2vmdmH8vb32N97oTTmHbvRzNzMRkev++Q+HjF3j9UPkATeAI4B6ghnNZ3RR2kZD8yOtocCrxNOtf0PwE3R/puAv4+2LwJ+STio7ixgYS+l86vAj4HHo9cPApdF23cB/zPa/iJwV7R9GfDvvZS++4Brou06YER/uYeE06G/BQzKu3d/2Nf3EHgfMBtYlrevonsGjCSc92sk0BRtN/VwGj8I1ETbf5+XxhnR33I9MDX6G0/29N97oTRG+ycBTxCOYRrdl/fxiL9jXyeg278QvAd4Iu/1zcDNfZ2uKC2PAh8AXgPGR/vGA69F23cDn8s7P3deD6ZpIuGiQOcBj0f/gbfl/SHm7mf0n/490XZNdJ71cPqGR5msddjfL+4hYRB4J/oDr4nu4Yf6wz0EmjtksBXdM+BzwN15+9ud1xNp7HDsE4STTXb6O87ex974ey+URuAh4FRgLW1BoM/u45H8xLE6KPtHmbWefrB4jZW3FkNfpP07wNeAIHo9Ctjl7ukCacilLzq+Ozq/J00FtgI/iKqsvm9mg+kn99DdNwD/BLwNbCS8Jy/Sv+5hVqX3rK//lv6I8MmaLtLS62k0s4uBDe7+SodD/SaNlYhjEOh3rPy1GHo7XR8Ftrj7i33x+WWqISyO3+nus4D9tC1lCvT5PWwiXGVvKnA0MBi4sC/SUom+vGflMLOvEy5Je39fpyWfmTUSLqH7jb5OS3eJYxDYQFhflzUx2tcnrIu1GKLjubUY6P20nwN83MzWAg8QVgndTrgoUHZywfw05NIXHR8ObO/B9EH41LTe3bOr0D1EGBT6yz28AHjL3be6ewp4mPC+9qd7mFXpPeuTvyUz+0Pgo8Dno2DVn9J4LGHAfyX6u5kIvGRmR/WjNFYkjkHgBWBa1DujjrDx7bG+SIhZZWsxRPuvjHoZnAXsziu+dzt3v9ndJ7p7M+F9etrdPw88A3yqSPqy6f5UdH6PPk26+ybgHTM7Idp1PrCCfnIPCauBzjKzxujfO5u+fnMP81R6z54APmhmTVGJ54PRvh5jZhcSVk9+3N0PdEj7ZVHvqqnANGARvfz37u5L3X2suzdHfzfrCTt/bKIf3ceK9HWjRE/8ELbSv07Ya+DrfZiO3yMscr8KLIl+LiKsA54PrAaeAkZG5xvwvSjdS4E5vZjWc2nrHXQM4R/YGuA/gPpof0P0ek10/JheSttpwOLoPv4nYQ+LfnMPgb8EVgHLgB8R9mDp03sI/ISwjSJFmFFdfTj3jLBefk30c1UvpHENYf159u/lrrzzvx6l8TXgw3n7e+zvvVAaOxxfS1vDcJ/cxyP90YhhEZEqFsfqIBERKZOCgIhIFVMQEBGpYgoCIiJVTEFARKSKKQiIiFQxBQERkSqmICAiUsX+P8qEk5K5t/3fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20cbdb6f0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.pyplot(scores)\n",
    "matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The above plot shows the convergence of the model or after how many iterations Jerry learnt the best path i.e. after approximately 900 iterations."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}