Results Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "82991365",
   "metadata": {},
   "source": [
    "# Tak Player Bot Project\n",
    "## Experiments Results Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8590cd14",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47f9c86c",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"./experiments/results\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71d077fa",
   "metadata": {},
   "source": [
    "### Random Policy Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "59755594",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>board_size</th>\n",
       "      <th>starting_player</th>\n",
       "      <th>trial_number</th>\n",
       "      <th>steps</th>\n",
       "      <th>reward_for_white_player</th>\n",
       "      <th>reward_for_black_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>5</td>\n",
       "      <td>black</td>\n",
       "      <td>295</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>5</td>\n",
       "      <td>black</td>\n",
       "      <td>296</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1797</th>\n",
       "      <td>5</td>\n",
       "      <td>black</td>\n",
       "      <td>297</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1798</th>\n",
       "      <td>5</td>\n",
       "      <td>black</td>\n",
       "      <td>298</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>5</td>\n",
       "      <td>black</td>\n",
       "      <td>299</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1800 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      board_size starting_player  trial_number  steps  \\\n",
       "0              3           white             0     19   \n",
       "1              3           white             1     24   \n",
       "2              3           white             2     20   \n",
       "3              3           white             3     14   \n",
       "4              3           white             4      8   \n",
       "...          ...             ...           ...    ...   \n",
       "1795           5           black           295     37   \n",
       "1796           5           black           296     51   \n",
       "1797           5           black           297     81   \n",
       "1798           5           black           298     65   \n",
       "1799           5           black           299     61   \n",
       "\n",
       "      reward_for_white_player  reward_for_black_player  \n",
       "0                           0                        0  \n",
       "1                           0                        0  \n",
       "2                           0                        0  \n",
       "3                           0                       10  \n",
       "4                           0                       10  \n",
       "...                       ...                      ...  \n",
       "1795                        0                       26  \n",
       "1796                        0                        0  \n",
       "1797                        0                       26  \n",
       "1798                        0                        0  \n",
       "1799                        0                        0  \n",
       "\n",
       "[1800 rows x 6 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs = pd.read_csv(f\"{path}/random_eff_run_16.csv\")\n",
    "df_random_eff_runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1639d4ed",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>starting_player</th>\n",
       "      <th>trial_number</th>\n",
       "      <th>steps</th>\n",
       "      <th>reward_for_white_player</th>\n",
       "      <th>reward_for_black_player</th>\n",
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       "      <th>0</th>\n",
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       "      <td>19</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>white</td>\n",
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       "      <th>3</th>\n",
       "      <td>white</td>\n",
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       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>white</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>black</td>\n",
       "      <td>295</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>black</td>\n",
       "      <td>296</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>black</td>\n",
       "      <td>297</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>black</td>\n",
       "      <td>298</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>black</td>\n",
       "      <td>299</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    starting_player  trial_number  steps  reward_for_white_player  \\\n",
       "0             white             0     19                        0   \n",
       "1             white             1     24                        0   \n",
       "2             white             2     20                        0   \n",
       "3             white             3     14                        0   \n",
       "4             white             4      8                        0   \n",
       "..              ...           ...    ...                      ...   \n",
       "595           black           295     26                        0   \n",
       "596           black           296     23                        0   \n",
       "597           black           297     14                        0   \n",
       "598           black           298     27                        0   \n",
       "599           black           299     26                        0   \n",
       "\n",
       "     reward_for_black_player  \n",
       "0                          0  \n",
       "1                          0  \n",
       "2                          0  \n",
       "3                         10  \n",
       "4                         10  \n",
       "..                       ...  \n",
       "595                        0  \n",
       "596                       10  \n",
       "597                        0  \n",
       "598                        0  \n",
       "599                        0  \n",
       "\n",
       "[600 rows x 5 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b3 = df_random_eff_runs[df_random_eff_runs[\"board_size\"] == 3]\n",
    "df_random_eff_runs_b3 = df_random_eff_runs_b3.drop(\"board_size\", axis=1)\n",
    "df_random_eff_runs_b3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "4f6c86b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/3237180122.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b3_white = df_random_eff_runs_b3[df_random_eff_runs[\"starting_player\"] == \"white\"]\n"
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       "      <th></th>\n",
       "      <th>trial_number</th>\n",
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       "      <th>295</th>\n",
       "      <td>295</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>296</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>297</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>298</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
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       "      <th>299</th>\n",
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       "      <td>10</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "     trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "0               0     19                        0                        0\n",
       "1               1     24                        0                        0\n",
       "2               2     20                        0                        0\n",
       "3               3     14                        0                       10\n",
       "4               4      8                        0                       10\n",
       "..            ...    ...                      ...                      ...\n",
       "295           295     12                        0                       10\n",
       "296           296     11                       10                        0\n",
       "297           297     15                        0                        0\n",
       "298           298     11                       10                        0\n",
       "299           299     17                       10                        0\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b3_white = df_random_eff_runs_b3[df_random_eff_runs[\"starting_player\"] == \"white\"]\n",
    "df_random_eff_runs_b3_white = df_random_eff_runs_b3_white.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b3_white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "73f6c873",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/4179872691.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b3_black = df_random_eff_runs_b3[df_random_eff_runs[\"starting_player\"] == \"black\"]\n"
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>599</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "300             0     22                        0                        0\n",
       "301             1     14                        0                        0\n",
       "302             2     26                       10                        0\n",
       "303             3     11                        0                       10\n",
       "304             4     14                        0                        0\n",
       "..            ...    ...                      ...                      ...\n",
       "595           295     26                        0                        0\n",
       "596           296     23                        0                       10\n",
       "597           297     14                        0                        0\n",
       "598           298     27                        0                        0\n",
       "599           299     26                        0                        0\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b3_black = df_random_eff_runs_b3[df_random_eff_runs[\"starting_player\"] == \"black\"]\n",
    "df_random_eff_runs_b3_black = df_random_eff_runs_b3_black.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b3_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "09df8404",
   "metadata": {},
   "outputs": [
    {
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       "<p>600 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     starting_player  trial_number  steps  reward_for_white_player  \\\n",
       "600            white             0     21                        0   \n",
       "601            white             1     25                       17   \n",
       "602            white             2     37                        0   \n",
       "603            white             3     40                        0   \n",
       "604            white             4     41                        0   \n",
       "...              ...           ...    ...                      ...   \n",
       "1195           black           295     33                        0   \n",
       "1196           black           296     45                        0   \n",
       "1197           black           297     57                        0   \n",
       "1198           black           298     46                        0   \n",
       "1199           black           299     64                       17   \n",
       "\n",
       "      reward_for_black_player  \n",
       "600                         0  \n",
       "601                         0  \n",
       "602                         0  \n",
       "603                        17  \n",
       "604                         0  \n",
       "...                       ...  \n",
       "1195                       17  \n",
       "1196                        0  \n",
       "1197                       17  \n",
       "1198                        0  \n",
       "1199                        0  \n",
       "\n",
       "[600 rows x 5 columns]"
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     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b4 = df_random_eff_runs[df_random_eff_runs[\"board_size\"] == 4]\n",
    "df_random_eff_runs_b4 = df_random_eff_runs_b4.drop(\"board_size\", axis=1)\n",
    "df_random_eff_runs_b4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "137edf16",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/2832092955.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b4_white = df_random_eff_runs_b4[df_random_eff_runs[\"starting_player\"] == \"white\"]\n"
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      "text/plain": [
       "     trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "600             0     21                        0                        0\n",
       "601             1     25                       17                        0\n",
       "602             2     37                        0                        0\n",
       "603             3     40                        0                       17\n",
       "604             4     41                        0                        0\n",
       "..            ...    ...                      ...                      ...\n",
       "895           295     39                        0                        0\n",
       "896           296     29                        0                        0\n",
       "897           297     31                       17                        0\n",
       "898           298     44                        0                       17\n",
       "899           299     36                        0                       17\n",
       "\n",
       "[300 rows x 4 columns]"
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     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_random_eff_runs_b4_white = df_random_eff_runs_b4[df_random_eff_runs[\"starting_player\"] == \"white\"]\n",
    "df_random_eff_runs_b4_white = df_random_eff_runs_b4_white.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b4_white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "6642a7f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/1210910269.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b4_black = df_random_eff_runs_b4[df_random_eff_runs[\"starting_player\"] == \"black\"]\n"
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       "<p>300 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "      trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "900              0     46                        0                        0\n",
       "901              1     47                        0                        0\n",
       "902              2     31                        0                        0\n",
       "903              3     34                        0                        0\n",
       "904              4     21                        0                        0\n",
       "...            ...    ...                      ...                      ...\n",
       "1195           295     33                        0                       17\n",
       "1196           296     45                        0                        0\n",
       "1197           297     57                        0                       17\n",
       "1198           298     46                        0                        0\n",
       "1199           299     64                       17                        0\n",
       "\n",
       "[300 rows x 4 columns]"
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     "execution_count": 124,
     "metadata": {},
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   "source": [
    "df_random_eff_runs_b4_black = df_random_eff_runs_b4[df_random_eff_runs[\"starting_player\"] == \"black\"]\n",
    "df_random_eff_runs_b4_black = df_random_eff_runs_b4_black.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b4_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "c8856a16",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>black</td>\n",
       "      <td>299</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     starting_player  trial_number  steps  reward_for_white_player  \\\n",
       "1200           white             0     53                       26   \n",
       "1201           white             1     60                        0   \n",
       "1202           white             2     49                       26   \n",
       "1203           white             3     43                       26   \n",
       "1204           white             4     48                        0   \n",
       "...              ...           ...    ...                      ...   \n",
       "1795           black           295     37                        0   \n",
       "1796           black           296     51                        0   \n",
       "1797           black           297     81                        0   \n",
       "1798           black           298     65                        0   \n",
       "1799           black           299     61                        0   \n",
       "\n",
       "      reward_for_black_player  \n",
       "1200                        0  \n",
       "1201                       26  \n",
       "1202                        0  \n",
       "1203                        0  \n",
       "1204                        0  \n",
       "...                       ...  \n",
       "1795                       26  \n",
       "1796                        0  \n",
       "1797                       26  \n",
       "1798                        0  \n",
       "1799                        0  \n",
       "\n",
       "[600 rows x 5 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b5 = df_random_eff_runs[df_random_eff_runs[\"board_size\"] == 5]\n",
    "df_random_eff_runs_b5 = df_random_eff_runs_b5.drop(\"board_size\", axis=1)\n",
    "df_random_eff_runs_b5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7917362f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/4263729703.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b5_white = df_random_eff_runs_b5[df_random_eff_runs[\"starting_player\"] == \"white\"]\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>trial_number</th>\n",
       "      <th>steps</th>\n",
       "      <th>reward_for_white_player</th>\n",
       "      <th>reward_for_black_player</th>\n",
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       "  </thead>\n",
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       "      <td>53</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>295</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>296</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>297</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>298</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>299</td>\n",
       "      <td>59</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "1200             0     53                       26                        0\n",
       "1201             1     60                        0                       26\n",
       "1202             2     49                       26                        0\n",
       "1203             3     43                       26                        0\n",
       "1204             4     48                        0                        0\n",
       "...            ...    ...                      ...                      ...\n",
       "1495           295     64                        0                        0\n",
       "1496           296     59                        0                        0\n",
       "1497           297     80                        0                       26\n",
       "1498           298     66                        0                        0\n",
       "1499           299     59                       26                        0\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b5_white = df_random_eff_runs_b5[df_random_eff_runs[\"starting_player\"] == \"white\"]\n",
    "df_random_eff_runs_b5_white = df_random_eff_runs_b5_white.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b5_white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "a50d6349",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/2323754284.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b5_black = df_random_eff_runs_b5[df_random_eff_runs[\"starting_player\"] == \"black\"]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trial_number</th>\n",
       "      <th>steps</th>\n",
       "      <th>reward_for_white_player</th>\n",
       "      <th>reward_for_black_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1500</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1501</th>\n",
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       "      <td>0</td>\n",
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       "      <th>1502</th>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>3</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>295</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>296</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1797</th>\n",
       "      <td>297</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1798</th>\n",
       "      <td>298</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>299</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "1500             0     61                        0                        0\n",
       "1501             1     49                        0                       26\n",
       "1502             2     70                        0                        0\n",
       "1503             3     63                        0                       26\n",
       "1504             4     64                        0                        0\n",
       "...            ...    ...                      ...                      ...\n",
       "1795           295     37                        0                       26\n",
       "1796           296     51                        0                        0\n",
       "1797           297     81                        0                       26\n",
       "1798           298     65                        0                        0\n",
       "1799           299     61                        0                        0\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b5_black = df_random_eff_runs_b5[df_random_eff_runs[\"starting_player\"] == \"black\"]\n",
    "df_random_eff_runs_b5_black = df_random_eff_runs_b5_black.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b5_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7523489e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66, 75, 159)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b3_white_wins_startW = sum(df_random_eff_runs_b3_white[\"reward_for_white_player\"] > 0)\n",
    "b3_black_wins_startW = sum(df_random_eff_runs_b3_white[\"reward_for_black_player\"] > 0)\n",
    "b3_ties_startW = 300 - b3_white_wins_startW - b3_black_wins_startW\n",
    "b3_white_wins_startW, b3_black_wins_startW, b3_ties_startW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "a94b3568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67, 74, 159)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b3_white_wins_startB = sum(df_random_eff_runs_b3_black[\"reward_for_white_player\"] > 0)\n",
    "b3_black_wins_startB = sum(df_random_eff_runs_b3_black[\"reward_for_black_player\"] > 0)\n",
    "b3_ties_startB = 300 - b3_white_wins_startB - b3_black_wins_startB\n",
    "b3_white_wins_startB, b3_black_wins_startB, b3_ties_startB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "b930b7f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73, 74, 153)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b4_white_wins_startW = sum(df_random_eff_runs_b4_white[\"reward_for_white_player\"] > 0)\n",
    "b4_black_wins_startW = sum(df_random_eff_runs_b4_white[\"reward_for_black_player\"] > 0)\n",
    "b4_ties_startW = 300 - b4_white_wins_startW - b4_black_wins_startW\n",
    "b4_white_wins_startW, b4_black_wins_startW, b4_ties_startW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "1caa8646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68, 67, 165)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b4_white_wins_startB = sum(df_random_eff_runs_b4_black[\"reward_for_white_player\"] > 0)\n",
    "b4_black_wins_startB = sum(df_random_eff_runs_b4_black[\"reward_for_black_player\"] > 0)\n",
    "b4_ties_startB = 300 - b4_white_wins_startB - b4_black_wins_startB\n",
    "b4_white_wins_startB, b4_black_wins_startB, b4_ties_startB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "619635f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 78, 158)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b5_white_wins_startW = sum(df_random_eff_runs_b5_white[\"reward_for_white_player\"] > 0)\n",
    "b5_black_wins_startW = sum(df_random_eff_runs_b5_white[\"reward_for_black_player\"] > 0)\n",
    "b5_ties_startW = 300 - b5_white_wins_startW - b5_black_wins_startW\n",
    "b5_white_wins_startW, b5_black_wins_startW, b5_ties_startW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "1b4dd699",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73, 76, 151)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b5_white_wins_startB = sum(df_random_eff_runs_b5_black[\"reward_for_white_player\"] > 0)\n",
    "b5_black_wins_startB = sum(df_random_eff_runs_b5_black[\"reward_for_black_player\"] > 0)\n",
    "b5_ties_startB = 300 - b5_white_wins_startB - b5_black_wins_startB\n",
    "b5_white_wins_startB, b5_black_wins_startB, b5_ties_startB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "14725105",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_white_wins = np.array((\n",
    "    b3_white_wins_startW, b3_white_wins_startB, \n",
    "    b4_white_wins_startW, b4_white_wins_startB, \n",
    "    b5_white_wins_startW, b5_white_wins_startB\n",
    "))\n",
    "all_black_wins = np.array((\n",
    "    b3_black_wins_startW, b3_black_wins_startB,\n",
    "    b4_black_wins_startW, b4_black_wins_startB,\n",
    "    b5_black_wins_startW, b5_black_wins_startB\n",
    "))\n",
    "all_ties = np.array((\n",
    "    b3_ties_startW, b3_ties_startB, \n",
    "    b4_ties_startW, b4_ties_startB,\n",
    "    b5_ties_startW, b5_ties_startB\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "d25500fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = 0.35\n",
    "ind = np.arange(6)\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(ind, all_white_wins, width, color='r', label=\"White Wins\")\n",
    "ax.bar(ind, all_black_wins, width, bottom=all_white_wins, color='g', label=\"Black Wins\")\n",
    "ax.bar(ind, all_ties, width, bottom=all_white_wins+all_black_wins, color='b', label=\"Ties\")\n",
    "ax.set_ylabel('# Games')\n",
    "ax.set_title('Game winning/tie results for Board Size (Random Policy)')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(['3x3(W Start)', '3x3(B Start)', '4x4(W Start)', '4x4(B Start)', '5x5(W Start)', '5x5(B Start)'])\n",
    "ax.set_yticks(np.arange(0, 325, 25))\n",
    "ax.legend()\n",
    "plt.savefig(\"plot_random_policy_game_results.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7f86a9",
   "metadata": {},
   "source": [
    "### Random Take Wins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "8717a124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>board_size</th>\n",
       "      <th>starting_player</th>\n",
       "      <th>trial_number</th>\n",
       "      <th>steps</th>\n",
       "      <th>reward_for_white_player</th>\n",
       "      <th>reward_for_black_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>5</td>\n",
       "      <td>black</td>\n",
       "      <td>295</td>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>5</td>\n",
       "      <td>black</td>\n",
       "      <td>296</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1797</th>\n",
       "      <td>5</td>\n",
       "      <td>black</td>\n",
       "      <td>297</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1798</th>\n",
       "      <td>5</td>\n",
       "      <td>black</td>\n",
       "      <td>298</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>5</td>\n",
       "      <td>black</td>\n",
       "      <td>299</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1800 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      board_size starting_player  trial_number  steps  \\\n",
       "0              3           white             0      7   \n",
       "1              3           white             1     22   \n",
       "2              3           white             2      9   \n",
       "3              3           white             3     16   \n",
       "4              3           white             4     18   \n",
       "...          ...             ...           ...    ...   \n",
       "1795           5           black           295    104   \n",
       "1796           5           black           296     62   \n",
       "1797           5           black           297     64   \n",
       "1798           5           black           298     99   \n",
       "1799           5           black           299     54   \n",
       "\n",
       "      reward_for_white_player  reward_for_black_player  \n",
       "0                          10                        0  \n",
       "1                           0                        0  \n",
       "2                          10                        0  \n",
       "3                           0                       10  \n",
       "4                           0                        0  \n",
       "...                       ...                      ...  \n",
       "1795                        0                        0  \n",
       "1796                        0                        0  \n",
       "1797                        0                        0  \n",
       "1798                        0                        0  \n",
       "1799                        0                        0  \n",
       "\n",
       "[1800 rows x 6 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs = pd.read_csv(f\"{path}/random_eff_run_takewin_1.csv\")\n",
    "df_random_eff_runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "94ae7189",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>596</th>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    starting_player  trial_number  steps  reward_for_white_player  \\\n",
       "0             white             0      7                       10   \n",
       "1             white             1     22                        0   \n",
       "2             white             2      9                       10   \n",
       "3             white             3     16                        0   \n",
       "4             white             4     18                        0   \n",
       "..              ...           ...    ...                      ...   \n",
       "595           black           295     11                        0   \n",
       "596           black           296     30                        0   \n",
       "597           black           297     24                       10   \n",
       "598           black           298     14                        0   \n",
       "599           black           299     12                        0   \n",
       "\n",
       "     reward_for_black_player  \n",
       "0                          0  \n",
       "1                          0  \n",
       "2                          0  \n",
       "3                         10  \n",
       "4                          0  \n",
       "..                       ...  \n",
       "595                       10  \n",
       "596                        0  \n",
       "597                        0  \n",
       "598                        0  \n",
       "599                        0  \n",
       "\n",
       "[600 rows x 5 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b3 = df_random_eff_runs[df_random_eff_runs[\"board_size\"] == 3]\n",
    "df_random_eff_runs_b3 = df_random_eff_runs_b3.drop(\"board_size\", axis=1)\n",
    "df_random_eff_runs_b3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "aaf5532f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/3237180122.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b3_white = df_random_eff_runs_b3[df_random_eff_runs[\"starting_player\"] == \"white\"]\n"
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       "<p>300 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "     trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "0               0      7                       10                        0\n",
       "1               1     22                        0                        0\n",
       "2               2      9                       10                        0\n",
       "3               3     16                        0                       10\n",
       "4               4     18                        0                        0\n",
       "..            ...    ...                      ...                      ...\n",
       "295           295     10                        0                        0\n",
       "296           296     13                       10                        0\n",
       "297           297      9                       10                        0\n",
       "298           298     14                        0                        0\n",
       "299           299     21                        0                        0\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b3_white = df_random_eff_runs_b3[df_random_eff_runs[\"starting_player\"] == \"white\"]\n",
    "df_random_eff_runs_b3_white = df_random_eff_runs_b3_white.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b3_white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "3f0d9c2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/4179872691.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b3_black = df_random_eff_runs_b3[df_random_eff_runs[\"starting_player\"] == \"black\"]\n"
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       "<p>300 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "     trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "300             0     34                        0                        0\n",
       "301             1     12                        0                        0\n",
       "302             2     10                        0                        0\n",
       "303             3     11                        0                       10\n",
       "304             4     25                        0                       10\n",
       "..            ...    ...                      ...                      ...\n",
       "595           295     11                        0                       10\n",
       "596           296     30                        0                        0\n",
       "597           297     24                       10                        0\n",
       "598           298     14                        0                        0\n",
       "599           299     12                        0                        0\n",
       "\n",
       "[300 rows x 4 columns]"
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     "execution_count": 139,
     "metadata": {},
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    }
   ],
   "source": [
    "df_random_eff_runs_b3_black = df_random_eff_runs_b3[df_random_eff_runs[\"starting_player\"] == \"black\"]\n",
    "df_random_eff_runs_b3_black = df_random_eff_runs_b3_black.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b3_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "dcbc86d6",
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>600 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     starting_player  trial_number  steps  reward_for_white_player  \\\n",
       "600            white             0     44                        0   \n",
       "601            white             1     44                        0   \n",
       "602            white             2     37                        0   \n",
       "603            white             3     33                       17   \n",
       "604            white             4     25                       17   \n",
       "...              ...           ...    ...                      ...   \n",
       "1195           black           295     30                        0   \n",
       "1196           black           296     58                       17   \n",
       "1197           black           297     27                        0   \n",
       "1198           black           298     35                        0   \n",
       "1199           black           299     44                        0   \n",
       "\n",
       "      reward_for_black_player  \n",
       "600                         0  \n",
       "601                         0  \n",
       "602                         0  \n",
       "603                         0  \n",
       "604                         0  \n",
       "...                       ...  \n",
       "1195                        0  \n",
       "1196                        0  \n",
       "1197                       17  \n",
       "1198                       17  \n",
       "1199                        0  \n",
       "\n",
       "[600 rows x 5 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b4 = df_random_eff_runs[df_random_eff_runs[\"board_size\"] == 4]\n",
    "df_random_eff_runs_b4 = df_random_eff_runs_b4.drop(\"board_size\", axis=1)\n",
    "df_random_eff_runs_b4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "1cfd3457",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/2832092955.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b4_white = df_random_eff_runs_b4[df_random_eff_runs[\"starting_player\"] == \"white\"]\n"
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       "<p>300 rows × 4 columns</p>\n",
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       "     trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "600             0     44                        0                        0\n",
       "601             1     44                        0                        0\n",
       "602             2     37                        0                        0\n",
       "603             3     33                       17                        0\n",
       "604             4     25                       17                        0\n",
       "..            ...    ...                      ...                      ...\n",
       "895           295     49                       17                        0\n",
       "896           296     20                        0                        0\n",
       "897           297     44                        0                        0\n",
       "898           298     40                        0                        0\n",
       "899           299     27                       17                        0\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b4_white = df_random_eff_runs_b4[df_random_eff_runs[\"starting_player\"] == \"white\"]\n",
    "df_random_eff_runs_b4_white = df_random_eff_runs_b4_white.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b4_white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "456872e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/1210910269.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b4_black = df_random_eff_runs_b4[df_random_eff_runs[\"starting_player\"] == \"black\"]\n"
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      "text/plain": [
       "      trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "900              0     35                        0                       17\n",
       "901              1     48                        0                        0\n",
       "902              2     28                        0                        0\n",
       "903              3     51                        0                        0\n",
       "904              4     44                        0                        0\n",
       "...            ...    ...                      ...                      ...\n",
       "1195           295     30                        0                        0\n",
       "1196           296     58                       17                        0\n",
       "1197           297     27                        0                       17\n",
       "1198           298     35                        0                       17\n",
       "1199           299     44                        0                        0\n",
       "\n",
       "[300 rows x 4 columns]"
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     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b4_black = df_random_eff_runs_b4[df_random_eff_runs[\"starting_player\"] == \"black\"]\n",
    "df_random_eff_runs_b4_black = df_random_eff_runs_b4_black.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b4_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ec6df900",
   "metadata": {},
   "outputs": [
    {
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       "<p>600 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "     starting_player  trial_number  steps  reward_for_white_player  \\\n",
       "1200           white             0     79                        0   \n",
       "1201           white             1     75                       26   \n",
       "1202           white             2     90                        0   \n",
       "1203           white             3     72                        0   \n",
       "1204           white             4     93                       26   \n",
       "...              ...           ...    ...                      ...   \n",
       "1795           black           295    104                        0   \n",
       "1796           black           296     62                        0   \n",
       "1797           black           297     64                        0   \n",
       "1798           black           298     99                        0   \n",
       "1799           black           299     54                        0   \n",
       "\n",
       "      reward_for_black_player  \n",
       "1200                        0  \n",
       "1201                        0  \n",
       "1202                        0  \n",
       "1203                        0  \n",
       "1204                        0  \n",
       "...                       ...  \n",
       "1795                        0  \n",
       "1796                        0  \n",
       "1797                        0  \n",
       "1798                        0  \n",
       "1799                        0  \n",
       "\n",
       "[600 rows x 5 columns]"
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     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b5 = df_random_eff_runs[df_random_eff_runs[\"board_size\"] == 5]\n",
    "df_random_eff_runs_b5 = df_random_eff_runs_b5.drop(\"board_size\", axis=1)\n",
    "df_random_eff_runs_b5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "944cc472",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/4263729703.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b5_white = df_random_eff_runs_b5[df_random_eff_runs[\"starting_player\"] == \"white\"]\n"
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       "<p>300 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "      trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "1200             0     79                        0                        0\n",
       "1201             1     75                       26                        0\n",
       "1202             2     90                        0                        0\n",
       "1203             3     72                        0                        0\n",
       "1204             4     93                       26                        0\n",
       "...            ...    ...                      ...                      ...\n",
       "1495           295     78                        0                        0\n",
       "1496           296     56                        0                        0\n",
       "1497           297     88                        0                        0\n",
       "1498           298     77                        0                        0\n",
       "1499           299     52                        0                        0\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b5_white = df_random_eff_runs_b5[df_random_eff_runs[\"starting_player\"] == \"white\"]\n",
    "df_random_eff_runs_b5_white = df_random_eff_runs_b5_white.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b5_white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "f58385aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tv/5x2dppvx0q35zy5077krck6m0000gn/T/ipykernel_13952/2323754284.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_random_eff_runs_b5_black = df_random_eff_runs_b5[df_random_eff_runs[\"starting_player\"] == \"black\"]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trial_number</th>\n",
       "      <th>steps</th>\n",
       "      <th>reward_for_white_player</th>\n",
       "      <th>reward_for_black_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>3</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>4</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>295</td>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>296</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1797</th>\n",
       "      <td>297</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1798</th>\n",
       "      <td>298</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>299</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "1500             0     86                        0                        0\n",
       "1501             1     97                        0                       26\n",
       "1502             2     32                        0                        0\n",
       "1503             3    120                        0                        0\n",
       "1504             4     59                        0                        0\n",
       "...            ...    ...                      ...                      ...\n",
       "1795           295    104                        0                        0\n",
       "1796           296     62                        0                        0\n",
       "1797           297     64                        0                        0\n",
       "1798           298     99                        0                        0\n",
       "1799           299     54                        0                        0\n",
       "\n",
       "[300 rows x 4 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random_eff_runs_b5_black = df_random_eff_runs_b5[df_random_eff_runs[\"starting_player\"] == \"black\"]\n",
    "df_random_eff_runs_b5_black = df_random_eff_runs_b5_black.drop(\"starting_player\", axis=1)\n",
    "df_random_eff_runs_b5_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "4dcb5d61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(116, 34, 150)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b3_white_wins_startW = sum(df_random_eff_runs_b3_white[\"reward_for_white_player\"] > 0)\n",
    "b3_black_wins_startW = sum(df_random_eff_runs_b3_white[\"reward_for_black_player\"] > 0)\n",
    "b3_ties_startW = 300 - b3_white_wins_startW - b3_black_wins_startW\n",
    "b3_white_wins_startW, b3_black_wins_startW, b3_ties_startW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "5bac6ed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 103, 149)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b3_white_wins_startB = sum(df_random_eff_runs_b3_black[\"reward_for_white_player\"] > 0)\n",
    "b3_black_wins_startB = sum(df_random_eff_runs_b3_black[\"reward_for_black_player\"] > 0)\n",
    "b3_ties_startB = 300 - b3_white_wins_startB - b3_black_wins_startB\n",
    "b3_white_wins_startB, b3_black_wins_startB, b3_ties_startB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "d1cf2f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83, 11, 206)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b4_white_wins_startW = sum(df_random_eff_runs_b4_white[\"reward_for_white_player\"] > 0)\n",
    "b4_black_wins_startW = sum(df_random_eff_runs_b4_white[\"reward_for_black_player\"] > 0)\n",
    "b4_ties_startW = 300 - b4_white_wins_startW - b4_black_wins_startW\n",
    "b4_white_wins_startW, b4_black_wins_startW, b4_ties_startW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "fe7ee871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 94, 181)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b4_white_wins_startB = sum(df_random_eff_runs_b4_black[\"reward_for_white_player\"] > 0)\n",
    "b4_black_wins_startB = sum(df_random_eff_runs_b4_black[\"reward_for_black_player\"] > 0)\n",
    "b4_ties_startB = 300 - b4_white_wins_startB - b4_black_wins_startB\n",
    "b4_white_wins_startB, b4_black_wins_startB, b4_ties_startB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "4f2dd140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(71, 29, 200)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b5_white_wins_startW = sum(df_random_eff_runs_b5_white[\"reward_for_white_player\"] > 0)\n",
    "b5_black_wins_startW = sum(df_random_eff_runs_b5_white[\"reward_for_black_player\"] > 0)\n",
    "b5_ties_startW = 300 - b5_white_wins_startW - b5_black_wins_startW\n",
    "b5_white_wins_startW, b5_black_wins_startW, b5_ties_startW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "8dfc5793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29, 59, 212)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b5_white_wins_startB = sum(df_random_eff_runs_b5_black[\"reward_for_white_player\"] > 0)\n",
    "b5_black_wins_startB = sum(df_random_eff_runs_b5_black[\"reward_for_black_player\"] > 0)\n",
    "b5_ties_startB = 300 - b5_white_wins_startB - b5_black_wins_startB\n",
    "b5_white_wins_startB, b5_black_wins_startB, b5_ties_startB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "4064e5dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_white_wins = np.array((\n",
    "    b3_white_wins_startW, b3_white_wins_startB, \n",
    "    b4_white_wins_startW, b4_white_wins_startB, \n",
    "    b5_white_wins_startW, b5_white_wins_startB\n",
    "))\n",
    "all_black_wins = np.array((\n",
    "    b3_black_wins_startW, b3_black_wins_startB,\n",
    "    b4_black_wins_startW, b4_black_wins_startB,\n",
    "    b5_black_wins_startW, b5_black_wins_startB\n",
    "))\n",
    "all_ties = np.array((\n",
    "    b3_ties_startW, b3_ties_startB, \n",
    "    b4_ties_startW, b4_ties_startB,\n",
    "    b5_ties_startW, b5_ties_startB\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "06a7df39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = 0.35\n",
    "ind = np.arange(6)\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(ind, all_white_wins, width, color='r', label=\"White Wins\")\n",
    "ax.bar(ind, all_black_wins, width, bottom=all_white_wins, color='g', label=\"Black Wins\")\n",
    "ax.bar(ind, all_ties, width, bottom=all_white_wins+all_black_wins, color='b', label=\"Ties\")\n",
    "ax.set_ylabel('# Games')\n",
    "ax.set_title('Game winning/tie results for Board Size (Random Policy Take Wins)')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(['3x3(W Start)', '3x3(B Start)', '4x4(W Start)', '4x4(B Start)', '5x5(W Start)', '5x5(B Start)'])\n",
    "ax.set_yticks(np.arange(0, 325, 25))\n",
    "ax.legend()\n",
    "plt.savefig(\"plot_random_policy_take_win_game_results.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aae4c676",
   "metadata": {},
   "source": [
    "### MCTS Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "66ab06d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mcts_expansion_epsilon</th>\n",
       "      <th>rollout_runs</th>\n",
       "      <th>trial_number</th>\n",
       "      <th>steps</th>\n",
       "      <th>reward_for_white_player</th>\n",
       "      <th>reward_for_black_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>95</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>96</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>97</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>98</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>99</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mcts_expansion_epsilon  rollout_runs  trial_number  steps  \\\n",
       "0                       0.9             1             0     16   \n",
       "1                       0.9             1             1     10   \n",
       "2                       0.9             1             2     14   \n",
       "3                       0.9             1             3     13   \n",
       "4                       0.9             1             4     17   \n",
       "..                      ...           ...           ...    ...   \n",
       "595                     1.0            32            95     12   \n",
       "596                     1.0            32            96     12   \n",
       "597                     1.0            32            97     17   \n",
       "598                     1.0            32            98     10   \n",
       "599                     1.0            32            99     13   \n",
       "\n",
       "     reward_for_white_player  reward_for_black_player  \n",
       "0                          0                        0  \n",
       "1                          0                        0  \n",
       "2                          0                       10  \n",
       "3                         10                        0  \n",
       "4                         10                        0  \n",
       "..                       ...                      ...  \n",
       "595                        0                        0  \n",
       "596                        0                        0  \n",
       "597                       10                        0  \n",
       "598                        0                        0  \n",
       "599                       10                        0  \n",
       "\n",
       "[600 rows x 6 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mcts_vs_rand_runs = pd.read_csv(f\"{path}/mcts_vs_randeff_run_3.csv\")\n",
    "df_mcts_vs_rand_runs = df_mcts_vs_rand_runs.drop(\"board_size\", axis=1)\n",
    "df_mcts_vs_rand_runs = df_mcts_vs_rand_runs.drop(\"mcts_expansion_depth\", axis=1)\n",
    "df_mcts_vs_rand_runs = df_mcts_vs_rand_runs.drop(\"mcts_iterations\", axis=1)\n",
    "df_mcts_vs_rand_runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "ee4123b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mcts_vs_rand_runs_eps1 = df_mcts_vs_rand_runs[df_mcts_vs_rand_runs[\"mcts_expansion_epsilon\"] == 1]\n",
    "df_mcts_vs_rand_runs_eps1_rr01 = df_mcts_vs_rand_runs_eps1[df_mcts_vs_rand_runs_eps1[\"rollout_runs\"] == 1]\n",
    "df_mcts_vs_rand_runs_eps1_rr08 = df_mcts_vs_rand_runs_eps1[df_mcts_vs_rand_runs_eps1[\"rollout_runs\"] == 8]\n",
    "df_mcts_vs_rand_runs_eps1_rr32 = df_mcts_vs_rand_runs_eps1[df_mcts_vs_rand_runs_eps1[\"rollout_runs\"] == 32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "39d8b7f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mcts_vs_rand_runs_eps09 = df_mcts_vs_rand_runs[df_mcts_vs_rand_runs[\"mcts_expansion_epsilon\"] == 0.9]\n",
    "df_mcts_vs_rand_runs_eps09_rr01 = df_mcts_vs_rand_runs_eps09[df_mcts_vs_rand_runs_eps09[\"rollout_runs\"] == 1]\n",
    "df_mcts_vs_rand_runs_eps09_rr08 = df_mcts_vs_rand_runs_eps09[df_mcts_vs_rand_runs_eps09[\"rollout_runs\"] == 8]\n",
    "df_mcts_vs_rand_runs_eps09_rr32 = df_mcts_vs_rand_runs_eps09[df_mcts_vs_rand_runs_eps09[\"rollout_runs\"] == 32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "5f323a9e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "x = np.arange(0, 100, 1)\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps1_rr01[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"e=1.0, r=1\", color=\"r\")\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps1_rr08[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"e=1.0, r=8\", color=\"g\")\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps1_rr32[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"e=1.0, r=32\", color=\"b\")\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps09_rr01[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"e=0.9, r=1\", color=\"orange\")\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps09_rr08[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"e=0.9, r=8\", color=\"pink\")\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps09_rr32[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"e=0.9, r=32\", color=\"purple\")\n",
    "\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps1_rr01[\"reward_for_black_player\"])\n",
    "plt.plot(x, y, linestyle=\"-.\", color=\"r\")\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps1_rr08[\"reward_for_black_player\"])\n",
    "plt.plot(x, y, linestyle=\"-.\", color=\"g\")\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps1_rr32[\"reward_for_black_player\"])\n",
    "plt.plot(x, y, linestyle=\"-.\", color=\"b\")\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps09_rr01[\"reward_for_black_player\"])\n",
    "plt.plot(x, y, linestyle=\"-.\", color=\"orange\")\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps09_rr08[\"reward_for_black_player\"])\n",
    "plt.plot(x, y, linestyle=\"-.\", color=\"pink\")\n",
    "y = np.cumsum(df_mcts_vs_rand_runs_eps09_rr32[\"reward_for_black_player\"])\n",
    "plt.plot(x, y, linestyle=\"-.\", color=\"purple\")\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"# Games\")\n",
    "plt.ylabel(\"Cummulative Reward for White (-)  & Black (-.-) Players\")\n",
    "plt.ylim([-50, 650])\n",
    "plt.title(\"Comparison of Explorations and Rollout Count Hyperparamters for MCTS vs Random Policy\")\n",
    "plt.savefig(\"plot_mcts_vs_random.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "91a8564f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64 4 9932\n"
     ]
    }
   ],
   "source": [
    "mcts_vs_rand_eps09_rr01_white_wins = sum(df_mcts_vs_rand_runs_eps09_rr32[\"reward_for_white_player\"] > 0)\n",
    "mcts_vs_rand_eps09_rr01_black_wins = sum(df_mcts_vs_rand_runs_eps09_rr32[\"reward_for_black_player\"] > 0)\n",
    "mcts_vs_rand_eps09_rr01_ties = 10000 - mcts_vs_rand_eps09_rr01_white_wins - mcts_vs_rand_eps09_rr01_black_wins\n",
    "print(mcts_vs_rand_eps09_rr01_white_wins, mcts_vs_rand_eps09_rr01_black_wins, mcts_vs_rand_eps09_rr01_ties)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc82960",
   "metadata": {},
   "source": [
    "# MCTS vs MCTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "ad3d223d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rollout_runs</th>\n",
       "      <th>rollout_policy</th>\n",
       "      <th>trial_number</th>\n",
       "      <th>steps</th>\n",
       "      <th>game_time</th>\n",
       "      <th>reward_for_white_player</th>\n",
       "      <th>reward_for_black_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>12583289000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>11139635000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>23072832000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>20134742000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>11216589000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>14150515000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>18254229000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>10825787000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>16846315000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>23486675000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>41177882000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>46849572000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>42901182000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>39164173000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>47357755000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>71742577000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>37225322000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>35292678000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>56895929000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>39718096000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>16</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>73179495000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>51067091000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>48067066000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>45279255000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>69879619000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>48146231000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>48333283000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>78781281000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>33777619000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>53114761000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>25937288000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>18474125000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>38094767000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>28330028000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>30463729000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>19430632000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>62657818000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>29128576000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>25312538000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>36895302000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>32</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>67415319000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>32</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>44916782000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>32</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>81592531000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>32</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>54126028000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>32</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>45867299000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>32</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>62841099000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>32</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>73883877000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>32</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>43109705000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>32</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>49295654000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>32</td>\n",
       "      <td>random_take_win</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>58026299000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>32</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>120614208000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>32</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>108233280000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>32</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>113230420000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>32</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>111729817000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>32</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>124607361000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>32</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>135935629000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>32</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>151243449000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>32</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>112876691000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>32</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>139590894000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>32</td>\n",
       "      <td>egreedy-sarsa</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>123089539000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rollout_runs   rollout_policy  trial_number  steps     game_time  \\\n",
       "0             16           random             0     13   12583289000   \n",
       "1             16           random             1     14   11139635000   \n",
       "2             16           random             2     21   23072832000   \n",
       "3             16           random             3     17   20134742000   \n",
       "4             16           random             4      9   11216589000   \n",
       "5             16           random             5     18   14150515000   \n",
       "6             16           random             6     17   18254229000   \n",
       "7             16           random             7      9   10825787000   \n",
       "8             16           random             8     16   16846315000   \n",
       "9             16           random             9     19   23486675000   \n",
       "10            16  random_take_win             0     17   41177882000   \n",
       "11            16  random_take_win             1     19   46849572000   \n",
       "12            16  random_take_win             2     17   42901182000   \n",
       "13            16  random_take_win             3     21   39164173000   \n",
       "14            16  random_take_win             4     16   47357755000   \n",
       "15            16  random_take_win             5     25   71742577000   \n",
       "16            16  random_take_win             6     12   37225322000   \n",
       "17            16  random_take_win             7     11   35292678000   \n",
       "18            16  random_take_win             8     21   56895929000   \n",
       "19            16  random_take_win             9     16   39718096000   \n",
       "20            16    egreedy-sarsa             0     18   73179495000   \n",
       "21            16    egreedy-sarsa             1      9   51067091000   \n",
       "22            16    egreedy-sarsa             2     11   48067066000   \n",
       "23            16    egreedy-sarsa             3     10   45279255000   \n",
       "24            16    egreedy-sarsa             4     15   69879619000   \n",
       "25            16    egreedy-sarsa             5     12   48146231000   \n",
       "26            16    egreedy-sarsa             6     12   48333283000   \n",
       "27            16    egreedy-sarsa             7     18   78781281000   \n",
       "28            16    egreedy-sarsa             8      9   33777619000   \n",
       "29            16    egreedy-sarsa             9     17   53114761000   \n",
       "30            32           random             0     22   25937288000   \n",
       "31            32           random             1     10   18474125000   \n",
       "32            32           random             2     21   38094767000   \n",
       "33            32           random             3     15   28330028000   \n",
       "34            32           random             4     14   30463729000   \n",
       "35            32           random             5      9   19430632000   \n",
       "36            32           random             6     26   62657818000   \n",
       "37            32           random             7     11   29128576000   \n",
       "38            32           random             8     10   25312538000   \n",
       "39            32           random             9     14   36895302000   \n",
       "40            32  random_take_win             0     25   67415319000   \n",
       "41            32  random_take_win             1     10   44916782000   \n",
       "42            32  random_take_win             2     19   81592531000   \n",
       "43            32  random_take_win             3     11   54126028000   \n",
       "44            32  random_take_win             4     11   45867299000   \n",
       "45            32  random_take_win             5     22   62841099000   \n",
       "46            32  random_take_win             6     17   73883877000   \n",
       "47            32  random_take_win             7      9   43109705000   \n",
       "48            32  random_take_win             8     12   49295654000   \n",
       "49            32  random_take_win             9     13   58026299000   \n",
       "50            32    egreedy-sarsa             0     15  120614208000   \n",
       "51            32    egreedy-sarsa             1     13  108233280000   \n",
       "52            32    egreedy-sarsa             2     17  113230420000   \n",
       "53            32    egreedy-sarsa             3     13  111729817000   \n",
       "54            32    egreedy-sarsa             4     15  124607361000   \n",
       "55            32    egreedy-sarsa             5     15  135935629000   \n",
       "56            32    egreedy-sarsa             6     15  151243449000   \n",
       "57            32    egreedy-sarsa             7     12  112876691000   \n",
       "58            32    egreedy-sarsa             8     13  139590894000   \n",
       "59            32    egreedy-sarsa             9     12  123089539000   \n",
       "\n",
       "    reward_for_white_player  reward_for_black_player  \n",
       "0                        10                        0  \n",
       "1                         0                        0  \n",
       "2                        10                        0  \n",
       "3                        10                        0  \n",
       "4                        10                        0  \n",
       "5                         0                        0  \n",
       "6                        10                        0  \n",
       "7                        10                        0  \n",
       "8                         0                        0  \n",
       "9                         0                        0  \n",
       "10                       10                        0  \n",
       "11                       10                        0  \n",
       "12                       10                        0  \n",
       "13                       10                        0  \n",
       "14                        0                        0  \n",
       "15                       10                        0  \n",
       "16                        0                        0  \n",
       "17                       10                        0  \n",
       "18                       10                        0  \n",
       "19                        0                        0  \n",
       "20                        0                        0  \n",
       "21                       10                        0  \n",
       "22                       10                        0  \n",
       "23                        0                        0  \n",
       "24                       10                        0  \n",
       "25                        0                        0  \n",
       "26                        0                        0  \n",
       "27                        0                        0  \n",
       "28                       10                        0  \n",
       "29                       10                        0  \n",
       "30                        0                        0  \n",
       "31                        0                        0  \n",
       "32                       10                        0  \n",
       "33                       10                        0  \n",
       "34                        0                        0  \n",
       "35                       10                        0  \n",
       "36                        0                        0  \n",
       "37                       10                        0  \n",
       "38                        0                        0  \n",
       "39                        0                        0  \n",
       "40                       10                        0  \n",
       "41                        0                        0  \n",
       "42                       10                        0  \n",
       "43                       10                        0  \n",
       "44                       10                        0  \n",
       "45                        0                        0  \n",
       "46                       10                        0  \n",
       "47                       10                        0  \n",
       "48                        0                        0  \n",
       "49                        0                        0  \n",
       "50                       10                        0  \n",
       "51                       10                        0  \n",
       "52                       10                        0  \n",
       "53                       10                        0  \n",
       "54                       10                        0  \n",
       "55                       10                        0  \n",
       "56                       10                        0  \n",
       "57                        0                        0  \n",
       "58                       10                        0  \n",
       "59                        0                        0  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mcts_vs_mcts = pd.read_csv(f\"{path}/mcts_with_sarsa_run2.csv\")\n",
    "df_mcts_vs_mcts = df_mcts_vs_mcts.drop(\"board_size\", axis=1)\n",
    "df_mcts_vs_mcts = df_mcts_vs_mcts.drop(\"mcts_expansion_depth\", axis=1)\n",
    "df_mcts_vs_mcts = df_mcts_vs_mcts.drop(\"mcts_expansion_epsilon\", axis=1)\n",
    "df_mcts_vs_mcts = df_mcts_vs_mcts.drop(\"mcts_iterations\", axis=1)\n",
    "df_mcts_vs_mcts = df_mcts_vs_mcts.drop(\"starting_player\", axis=1)\n",
    "df_mcts_vs_mcts = df_mcts_vs_mcts.drop(\"sarsa_epsilon\", axis=1)\n",
    "df_mcts_vs_mcts = df_mcts_vs_mcts.drop(\"sarsa_alpha\", axis=1)\n",
    "df_mcts_vs_mcts = df_mcts_vs_mcts.drop(\"sarsa_gamma\", axis=1)\n",
    "df_mcts_vs_mcts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "2f3032a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mcts_vs_mcts_rr16 = df_mcts_vs_mcts[df_mcts_vs_mcts[\"rollout_runs\"] == 16]\n",
    "df_mcts_vs_mcts_rr16 = df_mcts_vs_mcts_rr16.drop(\"rollout_runs\", axis=1)\n",
    "df_mcts_vs_mcts_rr32 = df_mcts_vs_mcts[df_mcts_vs_mcts[\"rollout_runs\"] == 32]\n",
    "df_mcts_vs_mcts_rr32 = df_mcts_vs_mcts_rr32.drop(\"rollout_runs\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "c8172d4a",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   trial_number  steps    game_time  reward_for_white_player  \\\n",
       "0             0     13  12583289000                       10   \n",
       "1             1     14  11139635000                        0   \n",
       "2             2     21  23072832000                       10   \n",
       "3             3     17  20134742000                       10   \n",
       "4             4      9  11216589000                       10   \n",
       "5             5     18  14150515000                        0   \n",
       "6             6     17  18254229000                       10   \n",
       "7             7      9  10825787000                       10   \n",
       "8             8     16  16846315000                        0   \n",
       "9             9     19  23486675000                        0   \n",
       "\n",
       "   reward_for_black_player  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  \n",
       "5                        0  \n",
       "6                        0  \n",
       "7                        0  \n",
       "8                        0  \n",
       "9                        0  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mcts_vs_mcts_rr16_rprandom = df_mcts_vs_mcts_rr16[df_mcts_vs_mcts_rr16[\"rollout_policy\"] == \"random\"]\n",
    "df_mcts_vs_mcts_rr16_rprandom = df_mcts_vs_mcts_rr16_rprandom.drop(\"rollout_policy\", axis=1)\n",
    "df_mcts_vs_mcts_rr16_rprandom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "5ef03203",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    trial_number  steps    game_time  reward_for_white_player  \\\n",
       "10             0     17  41177882000                       10   \n",
       "11             1     19  46849572000                       10   \n",
       "12             2     17  42901182000                       10   \n",
       "13             3     21  39164173000                       10   \n",
       "14             4     16  47357755000                        0   \n",
       "15             5     25  71742577000                       10   \n",
       "16             6     12  37225322000                        0   \n",
       "17             7     11  35292678000                       10   \n",
       "18             8     21  56895929000                       10   \n",
       "19             9     16  39718096000                        0   \n",
       "\n",
       "    reward_for_black_player  \n",
       "10                        0  \n",
       "11                        0  \n",
       "12                        0  \n",
       "13                        0  \n",
       "14                        0  \n",
       "15                        0  \n",
       "16                        0  \n",
       "17                        0  \n",
       "18                        0  \n",
       "19                        0  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mcts_vs_mcts_rr16_rprandwin = df_mcts_vs_mcts_rr16[df_mcts_vs_mcts_rr16[\"rollout_policy\"] == \"random_take_win\"]\n",
    "df_mcts_vs_mcts_rr16_rprandwin = df_mcts_vs_mcts_rr16_rprandwin.drop(\"rollout_policy\", axis=1)\n",
    "df_mcts_vs_mcts_rr16_rprandwin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "b0d542ab",
   "metadata": {},
   "outputs": [
    {
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       "      <td>9</td>\n",
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       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>53114761000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    trial_number  steps    game_time  reward_for_white_player  \\\n",
       "20             0     18  73179495000                        0   \n",
       "21             1      9  51067091000                       10   \n",
       "22             2     11  48067066000                       10   \n",
       "23             3     10  45279255000                        0   \n",
       "24             4     15  69879619000                       10   \n",
       "25             5     12  48146231000                        0   \n",
       "26             6     12  48333283000                        0   \n",
       "27             7     18  78781281000                        0   \n",
       "28             8      9  33777619000                       10   \n",
       "29             9     17  53114761000                       10   \n",
       "\n",
       "    reward_for_black_player  \n",
       "20                        0  \n",
       "21                        0  \n",
       "22                        0  \n",
       "23                        0  \n",
       "24                        0  \n",
       "25                        0  \n",
       "26                        0  \n",
       "27                        0  \n",
       "28                        0  \n",
       "29                        0  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mcts_vs_mcts_rr16_rpsarsa = df_mcts_vs_mcts_rr16[df_mcts_vs_mcts_rr16[\"rollout_policy\"] == \"egreedy-sarsa\"]\n",
    "df_mcts_vs_mcts_rr16_rpsarsa = df_mcts_vs_mcts_rr16_rpsarsa.drop(\"rollout_policy\", axis=1)\n",
    "df_mcts_vs_mcts_rr16_rpsarsa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "607bd48a",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    trial_number  steps    game_time  reward_for_white_player  \\\n",
       "30             0     22  25937288000                        0   \n",
       "31             1     10  18474125000                        0   \n",
       "32             2     21  38094767000                       10   \n",
       "33             3     15  28330028000                       10   \n",
       "34             4     14  30463729000                        0   \n",
       "35             5      9  19430632000                       10   \n",
       "36             6     26  62657818000                        0   \n",
       "37             7     11  29128576000                       10   \n",
       "38             8     10  25312538000                        0   \n",
       "39             9     14  36895302000                        0   \n",
       "\n",
       "    reward_for_black_player  \n",
       "30                        0  \n",
       "31                        0  \n",
       "32                        0  \n",
       "33                        0  \n",
       "34                        0  \n",
       "35                        0  \n",
       "36                        0  \n",
       "37                        0  \n",
       "38                        0  \n",
       "39                        0  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mcts_vs_mcts_rr32_rprandom = df_mcts_vs_mcts_rr32[df_mcts_vs_mcts_rr32[\"rollout_policy\"] == \"random\"]\n",
    "df_mcts_vs_mcts_rr32_rprandom = df_mcts_vs_mcts_rr32_rprandom.drop(\"rollout_policy\", axis=1)\n",
    "df_mcts_vs_mcts_rr32_rprandom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "2786d195",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    trial_number  steps    game_time  reward_for_white_player  \\\n",
       "40             0     25  67415319000                       10   \n",
       "41             1     10  44916782000                        0   \n",
       "42             2     19  81592531000                       10   \n",
       "43             3     11  54126028000                       10   \n",
       "44             4     11  45867299000                       10   \n",
       "45             5     22  62841099000                        0   \n",
       "46             6     17  73883877000                       10   \n",
       "47             7      9  43109705000                       10   \n",
       "48             8     12  49295654000                        0   \n",
       "49             9     13  58026299000                        0   \n",
       "\n",
       "    reward_for_black_player  \n",
       "40                        0  \n",
       "41                        0  \n",
       "42                        0  \n",
       "43                        0  \n",
       "44                        0  \n",
       "45                        0  \n",
       "46                        0  \n",
       "47                        0  \n",
       "48                        0  \n",
       "49                        0  "
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mcts_vs_mcts_rr32_rprandwin = df_mcts_vs_mcts_rr32[df_mcts_vs_mcts_rr32[\"rollout_policy\"] == \"random_take_win\"]\n",
    "df_mcts_vs_mcts_rr32_rprandwin = df_mcts_vs_mcts_rr32_rprandwin.drop(\"rollout_policy\", axis=1)\n",
    "df_mcts_vs_mcts_rr32_rprandwin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "6ea114f2",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "    trial_number  steps     game_time  reward_for_white_player  \\\n",
       "50             0     15  120614208000                       10   \n",
       "51             1     13  108233280000                       10   \n",
       "52             2     17  113230420000                       10   \n",
       "53             3     13  111729817000                       10   \n",
       "54             4     15  124607361000                       10   \n",
       "55             5     15  135935629000                       10   \n",
       "56             6     15  151243449000                       10   \n",
       "57             7     12  112876691000                        0   \n",
       "58             8     13  139590894000                       10   \n",
       "59             9     12  123089539000                        0   \n",
       "\n",
       "    reward_for_black_player  \n",
       "50                        0  \n",
       "51                        0  \n",
       "52                        0  \n",
       "53                        0  \n",
       "54                        0  \n",
       "55                        0  \n",
       "56                        0  \n",
       "57                        0  \n",
       "58                        0  \n",
       "59                        0  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mcts_vs_mcts_rr32_rpsarsa = df_mcts_vs_mcts_rr32[df_mcts_vs_mcts_rr32[\"rollout_policy\"] == \"egreedy-sarsa\"]\n",
    "df_mcts_vs_mcts_rr32_rpsarsa = df_mcts_vs_mcts_rr32_rpsarsa.drop(\"rollout_policy\", axis=1)\n",
    "df_mcts_vs_mcts_rr32_rpsarsa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "2b37defd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "x = np.arange(0, 10, 1)\n",
    "y = np.cumsum(df_mcts_vs_mcts_rr16_rprandom[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"r=16, p=random\", color=\"r\")\n",
    "y = np.cumsum(df_mcts_vs_mcts_rr16_rprandwin[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"r=16, p=randwin\", color=\"g\")\n",
    "y = np.cumsum(df_mcts_vs_mcts_rr16_rpsarsa[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"r=16, p=sarsa\", color=\"b\")\n",
    "y = np.cumsum(df_mcts_vs_mcts_rr32_rprandom[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"r=32, p=random\", color=\"orange\")\n",
    "y = np.cumsum(df_mcts_vs_mcts_rr32_rprandwin[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"r=32, p=randwin\", color=\"pink\")\n",
    "y = np.cumsum(df_mcts_vs_mcts_rr32_rpsarsa[\"reward_for_white_player\"])\n",
    "plt.plot(x, y, label=\"r=32, p=sarsa\", color=\"purple\")\n",
    "\n",
    "plt.legend() \n",
    "plt.xlabel(\"# Games\")\n",
    "plt.ylabel(\"Cummulative Reward for White Player\")\n",
    "plt.ylim([0, 100])\n",
    "plt.title(\"Comparison of Rollout Policies for MCTS\")\n",
    "plt.savefig(\"plot_mcts_vs_mcts_rollout_policies_comp.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27dc9f71",
   "metadata": {},
   "source": [
    "### Avg Time To select action on each Game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "1513dff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "conv_to_sec = 1e9\n",
    "\n",
    "x = np.arange(0, 10, 1)\n",
    "y = (df_mcts_vs_mcts_rr16_rprandom[\"game_time\"] / df_mcts_vs_mcts_rr16_rprandom[\"steps\"]) / conv_to_sec\n",
    "plt.plot(x, y, label=\"r=16, p=random\", color=\"r\")\n",
    "y = (df_mcts_vs_mcts_rr16_rprandwin[\"game_time\"] / df_mcts_vs_mcts_rr16_rprandwin[\"steps\"]) / conv_to_sec\n",
    "plt.plot(x, y, label=\"r=16, p=randwin\", color=\"g\")\n",
    "y = (df_mcts_vs_mcts_rr16_rpsarsa[\"game_time\"] / df_mcts_vs_mcts_rr16_rpsarsa[\"steps\"]) / conv_to_sec\n",
    "plt.plot(x, y, label=\"r=16, p=sarsa\", color=\"b\")\n",
    "y = (df_mcts_vs_mcts_rr32_rprandom[\"game_time\"] / df_mcts_vs_mcts_rr32_rprandom[\"steps\"]) / conv_to_sec\n",
    "plt.plot(x, y, label=\"r=32, p=random\", color=\"orange\")\n",
    "y = (df_mcts_vs_mcts_rr32_rprandwin[\"game_time\"] / df_mcts_vs_mcts_rr32_rprandwin[\"steps\"]) / conv_to_sec\n",
    "plt.plot(x, y, label=\"r=32, p=randwin\", color=\"pink\")\n",
    "y = (df_mcts_vs_mcts_rr32_rpsarsa[\"game_time\"] / df_mcts_vs_mcts_rr32_rpsarsa[\"steps\"]) / conv_to_sec\n",
    "plt.plot(x, y, label=\"r=32, p=sarsa\", color=\"purple\")\n",
    "\n",
    "plt.legend() \n",
    "plt.xlabel(\"Game #\")\n",
    "plt.ylabel(\"Average Seconds to select next Action\")\n",
    "plt.title(\"Comparison of Rollout Policies for MCTS - Timing\")\n",
    "plt.savefig(\"plot_mcts_vs_mcts_rollout_policies_timing_comp.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa63cba",
   "metadata": {},
   "source": [
    "### MCTS vs RANDOM POLICY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "453da45f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trial_number</th>\n",
       "      <th>steps</th>\n",
       "      <th>reward_for_white_player</th>\n",
       "      <th>reward_for_black_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    trial_number  steps  reward_for_white_player  reward_for_black_player\n",
       "0              0     16                        0                        0\n",
       "1              1     17                        0                        0\n",
       "2              2     15                        0                        0\n",
       "3              3     15                        0                        0\n",
       "4              4     20                        0                        0\n",
       "5              5     16                        0                        0\n",
       "6              6     14                        0                        0\n",
       "7              7     18                        0                        0\n",
       "8              8     39                       10                        0\n",
       "9              9     13                       10                        0\n",
       "10            10     24                        0                        0\n",
       "11            11     12                        0                        0\n",
       "12            12     17                       10                        0\n",
       "13            13     17                       10                        0\n",
       "14            14     21                       10                        0\n",
       "15            15     21                       10                        0\n",
       "16            16     17                       10                        0\n",
       "17            17     23                       10                        0\n",
       "18            18     19                       10                        0\n",
       "19            19     19                       10                        0\n",
       "20            20     21                        0                        0\n",
       "21            21     35                        0                        0\n",
       "22            22     17                       10                        0\n",
       "23            23     19                       10                        0\n",
       "24            24     18                        0                        0\n",
       "25            25     16                        0                        0\n",
       "26            26     25                       10                        0\n",
       "27            27     13                       10                        0\n",
       "28            28     16                        0                        0\n",
       "29            29     16                        0                        0\n",
       "30            30     12                        0                        0\n",
       "31            31     13                       10                        0\n",
       "32            32     18                        0                        0\n",
       "33            33     11                       10                        0\n",
       "34            34     13                        0                        0\n",
       "35            35     20                        0                        0\n",
       "36            36     16                        0                        0\n",
       "37            37     30                        0                        0\n",
       "38            38     17                       10                        0\n",
       "39            39     19                       10                        0\n",
       "40            40     18                        0                        0\n",
       "41            41     25                       10                        0\n",
       "42            42     18                        0                        0\n",
       "43            43     23                       10                        0\n",
       "44            44     15                        0                        0\n",
       "45            45     17                       10                        0\n",
       "46            46     21                       10                        0\n",
       "47            47     29                       10                        0\n",
       "48            48     12                        0                        0\n",
       "49            49     18                        0                        0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mcts_vs_rand = pd.read_csv(f\"{path}/mcts_with_sarsa_vs_mcts_rand_run2.csv\")\n",
    "df_mcts_vs_rand = df_mcts_vs_rand.drop(\"board_size\", axis=1)\n",
    "df_mcts_vs_rand = df_mcts_vs_rand.drop(\"mcts_expansion_depth\", axis=1)\n",
    "df_mcts_vs_rand = df_mcts_vs_rand.drop(\"mcts_expansion_epsilon\", axis=1)\n",
    "df_mcts_vs_rand = df_mcts_vs_rand.drop(\"mcts_iterations\", axis=1)\n",
    "df_mcts_vs_rand = df_mcts_vs_rand.drop(\"rollout_runs\", axis=1)\n",
    "df_mcts_vs_rand = df_mcts_vs_rand.drop(\"starting_player\", axis=1)\n",
    "df_mcts_vs_rand = df_mcts_vs_rand.drop(\"sarsa_epsilon\", axis=1)\n",
    "df_mcts_vs_rand = df_mcts_vs_rand.drop(\"sarsa_alpha\", axis=1)\n",
    "df_mcts_vs_rand = df_mcts_vs_rand.drop(\"sarsa_gamma\", axis=1)\n",
    "df_mcts_vs_rand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "17705923",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WHITE WINS: 23\n",
      "BLACK WINS: 0\n",
      "TIES: 27\n"
     ]
    }
   ],
   "source": [
    "ww = sum(df_mcts_vs_rand[\"reward_for_white_player\"] > 0)\n",
    "bw = sum(df_mcts_vs_rand[\"reward_for_black_player\"] > 0)\n",
    "print(\"WHITE WINS:\", ww)\n",
    "print(\"BLACK WINS:\", bw)\n",
    "print(\"TIES:\", 50 - ww - bw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa0da9f",
   "metadata": {},
   "source": [
    "### SARSA POLICY ITERATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3c5d0d0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>board_size</th>\n",
       "      <th>epsilon</th>\n",
       "      <th>gamma</th>\n",
       "      <th>alpha</th>\n",
       "      <th>starting_player</th>\n",
       "      <th>episode</th>\n",
       "      <th>trial_number</th>\n",
       "      <th>steps</th>\n",
       "      <th>reward_for_white_player</th>\n",
       "      <th>reward_for_black_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>white</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>white</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>white</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>white</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>white</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44995</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>white</td>\n",
       "      <td>995</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44996</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>white</td>\n",
       "      <td>996</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44997</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>white</td>\n",
       "      <td>997</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44998</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>white</td>\n",
       "      <td>998</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44999</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>white</td>\n",
       "      <td>999</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       board_size  epsilon  gamma  alpha starting_player  episode  \\\n",
       "0               3      0.1   0.99   0.99           white        0   \n",
       "1               3      0.1   0.99   0.99           white        1   \n",
       "2               3      0.1   0.99   0.99           white        2   \n",
       "3               3      0.1   0.99   0.99           white        3   \n",
       "4               3      0.1   0.99   0.99           white        4   \n",
       "...           ...      ...    ...    ...             ...      ...   \n",
       "44995           3      0.1   0.99   0.99           white      995   \n",
       "44996           3      0.1   0.99   0.99           white      996   \n",
       "44997           3      0.1   0.99   0.99           white      997   \n",
       "44998           3      0.1   0.99   0.99           white      998   \n",
       "44999           3      0.1   0.99   0.99           white      999   \n",
       "\n",
       "       trial_number  steps  reward_for_white_player  reward_for_black_player  \n",
       "0                 0     18                        0                       10  \n",
       "1                 0     11                        0                        0  \n",
       "2                 0     12                        0                        0  \n",
       "3                 0     11                        0                        0  \n",
       "4                 0     12                        0                       10  \n",
       "...             ...    ...                      ...                      ...  \n",
       "44995            29     15                        0                        0  \n",
       "44996            29     27                        0                        0  \n",
       "44997            29     19                        0                        0  \n",
       "44998            29     28                        0                       10  \n",
       "44999            29     13                       10                        0  \n",
       "\n",
       "[45000 rows x 10 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sarsa_iter = pd.read_csv(f\"{path}/sarsa_player_run_4.csv\")\n",
    "df_sarsa_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e105486",
   "metadata": {},
   "outputs": [
    {
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       "<p>45000 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "       episode  trial_number  steps  reward_for_white_player  \\\n",
       "0            0             0     18                        0   \n",
       "1            1             0     11                        0   \n",
       "2            2             0     12                        0   \n",
       "3            3             0     11                        0   \n",
       "4            4             0     12                        0   \n",
       "...        ...           ...    ...                      ...   \n",
       "44995      995            29     15                        0   \n",
       "44996      996            29     27                        0   \n",
       "44997      997            29     19                        0   \n",
       "44998      998            29     28                        0   \n",
       "44999      999            29     13                       10   \n",
       "\n",
       "       reward_for_black_player  \n",
       "0                           10  \n",
       "1                            0  \n",
       "2                            0  \n",
       "3                            0  \n",
       "4                           10  \n",
       "...                        ...  \n",
       "44995                        0  \n",
       "44996                        0  \n",
       "44997                        0  \n",
       "44998                       10  \n",
       "44999                        0  \n",
       "\n",
       "[45000 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sarsa_iter = pd.read_csv(f\"{path}/sarsa_player_run_4.csv\")\n",
    "df_sarsa_iter = df_sarsa_iter.drop(\"board_size\", axis=1)\n",
    "df_sarsa_iter = df_sarsa_iter.drop(\"epsilon\", axis=1)\n",
    "df_sarsa_iter = df_sarsa_iter.drop(\"gamma\", axis=1)\n",
    "df_sarsa_iter = df_sarsa_iter.drop(\"alpha\", axis=1)\n",
    "df_sarsa_iter = df_sarsa_iter.drop(\"starting_player\", axis=1)\n",
    "df_sarsa_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "14f27318",
   "metadata": {},
   "outputs": [
    {
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       "<p>15000 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "       episode  trial_number  steps  reward_for_white_player  \\\n",
       "0            0             0     18                        0   \n",
       "1            1             0     11                        0   \n",
       "2            2             0     12                        0   \n",
       "3            3             0     11                        0   \n",
       "4            4             0     12                        0   \n",
       "...        ...           ...    ...                      ...   \n",
       "14995      495            29     11                        0   \n",
       "14996      496            29     28                        0   \n",
       "14997      497            29     17                       10   \n",
       "14998      498            29     17                        0   \n",
       "14999      499            29     21                        0   \n",
       "\n",
       "       reward_for_black_player  \n",
       "0                           10  \n",
       "1                            0  \n",
       "2                            0  \n",
       "3                            0  \n",
       "4                           10  \n",
       "...                        ...  \n",
       "14995                        0  \n",
       "14996                       10  \n",
       "14997                        0  \n",
       "14998                        0  \n",
       "14999                        0  \n",
       "\n",
       "[15000 rows x 5 columns]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows_for_500_episode_trials = 500 * 30\n",
    "df_sarsa_iter_500_episodes = df_sarsa_iter.iloc[0:rows_for_500_episode_trials]\n",
    "df_sarsa_iter_500_episodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4cca465b",
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>500 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     episode  steps  reward_for_white_player  reward_for_black_player\n",
       "0          0     18                        0                       10\n",
       "1          1     11                        0                        0\n",
       "2          2     12                        0                        0\n",
       "3          3     11                        0                        0\n",
       "4          4     12                        0                       10\n",
       "..       ...    ...                      ...                      ...\n",
       "495      495     13                       10                        0\n",
       "496      496     15                       10                        0\n",
       "497      497     32                        0                       10\n",
       "498      498     13                       10                        0\n",
       "499      499     15                       10                        0\n",
       "\n",
       "[500 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sarsa_iter_500_episodes_trials = [\n",
    "    df_sarsa_iter_500_episodes[df_sarsa_iter_500_episodes[\"trial_number\"] == i].drop(\"trial_number\", axis=1)\n",
    "    for i in range(30)\n",
    "]\n",
    "df_sarsa_iter_500_episodes_trials[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "237b3551",
   "metadata": {},
   "outputs": [
    {
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       "<p>30000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       episode  trial_number  steps  reward_for_white_player  \\\n",
       "15000        0             0     24                        0   \n",
       "15001        1             0     28                        0   \n",
       "15002        2             0     15                       10   \n",
       "15003        3             0     18                        0   \n",
       "15004        4             0     14                        0   \n",
       "...        ...           ...    ...                      ...   \n",
       "44995      995            29     15                        0   \n",
       "44996      996            29     27                        0   \n",
       "44997      997            29     19                        0   \n",
       "44998      998            29     28                        0   \n",
       "44999      999            29     13                       10   \n",
       "\n",
       "       reward_for_black_player  \n",
       "15000                        0  \n",
       "15001                       10  \n",
       "15002                        0  \n",
       "15003                       10  \n",
       "15004                        0  \n",
       "...                        ...  \n",
       "44995                        0  \n",
       "44996                        0  \n",
       "44997                        0  \n",
       "44998                       10  \n",
       "44999                        0  \n",
       "\n",
       "[30000 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sarsa_iter_1000_episodes = df_sarsa_iter.iloc[rows_for_500_episode_trials:]\n",
    "df_sarsa_iter_1000_episodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d04c1f78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>episode</th>\n",
       "      <th>steps</th>\n",
       "      <th>reward_for_white_player</th>\n",
       "      <th>reward_for_black_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15000</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15001</th>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15002</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15003</th>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15004</th>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15995</th>\n",
       "      <td>995</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15996</th>\n",
       "      <td>996</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15997</th>\n",
       "      <td>997</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15998</th>\n",
       "      <td>998</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15999</th>\n",
       "      <td>999</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       episode  steps  reward_for_white_player  reward_for_black_player\n",
       "15000        0     24                        0                        0\n",
       "15001        1     28                        0                       10\n",
       "15002        2     15                       10                        0\n",
       "15003        3     18                        0                       10\n",
       "15004        4     14                        0                        0\n",
       "...        ...    ...                      ...                      ...\n",
       "15995      995     17                       10                        0\n",
       "15996      996     44                        0                       10\n",
       "15997      997     18                        0                        0\n",
       "15998      998     18                        0                        0\n",
       "15999      999     13                        0                        0\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sarsa_iter_1000_episodes = [\n",
    "    df_sarsa_iter_1000_episodes[df_sarsa_iter_1000_episodes[\"trial_number\"] == i].drop(\"trial_number\", axis=1)\n",
    "    for i in range(30)\n",
    "]\n",
    "df_sarsa_iter_1000_episodes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30182806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 1000)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rewards_per_episode_1000_episodes = np.array([df_sarsa_iter_1000_episodes[i][\"reward_for_white_player\"] for i in range(30)])\n",
    "rewards_per_episode_1000_episodes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "858eb579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1000,), (1000,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = np.mean(rewards_per_episode_1000_episodes, axis=0)\n",
    "std = np.std(rewards_per_episode_1000_episodes, axis=0)\n",
    "mean.shape, std.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2d5e3498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "76cfc652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.tight_layout()\n",
    "fig = plt.figure(figsize=(15, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "num_episodes = 1000\n",
    "\n",
    "x = np.linspace(0, num_episodes, num_episodes)\n",
    "conf_value = 1.645  # 90%\n",
    "\n",
    "ax.plot(mean, label=\"sarsa over time\", color=\"orange\")\n",
    "ax.fill_between(x,\n",
    "                np.max(np.array([mean - conf_value * std, np.zeros(mean.shape)]), axis=0),\n",
    "                mean + conf_value * std,\n",
    "                color=\"orange\", alpha=0.2)\n",
    "\n",
    "ax.legend()\n",
    "ax.set_title(\"SARSA Training on 1000 Episodes Results\")\n",
    "ax.set_xlabel(\"Episodes\")\n",
    "ax.set_ylabel(\"Average Reward\")\n",
    "\n",
    "plt.savefig(\"sarsa_training_1000_episodes.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ed321e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}