Statistics and VI agent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import glob\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pprint\n",
    "import flat_table\n",
    "\n",
    "pp = pprint.PrettyPrinter(depth=4)\n",
    "\n",
    "\n",
    "path = './data/road-game-default-rtdb-export_prev_prev.json'\n",
    "\n",
    "branchs = ['all-games','complete-games']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load data from the json file\n",
    "with open(path) as train_file:\n",
    "    data = json.load(train_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>additional_comments</th>\n",
       "      <th>aggressively_value</th>\n",
       "      <th>behavior</th>\n",
       "      <th>birthYear</th>\n",
       "      <th>blueScore</th>\n",
       "      <th>computer_value</th>\n",
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       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>generously_value</th>\n",
       "      <th>log</th>\n",
       "      <th>press-the-read-instructions-button</th>\n",
       "      <th>redScore</th>\n",
       "      <th>steps</th>\n",
       "      <th>win</th>\n",
       "      <th>wisely_value</th>\n",
       "      <th>predictable_value</th>\n",
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      "text/plain": [
       "                    index additional_comments  aggressively_value  \\\n",
       "0    -MZiWIwdnrn8TJ034z2w                                     5.0   \n",
       "1    -MZiWUxZs8BvWglGhc9r                                     2.0   \n",
       "2    -MZiWYgxyToIiCSDQY8S                                     5.0   \n",
       "3    -MZiWYz_z_BcQlpReXf_                                     6.0   \n",
       "4    -MZiWaNZ6n3K3AZDfr-q                                     6.0   \n",
       "..                    ...                 ...                 ...   \n",
       "667  -MbcRp52SnKf2_aDNt2a                 NaN                 NaN   \n",
       "668  -MbcURCQaLlJFHGxAvyW                 NaN                 NaN   \n",
       "669  -MbcbjkKh8M10JRrqfwb                 NaN                 NaN   \n",
       "670  -MbcfQpUktm_lYEmxpz2                 NaN                 NaN   \n",
       "671  -Mbcz6inzp9NEdJCbK17                 NaN                 NaN   \n",
       "\n",
       "                           behavior birthYear  blueScore  computer_value  \\\n",
       "0                   carefulBehavior      1961       20.0             5.0   \n",
       "1                   carefulBehavior      1982       19.0             6.0   \n",
       "2                aggressiveBehavior      1953     -105.0             7.0   \n",
       "3                aggressiveBehavior      1990       25.0             7.0   \n",
       "4                aggressiveBehavior      1953     -105.0             6.0   \n",
       "..                              ...       ...        ...             ...   \n",
       "667  valueIterationBasedBehavior_v2       NaN        NaN             NaN   \n",
       "668  valueIterationBasedBehavior_v2       NaN        NaN             NaN   \n",
       "669  valueIterationBasedBehavior_v2       NaN        NaN             NaN   \n",
       "670  valueIterationBasedBehavior_v2       NaN        NaN             NaN   \n",
       "671  valueIterationBasedBehavior_v2       NaN        NaN             NaN   \n",
       "\n",
       "    drivinglicense        education  gender  generously_value  \\\n",
       "0            valid               BA    male               6.0   \n",
       "1            valid               BA  female               4.0   \n",
       "2            valid               BA  female               2.0   \n",
       "3            valid  Master's degree    male               2.0   \n",
       "4            valid               BA  female               2.0   \n",
       "..             ...              ...     ...               ...   \n",
       "667            NaN              NaN     NaN               NaN   \n",
       "668            NaN              NaN     NaN               NaN   \n",
       "669            NaN              NaN     NaN               NaN   \n",
       "670            NaN              NaN     NaN               NaN   \n",
       "671            NaN              NaN     NaN               NaN   \n",
       "\n",
       "                                                   log  \\\n",
       "0    [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "1    [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "2    [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "3    [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "4    [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "..                                                 ...   \n",
       "667                                                NaN   \n",
       "668                                                NaN   \n",
       "669                                                NaN   \n",
       "670                                                NaN   \n",
       "671                                                NaN   \n",
       "\n",
       "    press-the-read-instructions-button  redScore  steps    win  wisely_value  \\\n",
       "0                                 True      23.0    7.0   True           1.0   \n",
       "1                                 True      22.0    8.0   True           4.0   \n",
       "2                                 True    -105.0    5.0  False           6.0   \n",
       "3                                 True      21.0    9.0   True           2.0   \n",
       "4                                 True    -105.0    5.0  False           1.0   \n",
       "..                                 ...       ...    ...    ...           ...   \n",
       "667                                NaN       NaN    NaN    NaN           NaN   \n",
       "668                                NaN       NaN    NaN    NaN           NaN   \n",
       "669                                NaN       NaN    NaN    NaN           NaN   \n",
       "670                                NaN       NaN    NaN    NaN           NaN   \n",
       "671                                NaN       NaN    NaN    NaN           NaN   \n",
       "\n",
       "     predictable_value answer-the-quiz  \n",
       "0                  NaN             NaN  \n",
       "1                  NaN             NaN  \n",
       "2                  NaN             NaN  \n",
       "3                  NaN             NaN  \n",
       "4                  NaN             NaN  \n",
       "..                 ...             ...  \n",
       "667                NaN             NaN  \n",
       "668                NaN             NaN  \n",
       "669                NaN             NaN  \n",
       "670                NaN             NaN  \n",
       "671                NaN             NaN  \n",
       "\n",
       "[672 rows x 19 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch = branchs[0]\n",
    "df = pd.DataFrame.from_dict(data[branch], orient='index')\n",
    "df.reset_index(level=0, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'additional_comments', 'aggressively_value', 'behavior',\n",
       "       'birthYear', 'blueScore', 'computer_value', 'drivinglicense',\n",
       "       'education', 'gender', 'generously_value', 'log',\n",
       "       'press-the-read-instructions-button', 'redScore', 'steps', 'win',\n",
       "       'wisely_value', 'predictable_value', 'answer-the-quiz'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>True</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>-105.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-MZiWYz_z_BcQlpReXf_</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td>aggressiveBehavior</td>\n",
       "      <td>1990</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>valid</td>\n",
       "      <td>Master's degree</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[None, {'blue': 'from: a6, action: left', 'red...</td>\n",
       "      <td>True</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-MZiWaNZ6n3K3AZDfr-q</td>\n",
       "      <td></td>\n",
       "      <td>6.0</td>\n",
       "      <td>aggressiveBehavior</td>\n",
       "      <td>1953</td>\n",
       "      <td>-105.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>valid</td>\n",
       "      <td>BA</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[None, {'blue': 'from: a6, action: left', 'red...</td>\n",
       "      <td>True</td>\n",
       "      <td>-105.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <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>599</th>\n",
       "      <td>-MbMhZJJjMuTmSQzAJOG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>randomBehavior</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[None, {'blue': 'from: a6, action: stay', 'red...</td>\n",
       "      <td>True</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>-Mbc5KOa09qPXd1istvW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>valueIterationBasedBehavior_v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[None, {'blue': 'from: a6, action: left', 'red...</td>\n",
       "      <td>True</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>-Mbc6QvkHMVuhZ2h7Y9K</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>valueIterationBasedBehavior_v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[None, {'blue': 'from: a6, action: left', 'red...</td>\n",
       "      <td>True</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>-Mbc7ftA1HqxUb0gkdJ1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>valueIterationBasedBehavior_v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[None, {'blue': 'from: a6, action: left', 'red...</td>\n",
       "      <td>True</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>-MbcJHhtXTo0rb8QxsnY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>valueIterationBasedBehavior_v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[None, {'blue': 'from: a6, action: left', 'red...</td>\n",
       "      <td>True</td>\n",
       "      <td>-105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>483 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    index additional_comments  aggressively_value  \\\n",
       "0    -MZiWIwdnrn8TJ034z2w                                     5.0   \n",
       "1    -MZiWUxZs8BvWglGhc9r                                     2.0   \n",
       "2    -MZiWYgxyToIiCSDQY8S                                     5.0   \n",
       "3    -MZiWYz_z_BcQlpReXf_                                     6.0   \n",
       "4    -MZiWaNZ6n3K3AZDfr-q                                     6.0   \n",
       "..                    ...                 ...                 ...   \n",
       "599  -MbMhZJJjMuTmSQzAJOG                 NaN                 NaN   \n",
       "622  -Mbc5KOa09qPXd1istvW                 NaN                 NaN   \n",
       "635  -Mbc6QvkHMVuhZ2h7Y9K                 NaN                 NaN   \n",
       "636  -Mbc7ftA1HqxUb0gkdJ1                 NaN                 NaN   \n",
       "658  -MbcJHhtXTo0rb8QxsnY                 NaN                 NaN   \n",
       "\n",
       "                           behavior birthYear  blueScore  computer_value  \\\n",
       "0                   carefulBehavior      1961       20.0             5.0   \n",
       "1                   carefulBehavior      1982       19.0             6.0   \n",
       "2                aggressiveBehavior      1953     -105.0             7.0   \n",
       "3                aggressiveBehavior      1990       25.0             7.0   \n",
       "4                aggressiveBehavior      1953     -105.0             6.0   \n",
       "..                              ...       ...        ...             ...   \n",
       "599                  randomBehavior       NaN       21.0             NaN   \n",
       "622  valueIterationBasedBehavior_v2       NaN       25.0             NaN   \n",
       "635  valueIterationBasedBehavior_v2       NaN       25.0             NaN   \n",
       "636  valueIterationBasedBehavior_v2       NaN       25.0             NaN   \n",
       "658  valueIterationBasedBehavior_v2       NaN     -105.0             NaN   \n",
       "\n",
       "    drivinglicense        education  gender  generously_value  \\\n",
       "0            valid               BA    male               6.0   \n",
       "1            valid               BA  female               4.0   \n",
       "2            valid               BA  female               2.0   \n",
       "3            valid  Master's degree    male               2.0   \n",
       "4            valid               BA  female               2.0   \n",
       "..             ...              ...     ...               ...   \n",
       "599            NaN              NaN     NaN               NaN   \n",
       "622            NaN              NaN     NaN               NaN   \n",
       "635            NaN              NaN     NaN               NaN   \n",
       "636            NaN              NaN     NaN               NaN   \n",
       "658            NaN              NaN     NaN               NaN   \n",
       "\n",
       "                                                   log  \\\n",
       "0    [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "1    [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "2    [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "3    [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "4    [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "..                                                 ...   \n",
       "599  [None, {'blue': 'from: a6, action: stay', 'red...   \n",
       "622  [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "635  [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "636  [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "658  [None, {'blue': 'from: a6, action: left', 'red...   \n",
       "\n",
       "    press-the-read-instructions-button  redScore  steps    win  wisely_value  \\\n",
       "0                                 True      23.0    7.0   True           1.0   \n",
       "1                                 True      22.0    8.0   True           4.0   \n",
       "2                                 True    -105.0    5.0  False           6.0   \n",
       "3                                 True      21.0    9.0   True           2.0   \n",
       "4                                 True    -105.0    5.0  False           1.0   \n",
       "..                                 ...       ...    ...    ...           ...   \n",
       "599                               True      25.0    5.0   True           NaN   \n",
       "622                               True      23.0    7.0   True           NaN   \n",
       "635                               True      23.0    7.0   True           NaN   \n",
       "636                               True      22.0    8.0   True           NaN   \n",
       "658                               True    -105.0    NaN    NaN           NaN   \n",
       "\n",
       "     predictable_value answer-the-quiz  \n",
       "0                  NaN             NaN  \n",
       "1                  NaN             NaN  \n",
       "2                  NaN             NaN  \n",
       "3                  NaN             NaN  \n",
       "4                  NaN             NaN  \n",
       "..                 ...             ...  \n",
       "599                NaN            True  \n",
       "622                NaN            True  \n",
       "635                NaN            True  \n",
       "636                NaN            True  \n",
       "658                NaN            True  \n",
       "\n",
       "[483 rows x 19 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(subset=['log'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      268\n",
       "female    179\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BA                 265\n",
       "High School        128\n",
       "Master's degree     45\n",
       "Ph.D                 7\n",
       "none                 1\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "valid      411\n",
       "none        22\n",
       "invalid     13\n",
       "Name: drivinglicense, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drivinglicense'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survay results:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "upload Id workers and search for duplicate workers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = './data/workers' # use your path\n",
    "all_files = glob.glob(path + \"/*.csv\")\n",
    "\n",
    "li = []\n",
    "\n",
    "for filename in all_files:\n",
    "    fromOne = pd.read_csv(filename, index_col=None, header=0)\n",
    "    fromOne['filename'] = filename\n",
    "    li.append(fromOne)\n",
    "\n",
    "workers = pd.concat(li, axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-10838bc39a57>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['WorkerId'] = workers['WorkerId']\n"
     ]
    }
   ],
   "source": [
    "df['WorkerId'] = workers['WorkerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WorkerId\n",
       "A17AF42SNQNH9C    7\n",
       "A1AZLJ3R5FQ9B7    2\n",
       "A1K1E4KCVW1HOZ    2\n",
       "A1OW27AZS122X2    2\n",
       "A209KMZQTLEZZC    2\n",
       "A244IQQR0CTZ3I    2\n",
       "A28U7B76HLCS1U    2\n",
       "A29RCIFHRAZ797    2\n",
       "A2DCKE7398C0XV    2\n",
       "A2I6ZALE49CVSC    3\n",
       "A2IFXO71MXB9GK    2\n",
       "A2KNDIH7FHF7JL    3\n",
       "A2LK6J2I386VC1    2\n",
       "A2OFN0A5CPLH57    2\n",
       "A2OU4N4VBIE91I    2\n",
       "A2XK59FYAFO9EX    2\n",
       "A2YR29YNFLZSUG    2\n",
       "A2ZRL1ZWWXJ0L7    3\n",
       "A304UJAE051J89    2\n",
       "A30YR5TPOKQYXJ    2\n",
       "A337Y4X67PY4QI    2\n",
       "A33XAJW8WGY3O9    3\n",
       "A34CPKFZXBX1PO    2\n",
       "A36UD0YBRIWTVL    2\n",
       "A37W0GRXSM04LX    2\n",
       "A37XBH865833FE    2\n",
       "A39ICPRR4OS3OX    2\n",
       "A3ADHMAWDD7M6V    2\n",
       "A3FMBSTZ3ZGSV1    3\n",
       "A3GWRDHAURRNK6    2\n",
       "A3H073G4EPBYOM    2\n",
       "A3H3BTRV6I3LYU    2\n",
       "A3HCQGTFEZGEEF    2\n",
       "A3KK1BNF5H0N1Z    3\n",
       "A3L7TVDAMZM0FG    2\n",
       "A3NKZMF7M4X0ZP    2\n",
       "A3P35II1RN3FYV    2\n",
       "A3PX6V75SHQ4JP    2\n",
       "A3PXU2ZLEL8WRW    2\n",
       "A3UWHJTAUTF6TO    2\n",
       "ACJNWSBIVI46H     2\n",
       "ACXP8KHFX06KR     3\n",
       "AD1ILDUXZHASF     2\n",
       "AREQ43Q0T8ENT     2\n",
       "ASNMT3I94HZ44     2\n",
       "AT6ICEVY8RM8D     2\n",
       "ATJ92FYQX176Z     7\n",
       "Name: index, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(g for _, g in df.groupby('WorkerId') if len(g) > 1).groupby('WorkerId')['index'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how many to drop?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pd.concat(g for _, g in df.groupby('WorkerId') if len(g) > 1).groupby('WorkerId')['index'].count()) - len(pd.concat(g for _, g in df.groupby('WorkerId') if len(g) > 1).groupby('WorkerId')['index'].count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will keep the first one of any 'WorkerId' instance to keep samples unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df[(~df.duplicated('WorkerId')) | (df['WorkerId'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number of people that answer the survay:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "483"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8                                    Thankyouandstaysafe\n",
       "11     Icouldn'tmovetotherightusingtherightarrowasins...\n",
       "12                                                  none\n",
       "24                                           GodBlessYou\n",
       "34                                                  none\n",
       "                             ...                        \n",
       "397                                            Thankyou.\n",
       "402                                                 cool\n",
       "405                                                 None\n",
       "425                                                 Good\n",
       "428                                                  N/A\n",
       "Name: additional_comments, Length: 70, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['additional_comments'][df['additional_comments'].notna()][df['additional_comments'] != \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ido/.local/lib/python3.8/site-packages/pandas/core/indexing.py:1786: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, val, pi)\n"
     ]
    }
   ],
   "source": [
    "df.loc[:, ['aggressively_value', 'computer_value', 'generously_value', 'wisely_value', 'predictable_value']] = df.loc[:, ['aggressively_value', 'computer_value', 'generously_value', 'wisely_value', 'predictable_value']].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aggressively_value</th>\n",
       "      <th>blueScore</th>\n",
       "      <th>computer_value</th>\n",
       "      <th>generously_value</th>\n",
       "      <th>redScore</th>\n",
       "      <th>steps</th>\n",
       "      <th>wisely_value</th>\n",
       "      <th>predictable_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavior</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aggressiveBehavior</th>\n",
       "      <td>5.045455</td>\n",
       "      <td>-16.277778</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>3.287879</td>\n",
       "      <td>-18.402778</td>\n",
       "      <td>6.873239</td>\n",
       "      <td>4.590909</td>\n",
       "      <td>4.971429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carefulBehavior</th>\n",
       "      <td>2.964286</td>\n",
       "      <td>11.787879</td>\n",
       "      <td>5.214286</td>\n",
       "      <td>5.071429</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>7.468750</td>\n",
       "      <td>4.035714</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>randomBehavior</th>\n",
       "      <td>3.519481</td>\n",
       "      <td>-72.285714</td>\n",
       "      <td>5.649351</td>\n",
       "      <td>4.012987</td>\n",
       "      <td>-71.511905</td>\n",
       "      <td>11.524390</td>\n",
       "      <td>3.727273</td>\n",
       "      <td>3.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semiAggressiveBehavior</th>\n",
       "      <td>4.578313</td>\n",
       "      <td>-61.911111</td>\n",
       "      <td>5.734940</td>\n",
       "      <td>3.216867</td>\n",
       "      <td>-63.033333</td>\n",
       "      <td>6.431818</td>\n",
       "      <td>4.337349</td>\n",
       "      <td>4.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semiCarefulBehavior</th>\n",
       "      <td>3.940476</td>\n",
       "      <td>-4.863636</td>\n",
       "      <td>5.702381</td>\n",
       "      <td>4.238095</td>\n",
       "      <td>-3.250000</td>\n",
       "      <td>12.720930</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>4.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valueIterationBasedBehavior_v2</th>\n",
       "      <td>4.824074</td>\n",
       "      <td>-6.000000</td>\n",
       "      <td>6.074074</td>\n",
       "      <td>3.574074</td>\n",
       "      <td>-8.017241</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.703704</td>\n",
       "      <td>4.767123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                aggressively_value  blueScore  computer_value  \\\n",
       "behavior                                                                        \n",
       "aggressiveBehavior                        5.045455 -16.277778        5.833333   \n",
       "carefulBehavior                           2.964286  11.787879        5.214286   \n",
       "randomBehavior                            3.519481 -72.285714        5.649351   \n",
       "semiAggressiveBehavior                    4.578313 -61.911111        5.734940   \n",
       "semiCarefulBehavior                       3.940476  -4.863636        5.702381   \n",
       "valueIterationBasedBehavior_v2            4.824074  -6.000000        6.074074   \n",
       "\n",
       "                                generously_value   redScore      steps  \\\n",
       "behavior                                                                 \n",
       "aggressiveBehavior                      3.287879 -18.402778   6.873239   \n",
       "carefulBehavior                         5.071429  14.333333   7.468750   \n",
       "randomBehavior                          4.012987 -71.511905  11.524390   \n",
       "semiAggressiveBehavior                  3.216867 -63.033333   6.431818   \n",
       "semiCarefulBehavior                     4.238095  -3.250000  12.720930   \n",
       "valueIterationBasedBehavior_v2          3.574074  -8.017241   9.000000   \n",
       "\n",
       "                                wisely_value  predictable_value  \n",
       "behavior                                                         \n",
       "aggressiveBehavior                  4.590909           4.971429  \n",
       "carefulBehavior                     4.035714                NaN  \n",
       "randomBehavior                      3.727273           3.571429  \n",
       "semiAggressiveBehavior              4.337349           4.523810  \n",
       "semiCarefulBehavior                 4.920000           4.285714  \n",
       "valueIterationBasedBehavior_v2      4.703704           4.767123  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('behavior').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-18-c4d30ac448c8>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df.groupby('behavior')['blueScore', 'redScore'].describe()\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th colspan=\"8\" halign=\"left\">redScore</th>\n",
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       "      <td>72.0</td>\n",
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       "      <td>-108.0</td>\n",
       "      <td>-103.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.00</td>\n",
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       "      <td>72.0</td>\n",
       "      <td>-18.402778</td>\n",
       "      <td>59.231620</td>\n",
       "      <td>-108.0</td>\n",
       "      <td>-103.00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.25</td>\n",
       "      <td>23.0</td>\n",
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       "    <tr>\n",
       "      <th>carefulBehavior</th>\n",
       "      <td>33.0</td>\n",
       "      <td>11.787879</td>\n",
       "      <td>31.778292</td>\n",
       "      <td>-118.0</td>\n",
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       "      <td>20.0</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>14.333333</td>\n",
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       "      <td>84.0</td>\n",
       "      <td>-72.285714</td>\n",
       "      <td>59.131377</td>\n",
       "      <td>-152.0</td>\n",
       "      <td>-109.00</td>\n",
       "      <td>-107.0</td>\n",
       "      <td>13.25</td>\n",
       "      <td>22.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>-71.511905</td>\n",
       "      <td>60.413534</td>\n",
       "      <td>-152.0</td>\n",
       "      <td>-109.00</td>\n",
       "      <td>-107.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semiAggressiveBehavior</th>\n",
       "      <td>90.0</td>\n",
       "      <td>-61.911111</td>\n",
       "      <td>61.065749</td>\n",
       "      <td>-114.0</td>\n",
       "      <td>-105.00</td>\n",
       "      <td>-104.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>-63.033333</td>\n",
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       "      <td>-114.0</td>\n",
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       "      <td>19.75</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>semiCarefulBehavior</th>\n",
       "      <td>88.0</td>\n",
       "      <td>-4.863636</td>\n",
       "      <td>49.036740</td>\n",
       "      <td>-137.0</td>\n",
       "      <td>11.75</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-3.250000</td>\n",
       "      <td>49.981203</td>\n",
       "      <td>-137.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valueIterationBasedBehavior_v2</th>\n",
       "      <td>116.0</td>\n",
       "      <td>-6.000000</td>\n",
       "      <td>55.386861</td>\n",
       "      <td>-145.0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>25.0</td>\n",
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       "      <td>25.0</td>\n",
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      ],
      "text/plain": [
       "                               blueScore                                       \\\n",
       "                                   count       mean        std    min     25%   \n",
       "behavior                                                                        \n",
       "aggressiveBehavior                  72.0 -16.277778  60.677913 -108.0 -103.00   \n",
       "carefulBehavior                     33.0  11.787879  31.778292 -118.0   19.00   \n",
       "randomBehavior                      84.0 -72.285714  59.131377 -152.0 -109.00   \n",
       "semiAggressiveBehavior              90.0 -61.911111  61.065749 -114.0 -105.00   \n",
       "semiCarefulBehavior                 88.0  -4.863636  49.036740 -137.0   11.75   \n",
       "valueIterationBasedBehavior_v2     116.0  -6.000000  55.386861 -145.0   11.50   \n",
       "\n",
       "                                                   redScore             \\\n",
       "                                  50%    75%   max    count       mean   \n",
       "behavior                                                                 \n",
       "aggressiveBehavior               25.0  25.00  25.0     72.0 -18.402778   \n",
       "carefulBehavior                  20.0  20.00  22.0     33.0  14.333333   \n",
       "randomBehavior                 -107.0  13.25  22.0     84.0 -71.511905   \n",
       "semiAggressiveBehavior         -104.0  24.00  25.0     90.0 -63.033333   \n",
       "semiCarefulBehavior              17.0  19.00  22.0     88.0  -3.250000   \n",
       "valueIterationBasedBehavior_v2   25.0  25.00  25.0    116.0  -8.017241   \n",
       "\n",
       "                                                                              \n",
       "                                      std    min     25%    50%    75%   max  \n",
       "behavior                                                                      \n",
       "aggressiveBehavior              59.231620 -108.0 -103.00   22.0  22.25  23.0  \n",
       "carefulBehavior                 32.463120 -118.0   21.00   23.0  23.00  25.0  \n",
       "randomBehavior                  60.413534 -152.0 -109.00 -107.0  17.00  25.0  \n",
       "semiAggressiveBehavior          59.472673 -114.0 -105.00 -104.0  19.75  22.0  \n",
       "semiCarefulBehavior             49.981203 -137.0   13.00   19.0  22.00  25.0  \n",
       "valueIterationBasedBehavior_v2  54.288117 -145.0   12.75   21.0  23.00  25.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('behavior')['blueScore', 'redScore'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-43764c80785d>:3: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  df.ages = current_year - birth_year\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "38.64798206278027"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birth_year = df.birthYear.dropna().astype(int)\n",
    "current_year = pd.Series(np.ones((len(birth_year))) * 2021).astype(int)\n",
    "df.ages = current_year - birth_year\n",
    "df.ages.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.213530655391121"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.steps.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'aggressively_value')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1) = plt.subplots(ncols=1, figsize=(8,5))\n",
    "sns.kdeplot(df.aggressively_value[df['behavior'] == 'carefulBehavior'], label='carefulBehavior', shade=True)\n",
    "sns.kdeplot(df.aggressively_value[df['behavior'] == 'semiAggressiveBehavior'], label='semiAggressiveBehavior', shade=True)\n",
    "sns.kdeplot(df.aggressively_value[df['behavior'] == 'aggressiveBehavior'], label='aggressiveBehavior', shade=True)\n",
    "sns.kdeplot(df.aggressively_value[df['behavior'] == 'randomBehavior'], label='randomBehavior', shade=True)\n",
    "sns.kdeplot(df.aggressively_value[df['behavior'] == 'valueIterationBasedBehavior_v2'], label='valueIterationBasedBehavior_v2', shade=True)\n",
    "sns.kdeplot(df.aggressively_value[df['behavior'] == 'semiCarefulBehavior'], label='semiCarefulBehavior', shade=True)\n",
    "ax1.set_ylabel('Distribution')\n",
    "ax1.set_xlabel('aggressively_value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temp = df[df.behavior != 'carefulBehavior']\n",
    "\n",
    "def count_plot_by_behavior(feature):\n",
    "    sns.countplot(x=df_temp[feature],hue=df_temp['behavior'])\n",
    "    plt.show()\n",
    "    \n",
    "for value in ['aggressively_value', 'computer_value', 'generously_value', 'wisely_value', 'predictable_value']:\n",
    "    count_plot_by_behavior(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3CBHPaoDJQRht6ztIudG0SREkByxc7juHSAl5H9gml8285jtIuVB5F1gQRrsAfyMecYjIEs8D2+WymTm+g5QDTZsUUBBGGwK3o+IW6c3GwO1BGNX6DlIOVN4Fkhz2fg+6ULBIf3YBLvMdohyovAsgGUncAaznO4tIChwahNGhvkOkncq7MC4DdvIdQiRFLgzCaGPfIdJM5T1MQRgdDWgUITI4SxHvQtjgO0haqbyHIQijzdA5HESG6nPAeb5DpJV2FRyiIIwagceBTXxnEUm5vXPZzB2+Q6SNRt5Ddw4qbpFCuDoIo7G+Q6SNRt5DEITRHsBdvnOIlJFpwA65bKbdd5C00Mh7kIIwWgW4xncOkTIzAfi17xBpovIevCuAlX2HEClDvwjC6Cu+Q6SFpk0GIQijA4CbfecQKWPvAhvlsplm30FKnUbeeQrCaHXgIt85RMrcaoAuYpwHlXf+rgJW8B1CpAIcFYTRFr5DlDqVdx6CMNobyPjOIVIhqoFLgjAy30FKmcp7AEEY1QO/951DpMJMAL7vO0QpU3kP7CdA4DuESAU6KwgjTVX2QeXdjyCMVgNO9J1DpEKtDJzpO0SpUnn37zfAKN8hRCrYD4IwGu87RCnSft59SLZ2P47+wIn49hiwbS6b6fQdpJSomPp2Hnp9RErB1sBhvkOUGo28exGE0beIL2smIqXhQ2CdXDYz13eQUqGRZQ9BGNWhXQNFSs1KwNG+Q5QSlfenHQes4zuEiHzK8UEYLe07RKlQeXcThNFywMm+c4hIr1YGjvIdolSovD/pSGBZ3yFEpE8/1UWLYyrvRDLXfYzvHCLSr9WAw32HKAUq7yX2B8b4DiEiA/p5MtiqaCrvJX7qO4CI5GVN4GDfIXzTft5AEEa7Aff6ziEieXsN2KCSL1iskXfsBN8BRGRQ1gYO8B3Cp4ofeQdhtCXwhO8cIjJoLwKbVOo5TzTy1qhbJK02pIKvcFXR5R2E0VhgH985RGTIKna3wYoub+B44uvliUg67R6E0Rq+Q/hQseWdXJvyYN85RGRYqqnQa11WbHkTz5Ut5zuEiAzboUEYVVyXVdwP3M3+vgOISEEEwJd9hxhpFVneQRgtSwVvpRYpQwf5DjDSKrK8gW8BOjOZSPnYq9LO9V2p5f1d3wFEpKCWJh6UVYyKK+8gjFYBdvGdQ0QK7nu+A4ykiitvYD+0b7dIOdo5CKPVfYcYKZVY3trLRKQ8VQPf9B1ipJREeZvZ/OTjGDO7vY9lppjZ+OE8TxBGawPbDmcdIlLSvuo7wEgpifLu4px72zm3dxGfQhsqRcrbl4IwqvUdYiQUpbzNLGtmP+r29SQzO8XM/mVmM8ysycz27OVxgZk9m3zeaGa3mtnzZvYXoLEA0Spqa7RIBRoFfMF3iJFQrJH3ZGDfbl/vC9wAfNM5tyXwJeAcM7N+1nEUsNA5tzFwGrDVcAIFYbQSsMVw1iEiqVARUydFKW/n3JPAKskc9jjgI+Bd4Ewzewa4D1gDWLWf1ewA3Jys7xngmWHG2oUSmyYSkaJQeQ/TbcDexLvmTSa+ZNFoYCvn3ObAe4zsUY4Vd+4DkQq1eXI8R1krZnlPBr5DXOC3EZ/Bb5Zzrs3MvgSMHeDxD5Ds1mdmnwU2G2aerwzz8SKSDgbs6jtEseVd3ma2vZl9P/l8tJmt3d/yzrnniDcezHTOvQP8ARhvZk3EJ5F5YYCnvAxYxsyeB05nGNeZDMJoXeIzj4lIZSj7qZO8LkBsZqcB44ENnXMbmNkY4DbnXCq26gZhdChwte8cIjJiZgGr5bKZsr3Cer4j728CewALIN4fm3hUnRbb+w4gIiNqFcp877J8y7vVxUN0B2BmaTv1ospbpPKU9U4K+Zb3n8zsCmB5MzuceFe/q4oXq3CCMFoNWM93DhEZcWU98q7JZyHn3Nlm9hVgLrAhcKpz7p9FTVY4GnWLVKZxvgMUU14j72Sa5H7n3AnEI+5GM0vL+QO28x1ARLzYIAijsr1iVr7TJg8A9Wa2BvA34pOeX1+sUAX2Od8BRMSLamBT3yGKJd/yNufcQuITO13mnNuH9LwoG/kOICLelO3USd7lbWYTiA9xj5L7Sv5qNEEYLQOs6TuHiHhT8eV9LHAi8Bfn3HNmtg7w7+LFKhiNukUqW9mWd757mzxAPO/d9fWrwDHFClVAKm+RyjbccyKVrHz3NtnAzK40s3+Y2f1dt2KHK4CNfQcQEa9WCMJoLd8hiiGvkTfxWQEvJz4/SEfx4hScRt4iMg5403eIQsu3vNudc5cVNUlxqLxFZBxwj+8QhZbvBsu7zeyHZra6ma3YdStqsmEKwqgGHRYvIrC+7wDFkO/Ie2Ly8YRu9zlgncLGKah1gDrfIUTEu/4ut5ha+e5t0u+FF0qUpkxEBMq0vPPd22QpMzvFzK5Mvl7fzL5e3GjDVsrvCkRk5KzmO0Ax5DvnfR3QypKTPM0EzihKosIp6Tl5ERkxo4MwMt8hCi3f8l7XOfc7oA0gOc9Jqb8YK/gOICIloQZYyXeIQsv7Sjpm1siSK+msC7QULVVhqLxFpEvZzXvnW96nEZ8Kdi0z+wPwL+DnRUtVGCpvEelSdvPe+e5t8k8zmwFsSzxdcqxz7oOiJhu+5X0HEJGSUZkjbzP7JvFRlpFz7h6g3cz2Kmqy4dPIW0S6VGZ5A6c555q7vnDOzSGeSillKm8R6VJ20yb5lndvy+V7dKYvKm8R6VKxI+/HzexcM1s3uZ0LPFHMYMORXHS03ncOESkZS/kOUGj5lvePiQ/SmZzcWoAfFStUAWjULSLdlfpMwaDlu7fJAiAscpZCUnmLSHclf83dweq3vM3sfOfccWZ2N8kBOt055/YoWrLh0dkERaS7iht535R8PLvYQQqs1XcAESkplVXezrknko9TRyZOwai8BYAVq2a90dH49nsLqvPeviPlyFW9AxnfKQpqoGmTJnqZLuninCvVKzOrvAWAjzpXXvOHix9+I9N499LPNVa1PtLQsKipvr5xVk31mHazsrwwrfRqNpzoO0NBDfRWotTP2d2XUj9plowQR1XVJR17bX/V/EzLqYtvnP676vs3qzK3IsDcKmt+qr4+N62xofmJhgZ7vbZmpYVm62DW4Du3FFyaLpyeF3Ouz4F1agVhtAIw23cOKT2jWNB8bu1lT365asbnzT69728HdLxUV5ub3tjw3qMNDa0v1NUt82F11VhnNtpHXimYe5omNn3Dd4hCyqu8zWwen54+aQYeB37qnHu1CNmGLAijpYH5vnNI6VqN2e9dXnfeS+PslQlmA2/Mer+66v3HGhremN7YMO+p+vq6mTU1q7YaAWZltwtambqzaWLTN32HKKR8t8CeD7wF3EJ8VsHvAOsCM4BrgZ2KkG04NOct/XqXFVfdq/XXq25gb752Ze257wZV703ob/nRHZ2jd1+wcPTuCxZ+fF+Lsbiprv6l6Y0NHzzWWO/+V1u73NyqqrUxW7boP4AMVrvvAIWW78j7aefcuB73PeWc27y375WCIIw6Kf2r/UiJ2Lbquecurb2wbUWbt/lw1/V6Tc1bjzbWz5ze2Lj42bq6Bm0cLQl/bJrYtL/vEIWU78h7oZntC9yefL03sDj5vFQnzVvR+U0kT9M7N910y5Yr2LPqocd/W3vVqKWsdcOhrmtse/uaY+e1r7nPvAUf39fLxtGVF5qtrY2jI6Z54EXSJd/yPgC4ALiUuKynAwcml0Y7ukjZhkvlLYN2V+cXxt/Vsp07svruh39ac9tnaq1jzUKsd9lOt9wOixaP22HR4o/vSzaOvtJt4+ioD6urPqONo0VR6hePGbSy3NsEIAij94GVfeeQ9KqlvfXkmpunH1T9z02rzI3YBWy1cbQojm+a2HT+YB9kZg8757YrZBAzmwL8zDn3uJndC+yfXCOh+zKTgPnOuT6Pbs9r5G3xSOBwIOj+GOfcIYMNPoJmo/KWYWijpm5S+8E7/L59v3ln114+9WtVj403Y+liP2+fG0fr61+c3tDwoTaODsmQRt6FLu5e1r/7UB+b77TJXcB/gPtIz87ubwMb+A4h6beAxlFHtR2/4yp89P6ldRc8sZW9NMGM2pHMUO9oGL+4ZZPxi1tgTnyfA/dGTc2bjzQ2vD29sWHRc3V1je/VVK/RYVaQqZ4y8+FQHmRm851zy5jZTsAk4j8CnyW+nsGBwFeBQ51z+yTL70Q8qv66mV0GbA00Arc75z519TEzywHjnXMfmNnJwERgFvAmA1wzId/yXso594s8ly0Vb/sOIOVlFiuM3rt10uh1bebrV9WeO3Nte2eCmb89mgxsbHv7WmPnzV9r33lLDmuYW2XNT9bXvz6tseGjGQ0NVdo4ChRmznsLYFPibnkI+ALxgPZKM1s6OXX2fsCtyfInO+dmWzzd9S8z28w590xvKzazrYh3wd6cuJdnUKDyvsfMdnfO3Zvn8qVA5S1F8YpbY+zOreeM3dpeeP6yuvMXrWxzt/SdqbtlO91yOy5avNmOPTaOvlhX+8ojjQ3vPtLQ0P5ifORoJW0cLUQfPOqcewviXaWBwDn3oJn9DfiGmd1OfParnyfL72tmPyDu2dWBTYBeyxv4IvAX59zCZP1/HShMvuV9LHCSmbUAbcT7TzvnXCnPt6m8pagecxttPL7lcjJV02f8rvaKxqWtZWPfmfpSDdWbtLatu0lr27rfb5738f2zqqvff7yh/vVpjQ0Lnq6vry3TjaMdwLsFWE/3cyZ1sKQ/byXe62428Lhzbp6ZrQ38DNjaOfeRmV0PFPSdT75X0hlVyCcdITN9B5DKEHVuu2XUso07tPr/poU1fxxTax1jfWfK1yodHZ/aOLrYbNGz9XWvTWtomP14Y33H/2prV5hbVRWkeOPou00Tm4q5rW4q8ZHmh7NkymRZYAHQbGarArsBU/pZxwPA9Wb2W+Je/gZwRX9POtApYTdyzr1gZr2+LXTOzejv8Z694TuAVBKzazp2n3BDx65tv6i59YFDqv9v42pzqZySaHCuMY+No0u9V1M9JiUbR98q5sqdcx1mdg9wMPEGR5xzT5vZk8ALxBsfHxpgHTPMbDLwNPEGy8cGet5+9/M2syudcz8ws3/3/nxu54GewJcgjFYB3vOdQyrT0iyan6296vGvV03fyow0vnPNS/OSI0fnzGhoqE5Oq1tqG0fvaJrYtLfvEIVWtgfpAARhNB+Kv1+uSF9WovmDS+oufG4be36CWWVcW7Ud2pPT6s56tKGh9UX/R45e0DSx6ThPz100+Z6Y6hngj8CfnHOvFD1VgQRh1ES8T6aIV4G98+aVtee+sb7N3M7n7oU+zaqunvVYQ/2b0xsb5j09skeOHtk0sanf+eM0yre8xxLvv7gf0AlMJi7ykp5XDsLoLqBUr3AvFWgLe/nFy+vOm7eqzRnvO0spWGy2qKm+7rWuI0dfqa1dvggbR7dvmtjU75xzGg162sTM1gd+CRzgnCvp3YmCMDoXON53DpGedq167Mlzay+rX8YWb+I7S6lx4OLT6jbMnN7Y0JKcVnfNDrM1hrjKFZomNs0pZMZSkHd59xh9dwCTnXPnFDHbsAVhdCBwk+8cIn05qPrv006p+cNqdda+tu8spa5r4+jDjY3NTzbUW662ZvSieONof2cPfbtpYtNQS7+k5Ttt8ghQC9xGXNolddmzvgRhtCHxrjoiJauajvaf1fzp4R9U37NhtblVfedJk54bR1+oq1t2dnXVWt02jv6zaWLTrl5DFkm+5b2hc+7FEchTUEEYGfARsJzvLCIDaaRl4Zm1Vz+2V9VDW5iR1gNiSsJ71dWzHm+of+OFuro///TY13/rO08xDLSf94HOuZvN7Ce9fd85d27RkhVIEEb3Abv4ziGSrxWYO/ui2oubvlD17LZmuqDIMB3IpOY/+A5RDFUDfL9rH+lRfdzS4FHfAUQG4yOWXfHAtpN23KH1/A+e71zrIefo9J0pxfo9M1+a9Xt4vHPuiuR0hnOdc+eNUKZCG/AwU5FS9KZbZY3dWs9aYzN75eUr6s6bs7rN3tp3ppSZD7zkO0SxDDTyxjnXAXx3BLIUi8pbUu0Zt+76E1ou3vr7rSc8Pdct1eQ7T4o8xaTmsn3XMmB5Jx4ys4vN7ItmtmXXrajJCiSXzbwFvOM7h8hw/btzi3GbtVz9uZPaDnmkxdWkYo8vz8p6yjTf83lvnnz8VfLRiK8iX7InpurhMXSkpZSJWzq+vM2tHTt3HFdz+4M/qv7rutXWubrvTCXqft8BimmgU8J27WVyD3FZdz8nQ5rOaPUoKm8pI51UVZ/bvu/2l7bvuej0muun7lM9dXMz7RLbTRvxebbL1kDTJl17lWwFHEV8KZ8xwBFAKqZNEpr3lrK0mPrGn7cfsePmLVe6KR2bTXWOxQM/qiJMZ1Lz/IEXS6+B9jb5FYCZPQBs6Zybl3w9CYiKnq5wpgGtUBmn5JTK08wyyx/cFu64Bu+/c0Xdea9sarkJZpT0uYeK7D7fAYot3w2WqxKXX5fW5L5UyGUz8yjzt1AiADMZvfrXW8/cPtN6Zu4tt/IjvvN4pPJO3Ag8amaTklH3I8D1xQpVJANejVmkXPzXBetu33LhNt9rDZvmuKX7umJ5uZpLme9pAoM7q+CWxJenB3jAOfdk0VIVQRBGnwFe951DxId9qqc89uua61ZosLb1fGcZAX9lUvOevkMUW1lfBq2nIIyeZMlujyIVxejsPLr6zmnH1vw5qLHOsjxNauIYJjVf5DtEseU7bVIuNHUiFctRVXVRx7e+sEnLdSv/oX3nqZ2Oj3xnKpJ/+g4wElTeIhWmldr6k9sP23Fcy1VV93VsMcU5FvrOVECvMam5Is7hX1HlnctmngBm+s4hUgrmsfRyh7WdsNN2LRfNfapz3f84R7vvTAVwm+8AI6Wiyjtxt+8AIqXkHVZaba/WX3/xa63ZN1/vXGW67zzDNNl3gJFSieWtqRORXrzoPrP2jq3nb7t/60nPzXajnvKdZwj+x6TmGb5DjJRKLO/7ic/zKyK9eLjzs5tu2XLF5se1/vDxRa4uTefDrpgpE6jA8s5lMy3EJ9oSkX7c2bn9+E1arl0v2/adh9pc9Vu+8+ShLC931peKK+/EVb4DiKSBo6rq8o49vrBJy3Wr3NC+69ROZx/6ztSHJ5nU/JzvECOpIss7l83cD7zoO4dIWrRRU3da+8E7btZyVe3fOsZPdY4FvjP1cONQH2hmgZk9b2ZXmdlzZvYPM2s0s83NbLqZPWNmfzGzFZLlp5jZWWb2qJm9ZGZfTO6vNrPfm9ljyWOOKNQP15uKLO/Elb4DiKTNfJZa9si2n+y4bcvFCx7v3OAB52jznQloB24Z5jrWBy5xzm0KzAG+TfwH4RfOuc2AJuC0bsvXOOc+DxzX7f5DgWbn3NbA1sDhZrb2MHP1qZLL+3rQuY9FhuI9Vlxl79ZJO3y59fdvv9q52jTnvF6c5W9Map41zHW85px7Kvn8CWBdYHnnXNfZSG8Adui2/J+7LRskn+8KHGRmTxGfvG8l4j8KRVGx5Z3LZmYDf/KdQyTNXnFrjN259dwJ+7ae+sIHbllfu+ldXIB1tHT7vANYPs/lO1hyXQQDfuyc2zy5re2c+0cBsvWqYss7cbnvACLl4DG30cbjWy7f8ujWH89Y6OpH8vD0/zKp+e9FWG8z8FHXfDbwPQa+JsDfgaPMrBbAzDYws6WLkA2o8PLOZTPTgKd95xApF/d0Tthyk5ZrNzyj7YCH21z1GyPwlBcUcd0Tgd+b2TPEZyM9fYDlrwb+C8wws2eBK8j/Iu+DVlGnhO1NEEZHAZf6ziFSbmpobwtr/jjtkOq/bVxlbnQRnuJDYC0mNS8qwrpLXkWPvBM3oyMuRQqunZraM9q/t8PnWq5uvKdj26nOMa/AT3F5pRY3qLy7rm9ZUUdmiYykBTQuc3TbMTuOb7ms5ZHOjR5w7hPXwx2qVuCSAqwntSq+vBMXg9ddnUTK3ocst/J+rafusHPr2e++3DnmoWHuXjiZSc3vFCxcClX8nHeXIIzuAL7lO4dIpdjCXn7xirrz5q1ic8YP4eFbMqk5VdfRLTSNvJf4FRp9i4yYJ936G36+5dLxR7Qe9+R81/DfQTx0aqUXN6i8P5bLZp4B7vSdQ6TS/L3z81t8tuXaTU5rmzi91dXk8njIb4qdKQ1U3p+k0beIJzd0fHXbjVuuW/Oy9m/8p8NZX4e7T2FSc0VcYHggKu9uctnM02j0LeJNB9U1Z7V/94ufbbl2mb90fGGqc8ztscjJXoKVIJX3p51CfL4CEfFkEfVLHd/2ox23bLm8/aGOTac6RwtwL5OaH/adrVRob5NeBGF0DXCI7xwiEvuMvffm+bWXfH3L0594xneWUqGRd+9OQ6eLFSkZb7hV/63i/iSVdy9y2cxbwEW+c4gIAIvQXPenqLz79lvgI98hRIRzkgGVdKPy7kMum/kIONF3DpEK9x5wlu8QpUjl3b8rgf/4DiFSwX6Zy2Z01s9eqLz7kctmHHA4n7xEkoiMjCnEFziQXqi8B5DLZl5Eh+OKjLSFwGHJAEp6ofLOTxZ41ncIkQpyUi6becV3iFKm8s5DLptpI54+6fSdRaQCPIR21R2QyjtPuWxmOrrWpUixLQIOyWUzGigNQOU9OCcBb/oOIVLGTs1lMy/5DpEGKu9BSK53+UPfOUTK1HTgPN8h0kLlPUi5bOYeYLLvHCJlpoV4ukRn9MyTyntojgRe9R1CpIxMymUzz/sOkSYq7yHIZTNzgG+jMw+KFMJ04Pe+Q6SNynuIctnMU2j+W2S43gW+remSwVN5D0Mum7kOuMZ3DpGUagP2zmUzb/sOkkYq7+E7GpjhO4RICh2by2Ye8h0irXQZtAIIwmht4AlgBd9ZRFLimlw2c5jvEGmmkXcB5LKZ14CDAP0lFBnYI8CPfIdIO5V3gST7f//Wdw6REvce8QZKnWZ5mFTehfVL4D7fIURKVNcGypm+g5QDlXcBJSfT+Q6ggw1EPu34XDbzoO8Q5UIbLIsgCKM1iE9rOdZ3FpESoQ2UBaaRdxEkbwu/AszynUWkBNwBHOE7RLlReRdJLpt5Gfgq0Ow7i4hH9wLf1RGUhafyLqLkEPqvE59gXqTS3E+8Z0mb7yDlSOVdZMkGmr2Jt7SLVIqHgD1y2YxO3lYkKu8RkMtm7iU+iEeXdpJK8ASwey6bWeA7SDlTeY+QXDZzK/F5UETKWROway6bmes7SLlTeY+gXDZzGXCy7xwiRfIi8JVcNjPbd5BKoP28PQjC6CfA2YD5ziJSIK8BO+Symbd8B6kUKm9PgjA6GLgaqPYcRWS4XiKeKnndd5BKovL2KAijvYBbgXrPUUSG6hHg67ls5gPfQSqN5rw9ymUzdwK7AfM8RxEZigjYWcXth8rbs1w2829ge0BnWpM0uRbYK5fNLPQdpFJp2qREJCezioBxvrOI9MMBk3LZzOm+g1Q6lXcJCcJoFHA7sKvvLCK9WAwcnMtmJvsOIpo2KSm5bGYekAGu9J1FpId3gZ1U3KVDI+8SFYTR94DLgKV9Z5GK9zTwjVw286bvILKERt4lKpfN3ASMJz7cWMSXq4DtVNylRyPvEheEUSNwAXC47yxSUT4CDs9lM3f4DiK9U3mnRBBG3wWuAEb5ziJl7wHgQI22S5umTVIil838EdgKeMpzFClf7cCpwJdU3KVPI++UCcKoATgPONJ3FikrOWD/XDYzzXcQyY/KO6WCMNqXeBplec9RJP1uBY7MZTO63mqKqLxTLAijVYhPLfs931kkleYDP85lM9f7DiKDp/IuA0EYfQm4FNjIdxZJjT8CJ+SyGZ1TJ6VU3mUiCKM64GfAKUCj5zhSupqIR9tTfQeR4VF5l5kgjNYGLiI+zF6kSzPxniSX5LKZDt9hZPhU3mUqCKNvER/cs6bvLOKVA64Hwlw2M8tzFikglXcZC8JoGWAScCxQ4zeNePA4cHQum3nEdxApPJV3BUimUk4CJgK1nuNI8X1A/Pu+JpfNdPoOI8Wh8q4gQRgFwInA91GJl6O3gXOAK3LZzALfYaS4VN4VKAijzxCX+CFAnec4MnyvAGcBN+SymVbfYWRkqLwrWBBGawEhcCi6gn0aNQG/Bf6kPUgqj8pbuq6fGRKfdlYlXvqmAWcCUS6b0X/gCqXylo8FYbQacYEfDqzlOY582j+BM3PZzBTfQcQ/lbd8ShBG1cDuwBHAbujUwT7NAm4Ers1lM8/7DiOlQ+Ut/Uo2bh5CfPKrdTzHqRQdwN+Ba4C7c9lMm+c8UoJU3pKXIIwM2J54X/F9gGX9JipLM4BbgD/mspm3fYeR0qbylkFLrqu5F7AHsCuwotdA6fY/4sK+JZfNvOg7jKSHyluGJZkf34Z4jnw3YAvAvIYqbQuBqcQbH/+Zy2ae9ZxHUkrlLQWV7LHyNeIi3xVd6acTeIKkrIGHdSCNFILKW4omGZVPIC7yHYBxwCivoUbGaywp6/tz2cxsz3mkDKm8ZcQkGz3XI55a2Tz5uAWwqsdYw9EOvEx8pGPX7elcNpPzGUoqg8pbvAvCaHWWFPkWxCP0tSitoz3fJC7nZ1lS1C/kspkWr6mkYqm8pWQFYbQisHq325g+Pl9qiE8xl/ggmPd73Hre92oum5kz1J9DpBhU3pJ6QRgtRTxKr+txqyHeYNh1c8nHRcD72nAoaabyFhFJIZ2zQkQkhVTeIiIppPIWEUkhlbeISAqpvEVEUkjlLSKSQipvEZEUUnmLiKSQyltEJIVU3iIiKaTyFhFJIZW3iEgKqbxFRFJI5S0ikkIqbxGRFFJ5i4ikkMpbRCSFVN4iIimk8hYRSSGVt4hICqm8RURSSOUtIpJCKm8RkRRSeYuIpJDKW0QkhVTeIiIppPIWEUkhlbeISAqpvEVEUkjlLSKSQipvEZEUUnmLiKSQyltEJIVU3iIiKaTyFhFJIZW3iEgKqbxFRFJI5S0ikkL/D5lnP64s8C73AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pie_count_plot(feature):\n",
    "    list_gen=df[feature].value_counts().plot.pie(figsize=(6, 6))\n",
    "    title = 'Distribution by: ', feature\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "    \n",
    "for value in ['birthYear', 'drivinglicense', 'education', 'gender', 'redScore', 'steps', 'win']:\n",
    "    pie_count_plot(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='gender'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('win')['gender'].value_counts().plot.pie(figsize=(6, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>aggressively_value</th>\n",
       "      <th>blueScore</th>\n",
       "      <th>computer_value</th>\n",
       "      <th>generously_value</th>\n",
       "      <th>redScore</th>\n",
       "      <th>steps</th>\n",
       "      <th>wisely_value</th>\n",
       "      <th>predictable_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>4.292135</td>\n",
       "      <td>-25.173184</td>\n",
       "      <td>5.797753</td>\n",
       "      <td>3.674157</td>\n",
       "      <td>-25.977654</td>\n",
       "      <td>9.346369</td>\n",
       "      <td>4.481928</td>\n",
       "      <td>4.575221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>4.309701</td>\n",
       "      <td>-25.496269</td>\n",
       "      <td>5.764925</td>\n",
       "      <td>3.817164</td>\n",
       "      <td>-25.660448</td>\n",
       "      <td>9.100746</td>\n",
       "      <td>4.361789</td>\n",
       "      <td>4.325153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        aggressively_value  blueScore  computer_value  generously_value  \\\n",
       "gender                                                                    \n",
       "female            4.292135 -25.173184        5.797753          3.674157   \n",
       "male              4.309701 -25.496269        5.764925          3.817164   \n",
       "\n",
       "         redScore     steps  wisely_value  predictable_value  \n",
       "gender                                                        \n",
       "female -25.977654  9.346369      4.481928           4.575221  \n",
       "male   -25.660448  9.100746      4.361789           4.325153  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('gender').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics of the red agent movements:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the statistics table from the log column: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>action</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log.red</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a1 a2</th>\n",
       "      <td>down</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a2</th>\n",
       "      <td>right</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a2</th>\n",
       "      <td>stay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a3</th>\n",
       "      <td>down</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a3</th>\n",
       "      <td>right</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b1</th>\n",
       "      <td>stay</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b1</th>\n",
       "      <td>up</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b4</th>\n",
       "      <td>up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b5</th>\n",
       "      <td>stay</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b5</th>\n",
       "      <td>up</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        action  value\n",
       "log.red              \n",
       "a1 a2     down      2\n",
       "a1 a2    right     32\n",
       "a1 a2     stay      1\n",
       "a1 a3     down      5\n",
       "a1 a3    right    106\n",
       "...        ...    ...\n",
       "b6 b1     stay     11\n",
       "b6 b1       up      3\n",
       "b6 b4       up      1\n",
       "b6 b5     stay      6\n",
       "b6 b5       up     34\n",
       "\n",
       "[195 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics_raw = pd.DataFrame.from_dict(data[branch], orient='index')\n",
    "log_red = flat_table.normalize(statistics_raw).iloc[:,-2:]\n",
    "log_red = log_red.dropna()\n",
    "log_red['log.red'] = log_red['log.red'] + ' ' + log_red['log.blue']\n",
    "log_red = log_red.drop('log.blue', axis=1)\n",
    "log_red['log.red'] = log_red.apply(lambda x: x['log.red'].rsplit(' ', 1)[0], axis = 1)\n",
    "for substr in ['from: ', ', action:']:\n",
    "    log_red['log.red'] = log_red.apply(lambda x: x['log.red'].replace(substr, '', 2), axis = 1)\n",
    "log_red['log.red'] = log_red['log.red'].str.split().apply(reversed).apply(' '.join)\n",
    "log_red['value']=1\n",
    "log_red['action'] = log_red['log.red'].str.split().str[1]\n",
    "log_red['log.red'] = log_red.apply(lambda x: x['log.red'].replace(str(x['action'])+' ', '', 2), axis = 1)\n",
    "\n",
    "log_red = log_red.groupby(['log.red', 'action']).count()\n",
    "log_red.reset_index(inplace=True)\n",
    "log_red = log_red.set_index('log.red')\n",
    "\n",
    "# s_r = df['log'].apply(pd.Series).iloc[:,1:]\n",
    "# s_r.columns = s_r.columns.astype(str)\n",
    "# s_r = flat_table.normalize(s_r).drop('index', axis=1)\n",
    "# log_red = pd.concat([s_r.melt(id_vars=[str(i)+'.blue' for i in range(1,int(len(s_r.columns)/2))],var_name='red', value_name=\"log.red\").iloc[:,-2:].dropna(),\n",
    "#            s_r.melt(id_vars=[str(i)+'.red' for i in range(1,int(len(s_r.columns)/2))],var_name='blue', value_name=\"log.blue\").iloc[:,-2:].dropna()\n",
    "#           ], axis=1)\n",
    "# log_red = log_red.drop(['red','blue'],axis=1)\n",
    "# log_red['log.red'] = log_red['log.red'] + ' ' + log_red['log.blue']\n",
    "# log_red = log_red.drop('log.blue', axis=1)\n",
    "# log_red['log.red'] = log_red.apply(lambda x: x['log.red'].rsplit(' ', 1)[0], axis = 1)\n",
    "# for substr in ['from: ', ', action:']:\n",
    "#     log_red['log.red'] = log_red.apply(lambda x: x['log.red'].replace(substr, '', 2), axis = 1)\n",
    "# log_red['log.red'] = log_red['log.red'].str.split().apply(reversed).apply(' '.join)\n",
    "# log_red['value']=1\n",
    "# log_red['action'] = log_red['log.red'].str.split().str[1]\n",
    "# log_red['log.red'] = log_red.apply(lambda x: x['log.red'].replace(str(x['action'])+' ', '', 2), axis = 1)\n",
    "\n",
    "# log_red = log_red.groupby(['log.red', 'action']).count()\n",
    "# log_red.reset_index(inplace=True)\n",
    "# log_red = log_red.set_index('log.red')\n",
    "\n",
    "log_red.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>action</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log.red</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a1 a5</th>\n",
       "      <td>down</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a5</th>\n",
       "      <td>right</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a5</th>\n",
       "      <td>stay</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        action  value\n",
       "log.red              \n",
       "a1 a5     down      6\n",
       "a1 a5    right    174\n",
       "a1 a5     stay      8"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(log_red.index)\n",
    "log_red[log_red.index == 'a1 a5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>log.red</th>\n",
       "      <th>right</th>\n",
       "      <th>stay</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a1 a2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a1 a3</td>\n",
       "      <td>106.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a1 a4</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a1 a5</td>\n",
       "      <td>174.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a1 b1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>b6 a4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>b6 a5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>b6 b1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>b6 b4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>b6 b5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   log.red  right  stay    up  down\n",
       "0    a1 a2   32.0   1.0   0.0   2.0\n",
       "1    a1 a3  106.0  10.0   0.0   5.0\n",
       "2    a1 a4  161.0   0.0   0.0   0.0\n",
       "3    a1 a5  174.0   8.0   0.0   6.0\n",
       "4    a1 b1    0.0  46.0  13.0   0.0\n",
       "..     ...    ...   ...   ...   ...\n",
       "88   b6 a4   11.0   1.0   0.0   0.0\n",
       "89   b6 a5   36.0   5.0   0.0   5.0\n",
       "90   b6 b1    0.0  11.0   3.0   0.0\n",
       "91   b6 b4    0.0   0.0   1.0   0.0\n",
       "92   b6 b5    0.0   6.0  34.0   0.0\n",
       "\n",
       "[93 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame(index=log_red.index.drop_duplicates(), columns=['right', 'stay', 'up', 'down'])\n",
    "# result.reset_index(level=0, inplace=True)\n",
    "for action in ['right', 'stay', 'up', 'down']:\n",
    "    result[action] = log_red[log_red['action'] == action]['value']\n",
    "result = result.fillna(0)\n",
    "result.reset_index(inplace=True)\n",
    "result.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After Laplace's Smoothing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>right</th>\n",
       "      <th>stay</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log.red</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a1 a2</th>\n",
       "      <td>33.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a3</th>\n",
       "      <td>107.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a4</th>\n",
       "      <td>162.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a5</th>\n",
       "      <td>175.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 b1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</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>b6 a4</th>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 a5</th>\n",
       "      <td>37.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         right  stay    up  down\n",
       "log.red                         \n",
       "a1 a2     33.0   2.0   0.0   3.0\n",
       "a1 a3    107.0  11.0   0.0   6.0\n",
       "a1 a4    162.0   1.0   0.0   1.0\n",
       "a1 a5    175.0   9.0   0.0   7.0\n",
       "a1 b1      0.0  47.0  14.0   0.0\n",
       "...        ...   ...   ...   ...\n",
       "b6 a4     12.0   2.0   0.0   1.0\n",
       "b6 a5     37.0   6.0   0.0   6.0\n",
       "b6 b1      0.0  12.0   4.0   0.0\n",
       "b6 b4      0.0   1.0   2.0   0.0\n",
       "b6 b5      0.0   7.0  35.0   0.0\n",
       "\n",
       "[93 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(1,7):\n",
    "    result.loc[result['log.red'].str.endswith('a'+str(i)), ['stay', 'right', 'down']] += 1\n",
    "    \n",
    "for i in range(1,7):\n",
    "    result.loc[result['log.red'].str.endswith('b'+str(i)), ['stay', 'up']] += 1\n",
    "\n",
    "result = result.set_index('log.red')\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert it to Probability table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "pd.options.display.float_format = \"{:,.2f}\".format\n",
    "result_probs = result.div(result.sum(axis=1), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save data on computer for the policy iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_probs.to_json('data/probs1.json' , orient='index', indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To calculate the score:\n",
    "crushing = -100;\n",
    "richToDestination = 30;\n",
    "step = -1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''==================================================\n",
    "Initial set up                              version 1\n",
    "=================================================='''\n",
    "#Define all states\n",
    "all_states=[]\n",
    "for i in range(12):\n",
    "    for j in range(12):\n",
    "            all_states.append((i,j))\n",
    "\n",
    "#Define rewards for all states\n",
    "rewards = {}\n",
    "for (i,j) in all_states:\n",
    "    if(i == j): # crushed\n",
    "        rewards[(i,j)] = crushing\n",
    "    elif(i == 0 and j != 0): # blue agent in a1\n",
    "        rewards[(i,j)] = richToDestination\n",
    "    else:\n",
    "        rewards[(i,j)] = step\n",
    "\n",
    "#Dictionnary of possible actions. We have two \"end\" states (1,2 and 2,2)\n",
    "actions = {}\n",
    "for (i,j) in all_states:\n",
    "    if i != 0 and i != j: #if s=(i, j) is not an \"end\" state\n",
    "        actions[(i,j)] = (\"stay\", \"left\", \"down\") if (i < 6) else (\"up\", \"stay\")\n",
    "\n",
    "#Define an initial policy\n",
    "Policy={}\n",
    "for s in actions.keys():\n",
    "    Policy[s] = np.random.choice(actions[s])\n",
    "\n",
    "probs = json.load(open('data/probs1.json',))\n",
    "    \n",
    "#Define initial value function \n",
    "V={}\n",
    "for (i,j) in all_states:\n",
    "    if (i,j) in actions.keys():\n",
    "        V[(i,j)] = step\n",
    "    elif(i == 0 and j != 0): # blue agent in a1\n",
    "        V[(i,j)] = richToDestination\n",
    "    elif(i == j): # crushed\n",
    "        V[(i,j)] = crushing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adapt_state(s):\n",
    "    # takes (i,j) format state and convert it to \"a|b[1-6] a|b[1-6]\" format\n",
    "    i = s[0]\n",
    "    j = s[1]\n",
    "    if i < 6:\n",
    "        blueState = 'a' + str(i+1)\n",
    "    else:\n",
    "        blueState = 'b' + str(i-5)\n",
    "    if j < 6:\n",
    "        redState = 'a' + str(j+1)\n",
    "    else:\n",
    "        redState = 'b' + str(j-5)\n",
    "    return blueState + ' ' + redState"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a1 a2\n",
      "a1 b2\n",
      "b1 b3\n",
      "b5 b6\n"
     ]
    }
   ],
   "source": [
    "print(adapt_state((0,1)))\n",
    "print(adapt_state((0,7)))\n",
    "print(adapt_state((6,8)))\n",
    "print(adapt_state((10,11)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def newPosGivenAction(s, a, agent_i):\n",
    "    # assert check\n",
    "    if s[agent_i] == 5 and agent_i == 1:\n",
    "        actions=[\"stay\"]\n",
    "    elif s[agent_i] == 0 and agent_i == 0:\n",
    "        actions=[\"stay\"]\n",
    "    elif s[agent_i] < 6 and agent_i == 1:\n",
    "        actions=[\"right\",\"stay\", \"down\"]\n",
    "    elif s[agent_i] < 6 and agent_i == 0:\n",
    "        actions=[\"left\",\"stay\", \"down\"]\n",
    "    else:\n",
    "        actions=[\"stay\", \"up\"]\n",
    "    assert a in actions , \"action: \" + str(a) + \" is not vallid at state: \" + str(s) + \" for agent: \" + str(agent_i)\n",
    "    # agent_i = 0 for blue and 1 for red\n",
    "    newPos = -1\n",
    "    if a == \"up\":\n",
    "        newPos = s[agent_i]-6\n",
    "    if a == \"down\":\n",
    "        newPos = s[agent_i]+6\n",
    "    if a == \"left\":\n",
    "        newPos = s[agent_i]-1\n",
    "    if a == \"right\":\n",
    "        newPos = s[agent_i]+1\n",
    "    if a == \"stay\":\n",
    "        newPos = s[agent_i]\n",
    "    assert newPos > -1 and newPos < 12 , \"Got out from board!\\naction: \" + str(a) + \" ,state: \" + str(s) + \" for agent: \" + str(agent_i)\n",
    "    return newPos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def randomProbForAction(s):\n",
    "    # if aX return 1/3 else (it is bX) return 1/2\n",
    "    if s[1] == 5: # a6\n",
    "        return 1\n",
    "    if s[1] < 6:\n",
    "        return 1.0/3\n",
    "    else:\n",
    "        return 1.0/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nextStateDistibution(s, a):\n",
    "    # given state s and action a, return distibution of possibles new states by probs\n",
    "    nxt = {}\n",
    "#     print('s: ', s)\n",
    "#     print('a: ', a)\n",
    "    bluePossition = newPosGivenAction(s, a, 0)\n",
    "#     print('bluePossition: ', bluePossition)\n",
    "    \n",
    "    # red possible action given state\n",
    "    if s[1] == 5:\n",
    "        actions=[\"stay\"]\n",
    "    elif s[1] < 6:\n",
    "        actions=[\"right\",\"stay\", \"down\"]\n",
    "    else:\n",
    "        actions=[\"stay\", \"up\"]\n",
    "    for r_a in actions:\n",
    "        redPossition = newPosGivenAction(s, r_a, 1)\n",
    "#         print('redPossition: '+ str(redPossition))\n",
    "        newState = (bluePossition, redPossition)\n",
    "        try:\n",
    "            nxt[newState] = probs[adapt_state(s)][r_a]\n",
    "#                 nxt[newState] = {probs[adapt_state(s)][r_a], r_a, adapt_state(newState)}\n",
    "        except KeyError:\n",
    "#                 print(\"Warning! State not found: assume random behavior. \", s)\n",
    "            nxt[newState] = randomProbForAction(s)\n",
    "    assert abs(sum(nxt.values()) - 1.0) < 0.0001, \"probs not sum to 1.0. \\nstate: \" + str(s) + \" action: \" + str(a) + \" nxt: \" + str(nxt) + \"\\nsum of probs: \" + str(sum(nxt.values()))\n",
    "    return nxt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(4, 5): 0.5, (4, 11): 0.5}\n",
      "{(1, 5): 1}\n"
     ]
    }
   ],
   "source": [
    "pp.pprint(nextStateDistibution((10, 11), \"up\"))\n",
    "pp.pprint(nextStateDistibution((1, 5), \"stay\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(6, 1): 0.5, (6, 7): 0.5}\n"
     ]
    }
   ],
   "source": [
    "pp.pprint(nextStateDistibution((6, 7), \"stay\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a1 a3'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adapt_state((0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 )  13.63945021547022\n",
      "converge after: 140 iterations\n"
     ]
    }
   ],
   "source": [
    "'''==================================================\n",
    "Value Iteration\n",
    "=================================================='''\n",
    "\n",
    "#Hyperparameters\n",
    "SMALL_ENOUGH = 0.0001\n",
    "GAMMA = 0.995\n",
    "\n",
    "iteration = 1\n",
    "while True:\n",
    "    biggest_change = 0\n",
    "    for s in all_states:\n",
    "        if s in Policy:\n",
    "            \n",
    "            old_v = V[s]\n",
    "            new_v = np.NINF\n",
    "            \n",
    "            for a in actions[s]:\n",
    "                nxt = nextStateDistibution(s, a)\n",
    "                v=0\n",
    "                #Calculate the value\n",
    "                for next_state, prob in nxt.items():\n",
    "                    v += prob * (rewards[s] + GAMMA * V[next_state])\n",
    "                if v > new_v: #Is this the best action so far? If so, keep it\n",
    "                    new_v = v\n",
    "                    Policy[s] = a\n",
    "\n",
    "       #Save the best of all actions for the state                                \n",
    "            V[s] = new_v\n",
    "            biggest_change = max(biggest_change, np.abs(old_v - V[s]))\n",
    "\n",
    "            \n",
    "   #See if the loop should stop now         \n",
    "    if biggest_change < SMALL_ENOUGH:\n",
    "        print('converge after: ' + str(iteration) + ' iterations')\n",
    "        break\n",
    "    iteration += 1\n",
    "    \n",
    "#     #Print every 100 iterations\n",
    "    if(iteration % 100 == 0):\n",
    "        print(iteration, ') ', V[(5,0)]) #V[(a6, a1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convertStates():\n",
    "    P_convert= {}\n",
    "    V_convert = {}\n",
    "    for s,v in V.items():\n",
    "        V_convert[adapt_state(s)] = v\n",
    "    \n",
    "#     print()\n",
    "    for s,p in Policy.items():\n",
    "        P_convert[adapt_state(s)] = p\n",
    "    \n",
    "    pp.pprint(V_convert)\n",
    "    pp.pprint(P_convert)\n",
    "    return P_convert, V_convert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iterations:  140\n",
      "{'a1 a1': -100,\n",
      " 'a1 a2': 30,\n",
      " 'a1 a3': 30,\n",
      " 'a1 a4': 30,\n",
      " 'a1 a5': 30,\n",
      " 'a1 a6': 30,\n",
      " 'a1 b1': 30,\n",
      " 'a1 b2': 30,\n",
      " 'a1 b3': 30,\n",
      " 'a1 b4': 30,\n",
      " 'a1 b5': 30,\n",
      " 'a1 b6': 30,\n",
      " 'a2 a1': 2.3596381418071464,\n",
      " 'a2 a2': -100,\n",
      " 'a2 a3': 28.85,\n",
      " 'a2 a4': 28.85,\n",
      " 'a2 a5': 28.849999999999998,\n",
      " 'a2 a6': 28.85,\n",
      " 'a2 b1': 0.5576111250660646,\n",
      " 'a2 b2': 28.85,\n",
      " 'a2 b3': 28.85,\n",
      " 'a2 b4': 28.85,\n",
      " 'a2 b5': 28.85,\n",
      " 'a2 b6': 28.85,\n",
      " 'a3 a1': 16.152708324953405,\n",
      " 'a3 a2': 18.65688587289803,\n",
      " 'a3 a3': -100,\n",
      " 'a3 a4': 27.705750000000002,\n",
      " 'a3 a5': 27.705749997229425,\n",
      " 'a3 a6': 27.705750000000002,\n",
      " 'a3 b1': 15.237855649903612,\n",
      " 'a3 b2': 18.807600407521857,\n",
      " 'a3 b3': 27.705750000000002,\n",
      " 'a3 b4': 27.705750000000002,\n",
      " 'a3 b5': 27.705750000000002,\n",
      " 'a3 b6': 27.705750000000002,\n",
      " 'a4 a1': 16.258923532165667,\n",
      " 'a4 a2': 13.432810592277479,\n",
      " 'a4 a3': 13.615465910342198,\n",
      " 'a4 a4': -100,\n",
      " 'a4 a5': 26.567221246424374,\n",
      " 'a4 a6': 26.567221250000003,\n",
      " 'a4 b1': 14.330718076666361,\n",
      " 'a4 b2': 17.595945964737123,\n",
      " 'a4 b3': 11.211154744427688,\n",
      " 'a4 b4': 26.567221250000003,\n",
      " 'a4 b5': 26.567221247855887,\n",
      " 'a4 b6': 26.567221250000003,\n",
      " 'a5 a1': 13.887443842671816,\n",
      " 'a5 a2': 13.072036485485718,\n",
      " 'a5 a3': 11.236910248949671,\n",
      " 'a5 a4': 12.840946628307675,\n",
      " 'a5 a5': -100,\n",
      " 'a5 a6': 25.434385143750003,\n",
      " 'a5 b1': 14.388885760701635,\n",
      " 'a5 b2': 14.436806387114764,\n",
      " 'a5 b3': 11.749958710841916,\n",
      " 'a5 b4': 11.248620344384392,\n",
      " 'a5 b5': 25.434385141584958,\n",
      " 'a5 b6': 25.434385143750003,\n",
      " 'a6 a1': 13.63945021547022,\n",
      " 'a6 a2': 15.20582055883505,\n",
      " 'a6 a3': 16.356294735119793,\n",
      " 'a6 a4': 17.19912025697044,\n",
      " 'a6 a5': 19.412812694890622,\n",
      " 'a6 a6': -100,\n",
      " 'a6 b1': 13.226225930372713,\n",
      " 'a6 b2': 13.773502285564932,\n",
      " 'a6 b3': 14.947276556931147,\n",
      " 'a6 b4': 15.747527069009013,\n",
      " 'a6 b5': 16.15420656543756,\n",
      " 'a6 b6': 24.307213218031254,\n",
      " 'b1 a1': -14.266666666666666,\n",
      " 'b1 a2': 28.85,\n",
      " 'b1 a3': 28.85,\n",
      " 'b1 a4': 28.85,\n",
      " 'b1 a5': 28.85,\n",
      " 'b1 a6': 28.85,\n",
      " 'b1 b1': -100,\n",
      " 'b1 b2': 28.85,\n",
      " 'b1 b3': 28.85,\n",
      " 'b1 b4': 28.85,\n",
      " 'b1 b5': 28.85,\n",
      " 'b1 b6': 28.85,\n",
      " 'b2 a1': 2.9467512842282773,\n",
      " 'b2 a2': 10.611650001502952,\n",
      " 'b2 a3': 27.705750000000002,\n",
      " 'b2 a4': 27.705750000000002,\n",
      " 'b2 a5': 27.705750000000002,\n",
      " 'b2 a6': 27.705750000000002,\n",
      " 'b2 b1': 0.874781833578498,\n",
      " 'b2 b2': -100,\n",
      " 'b2 b3': 27.705750000000002,\n",
      " 'b2 b4': 27.705750000000002,\n",
      " 'b2 b5': 27.705750000000002,\n",
      " 'b2 b6': 27.705750000000002,\n",
      " 'b3 a1': 17.354238452343488,\n",
      " 'b3 a2': 20.444811127697335,\n",
      " 'b3 a3': 22.029106207172394,\n",
      " 'b3 a4': 26.567221247817592,\n",
      " 'b3 a5': 26.567221249448657,\n",
      " 'b3 a6': 26.567221250000003,\n",
      " 'b3 b1': 15.72814689293034,\n",
      " 'b3 b2': 19.247262175460822,\n",
      " 'b3 b3': -100,\n",
      " 'b3 b4': 26.567221250000003,\n",
      " 'b3 b5': 26.567221249212366,\n",
      " 'b3 b6': 26.567221250000003,\n",
      " 'b4 a1': 13.600779978674574,\n",
      " 'b4 a2': 12.466276768892273,\n",
      " 'b4 a3': 12.20413750769676,\n",
      " 'b4 a4': 13.760407518097194,\n",
      " 'b4 a5': 25.434385143402558,\n",
      " 'b4 a6': 25.434385143750003,\n",
      " 'b4 b1': 13.898585962293012,\n",
      " 'b4 b2': 13.746419771043794,\n",
      " 'b4 b3': 11.949316178269253,\n",
      " 'b4 b4': -100,\n",
      " 'b4 b5': 25.434385140904432,\n",
      " 'b4 b6': 25.434385143750003,\n",
      " 'b5 a1': 12.606746117812932,\n",
      " 'b5 a2': 11.182695150270451,\n",
      " 'b5 a3': 11.329817033697818,\n",
      " 'b5 a4': 9.583436983926328,\n",
      " 'b5 a5': 10.439745083602478,\n",
      " 'b5 a6': 24.307213218031254,\n",
      " 'b5 b1': 13.150629762434868,\n",
      " 'b5 b2': 12.685649329118739,\n",
      " 'b5 b3': 10.350886770882168,\n",
      " 'b5 b4': 11.512681119696047,\n",
      " 'b5 b5': -100,\n",
      " 'b5 b6': 24.307213218031254,\n",
      " 'b6 a1': 13.346608839247917,\n",
      " 'b6 a2': 15.231600795170667,\n",
      " 'b6 a3': 16.51204259414346,\n",
      " 'b6 a4': 17.778934821420467,\n",
      " 'b6 a5': -71.79928356983847,\n",
      " 'b6 a6': -100.5,\n",
      " 'b6 b1': 12.262884341638856,\n",
      " 'b6 b2': 13.417213115088991,\n",
      " 'b6 b3': 14.573526717795342,\n",
      " 'b6 b4': 16.14966598243832,\n",
      " 'b6 b5': 17.77536311484046,\n",
      " 'b6 b6': -100}\n",
      "{'a2 a1': 'down',\n",
      " 'a2 a3': 'left',\n",
      " 'a2 a4': 'left',\n",
      " 'a2 a5': 'left',\n",
      " 'a2 a6': 'left',\n",
      " 'a2 b1': 'down',\n",
      " 'a2 b2': 'left',\n",
      " 'a2 b3': 'left',\n",
      " 'a2 b4': 'left',\n",
      " 'a2 b5': 'left',\n",
      " 'a2 b6': 'left',\n",
      " 'a3 a1': 'down',\n",
      " 'a3 a2': 'down',\n",
      " 'a3 a4': 'left',\n",
      " 'a3 a5': 'left',\n",
      " 'a3 a6': 'left',\n",
      " 'a3 b1': 'down',\n",
      " 'a3 b2': 'down',\n",
      " 'a3 b3': 'left',\n",
      " 'a3 b4': 'left',\n",
      " 'a3 b5': 'left',\n",
      " 'a3 b6': 'left',\n",
      " 'a4 a1': 'left',\n",
      " 'a4 a2': 'stay',\n",
      " 'a4 a3': 'left',\n",
      " 'a4 a5': 'left',\n",
      " 'a4 a6': 'left',\n",
      " 'a4 b1': 'left',\n",
      " 'a4 b2': 'left',\n",
      " 'a4 b3': 'stay',\n",
      " 'a4 b4': 'left',\n",
      " 'a4 b5': 'left',\n",
      " 'a4 b6': 'left',\n",
      " 'a5 a1': 'left',\n",
      " 'a5 a2': 'left',\n",
      " 'a5 a3': 'stay',\n",
      " 'a5 a4': 'left',\n",
      " 'a5 a6': 'left',\n",
      " 'a5 b1': 'left',\n",
      " 'a5 b2': 'left',\n",
      " 'a5 b3': 'left',\n",
      " 'a5 b4': 'stay',\n",
      " 'a5 b5': 'left',\n",
      " 'a5 b6': 'left',\n",
      " 'a6 a1': 'stay',\n",
      " 'a6 a2': 'down',\n",
      " 'a6 a3': 'down',\n",
      " 'a6 a4': 'stay',\n",
      " 'a6 a5': 'left',\n",
      " 'a6 b1': 'left',\n",
      " 'a6 b2': 'stay',\n",
      " 'a6 b3': 'down',\n",
      " 'a6 b4': 'down',\n",
      " 'a6 b5': 'stay',\n",
      " 'a6 b6': 'left',\n",
      " 'b1 a1': 'up',\n",
      " 'b1 a2': 'up',\n",
      " 'b1 a3': 'up',\n",
      " 'b1 a4': 'up',\n",
      " 'b1 a5': 'up',\n",
      " 'b1 a6': 'up',\n",
      " 'b1 b2': 'up',\n",
      " 'b1 b3': 'up',\n",
      " 'b1 b4': 'up',\n",
      " 'b1 b5': 'up',\n",
      " 'b1 b6': 'up',\n",
      " 'b2 a1': 'stay',\n",
      " 'b2 a2': 'up',\n",
      " 'b2 a3': 'up',\n",
      " 'b2 a4': 'up',\n",
      " 'b2 a5': 'up',\n",
      " 'b2 a6': 'up',\n",
      " 'b2 b1': 'stay',\n",
      " 'b2 b3': 'up',\n",
      " 'b2 b4': 'up',\n",
      " 'b2 b5': 'up',\n",
      " 'b2 b6': 'up',\n",
      " 'b3 a1': 'stay',\n",
      " 'b3 a2': 'stay',\n",
      " 'b3 a3': 'up',\n",
      " 'b3 a4': 'up',\n",
      " 'b3 a5': 'up',\n",
      " 'b3 a6': 'up',\n",
      " 'b3 b1': 'stay',\n",
      " 'b3 b2': 'stay',\n",
      " 'b3 b4': 'up',\n",
      " 'b3 b5': 'up',\n",
      " 'b3 b6': 'up',\n",
      " 'b4 a1': 'up',\n",
      " 'b4 a2': 'up',\n",
      " 'b4 a3': 'stay',\n",
      " 'b4 a4': 'stay',\n",
      " 'b4 a5': 'up',\n",
      " 'b4 a6': 'up',\n",
      " 'b4 b1': 'up',\n",
      " 'b4 b2': 'up',\n",
      " 'b4 b3': 'up',\n",
      " 'b4 b5': 'up',\n",
      " 'b4 b6': 'up',\n",
      " 'b5 a1': 'up',\n",
      " 'b5 a2': 'up',\n",
      " 'b5 a3': 'up',\n",
      " 'b5 a4': 'stay',\n",
      " 'b5 a5': 'up',\n",
      " 'b5 a6': 'up',\n",
      " 'b5 b1': 'up',\n",
      " 'b5 b2': 'up',\n",
      " 'b5 b3': 'up',\n",
      " 'b5 b4': 'up',\n",
      " 'b5 b6': 'up',\n",
      " 'b6 a1': 'up',\n",
      " 'b6 a2': 'stay',\n",
      " 'b6 a3': 'stay',\n",
      " 'b6 a4': 'up',\n",
      " 'b6 a5': 'up',\n",
      " 'b6 a6': 'up',\n",
      " 'b6 b1': 'up',\n",
      " 'b6 b2': 'up',\n",
      " 'b6 b3': 'up',\n",
      " 'b6 b4': 'stay',\n",
      " 'b6 b5': 'up'}\n"
     ]
    }
   ],
   "source": [
    "# print the results:\n",
    "print(\"iterations: \", iteration)\n",
    "optimal_policy, optimal_V = convertStates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Value Iteration results:')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8LtczwXLi9ReInNyRlbvC3vsk6R8PttQjHeOUm2c1Z/536jf4PQ+r9F5hOV18QYxeG9xJFSqcrt/WbdFDXRO1e88+jystORkZa9W376tasSJD3bt3UKdOd57wY2ZnH1CvXiO0YkWGIiMraeTIXqpTJ1ppad/p5ZfH68CBHJUpE6GePR9S06aXFMOz8Af2Ue7Iyg05+QedCIFO+kGECdO+1OjxqRo78rFDyzp0HnXo75eevl+/7z51v+gdlJObp94vTNT3y39VxQqn6+uZgzVvwTJ1uOtafZG2XMPf+Fj/fOx2/fOx2/X0i8lel+upYFnNW7BMzwyZrNzcPL3Q51717Bx/SmcVdJvi9ReAnNyRlbvC3vuaN43VrTc1UuObeys7O0dnRFX2sEJ/KCynN4cmqPcLk/TVop/U8e7r1P2RWzXw5WkeVlmyIiMrqV+/BM2bt/CY77tu3Sb16fOKJkx4MWD5tGmfqnLlipo7N0kzZ/5bw4e/o1deeUpVq1bWm28+o+joKK1a9Zs6deqvBQvGF9dT8Rz7KHdk5Yac4FdHPZzBGFPWGNPRGHNjwb/vM8a8bozpbIwpE5oST0za4p+1feeeoNe3vbWJpqZ8HcKK/Clr8059v/xXSdKevX/q5zXrdWbNaro1rpEmvv9vSdLE9/+t2266wsMq/SFYVvMWLFNubp4kafG3q1W7ZjUPq/ResJwOx+uPnI4FWbkr7L0voUOchr/xsbKzcyRJW7bt8qI0Xyksp3rn1tJXi36SJM1f8IPuuKWxF6WFTFRUpBo2bKCIiCN/V0pJ+Vzt2j2p+Piu6t//deXm5jo95vz5i9SmzQ2SpJYtmyk9famstYqNravo6ChJUv36Mdq/P1vZ2QeK78l4jH2UO7JyQ07+EW5syC6lQVFzIvxLUmtJTxhjJki6S9IiSVdKGlvCtZW4Zo3P16atvyvj1yyvS/GVmDrVdemF5+ib79aoRvUqytq8U1L+jqxG9SreFuczh2d1uI73XKfUL5Z6VJX/FJYTr78jkZM7sjp29c6tqWaNz9e/U57Xp1P7q1HD87wuyZd+WrXu0ID5na2bqE6tKI8r8kZGxlrNnr1AyclDlZIySmFhYZox40un+27atE21alWXJEVEhKtSpQrasSNw0Co19WvFxtZV2bKl4jepY8Y+yh1ZuSEn+ElRhzNcbK1taIyJkLRe0pnW2lxjzERJQb8hGWMSJCVI0pgxY4qt2OJ2d/zVmsbIXYAK5U9T8pju6jng3UKPAbUqHaNjoRAsq15d7lBuTp4mT//Kw+r8I1hOvP4CkZM7sjo+ERHhqlaloprHP6MrLqmriW88oQv++oTXZfnOIz3H6OUBD6j3E200c+63yj6Q43VJnkhPX6rlyzPUrt2TkqQ//8xWVFSkJKlz50Fat26TDhzI0caNWxQf31WS1LHj7Wrb9sYiH3v16t80fPg7GjduYInV7yX2Ue7Iyg05eY85EQIVNYgQZowpK6mCpPKSqkjaLuk0SUGHjq21SZKSDv7ziRc+L4ZSi1d4eJjib26sZq37el2Kb0REhCt5THdNmZ6mlDnfSJI2b/1dNWtEKmvzTtWsEaktW2l/lQrPSpLub9dct9xwmVrdO8jD6vwjWE68/gKRkzuyOn7rN27XR3MWS5KWLM1QnrWqXq2Stm7f7XFl/rIqY4Nuuz//GP9659ZUqxaXeltQCZg0aaamTk2VJCUlPXvoEIPDWWvVpk0L9ejxwBHXJSb2kxR8ToTo6Cht3LhVNWtWV05Ornbv3quqVfPn4MjK2qouXQZryJDuiompVdxPzXPso9yRlRtygh8VdTjD25J+lvS9pH6Sphlj3pL0jaTJJVtayWrx14u1KmOD1mdt97oU3xg9LEEr12zQqLGzDi2bOfc/ur9dc0n5X5A/mfsfr8rzlcKyirv2Ej35j9vUrtNw7fsz28Pq/KOwnCRef/+LnNyR1fGb8ekSXds0VlL+l+OyZSIYQCjEwQknjTHq3bWN3po4z+OKil/79q2VkjJKKSmjCh1AkKSmTS9Ramqatm3bKUnauXO31q/f7PT4LVpcpenT83NLTU1TkyYNZYzRrl17lJAwQD16PKBGjWKL5bn4Dfsod2Tlhpz8IcyE7lIaHLUTwVo70hgzpeDvDcaYdyXdKOkta+3iUBR4osa/9riuaXqBqletpDWLXtfzI97X+Clf6K7bm2rqx7T/HHT1lX9R+7bNteynTC2cnf+LwrNDp2j4Gx9r4ptP6IF7rlPm+q26/x+velyp94Jl9fKAB3Ra2TL6ZFL+iPDi79aoa9+3vSzVU8FySv38e15/hyEnd2TlrvD3vs81ZtijWjJ3qLKzc/T/nnzT6zI9V1hOFSucrkc63iRJSpmzWO9O/cLbIkvYli071LZtd+3Z84fCwsI0fvzHmjXrDdWrF6Nu3Tro4Yf7Ky/PqkyZcPXv/6hq165R5GO2axennj1HKC4uQVWqVNTIkfmnz5w4caYyMzcqMXGyEhPzf4saN27gocMkSjv2Ue7Iyg05wa+MtSV+jLstF3NvSa+j1NuXmSxyKtq+zPxTJpJV0dim3JCTO7JyQ05u2J+7y89qlddllAIN2J4csZ9yQ05uCvbnpeQ39OMzc+3skE0M1/qsVr7PsqjDGQAAAAAAACQVPbEiAAAAAACnrHDf9waEFp0IAAAAAADACYMIAAAAAADACYczAAAAAAAQRJgJ2byKpQKdCAAAAAAAwAmdCAAAAAAABMEv74HIAwAAAAAAOKETAQAAAACAIMI4xWMAOhEAAAAAAIATOhEAAAAAAAginE6EAHQiAAAAAAAAJ3QiAAAAAAAQRJixXpfgK3QiAAAAAAAAJ3QiAAAAAAAQBGdnCEQnAgAAAAAAcEInAgAAAAAAQdCJEMhYW+KTRDALBQAAAACcvE7qr9npm2eG7Dtt0xqtfZ9lSDoRysXcG4rVlGr7MpPJycG+zGRJbFMu2KbckJM7snJDTm7Yn7tjm3KTv02t8rqMUqIB25QDXntuDu7PT2bMARCIPAAAAAAAgBMGEQAAAAAAgBMmVgQAAAAAIAjj+1kKQotOBAAAAAAA4IROBAAAAAAAgqARIRCdCAAAAAAAwAmdCAAAAAAABMGcCIHoRAAAAAAAAE7oRAAAAAAAIAh+eQ9EHgAAAAAAwAmdCAAAAAAABGGM9boEX6ETAQAAAAAAOKETAQAAAACAIDg5QyA6EQAAAAAAgBM6EQAAAAAACMLQihCATgQAAAAAAOCETgQAAAAAAIKgESEQnQgAAAAAAMDJST+IMHrYI/rt29FaMnfoEdc98ffW2peZrKiqlTyozH8Ky2pw3/v0/fzhWpw6RFOSnlSVyuU9rNAfyMkNrz03dWpV05zJT+vbecP0n8+GqfPDN0uS+nVvq4zFiVo4+0UtnP2iWl5/qbeF+gBZueP154ac3PC+J2VkrNU99/xTF13URm+//WGxPGZ29gF16zZEcXEJuuuuHlq3bpMkKS3tO915ZzfddlsX3XlnN6WnLy2W9fkJ+3M35AS/OukHESZM+1LxHV86YnmdWtV0Q/OLlbluiwdV+VNhWc1bsEyN4nqpccuntPq/G9Wzc7xH1fkHObnhtecmJzdPvV+YqMtv6Klr45/RIx1v0vn1a0uSXhs7S01a9VGTVn2U+vn33hbqA2TljtefG3Jyw/ueFBlZSf36JahTpzbHfN916zapQ4c+RyyfNu1TVa5cUXPnJunBB+M1fPg7kqSqVSvrzTef0YwZr+ull7qrV68RJ1q+77A/d0NO/hFmQncpDYocRDDGnGeM+acx5lVjzAhjzKPGmMqhKK44pC3+Wdt37jli+dBnO6rf4PdkrQdF+VRhWc1bsEy5uXmSpMXfrlbtmtW8KM1XyMkNrz03WZt36vvlv0qS9uz9Uz+vWa8z2X4KRVbueP25ISc3vO9JUVGRatiwgSIijpxOLCXlc7Vr96Ti47uqf//XlZub6/SY8+cvUps2N0iSWrZspvT0pbLWKja2rqKjoyRJ9evHaP/+bGVnHyi+J+MD7M/dkBP86qiDCMaYrpJGSzpd0pWSTpN0lqSFxpjrSrq4knJrXCNtyNquZT9lel1KqdLxnuuU+sXJ11JX3MgpOF57RxdTp7ouvfAcffPdGknSow+01OLUIRo97BFFVqngcXX+QlbHjtefG3I6dqfy+15GxlrNnr1AyclDlZIySmFhYZox40un+27atE21alWXJEVEhKtSpQrasWNXwG1SU79WbGxdlS1bpthr9wv2527IyVsmhJfSoKhOhL9LamWtfUHSjZIutNb2k3SzpJHB7mSMSTDGLDHGLElKSiq+aotBudPLqleXOzTw5Wlel1Kq9Opyh3Jz8jR5+ldel+Jr5BQcr72jq1D+NCWP6a6eA97V7j379NaEzxR7zRO66ubeytq8Qy89fb/XJfoGWR07Xn9uyOnYnerve+npS7V8ecahToT09B+0dm2WJKlz50GKj++qhIQBWr58jeLjuyo+vqs++OAzp8devfo3DR/+jgYO7FyST8FT7M/dkBP8xuUUjxGScpXfhVBRkqy1mcaYoEOi1tokSQdHD+wTL3x+onUWm/POjtbZZ52hxXOGSJJq16qm9FmDdc3tT2vTlt89rs6f7m/XXLfccJla3TvI61J8jZyOjtdecBER4Uoe011TpqcpZc43kqTNW/8vk3HJ8/Xhv3p5VZ6vkNXx4fXnhpyOzcn+vjdp0kxNnZoqSUpKevbQIQaHs9aqTZsW6tHjgSOuS0zsJyl/ToQ+fV7RhAkvBlwfHR2ljRu3qmbN6srJydXu3XtVtWr+EcNZWVvVpctgDRnSXTExtYr7qfkC+3M35OQPprS0CIRIUYMIYyV9Y4xZJOkaSUMkyRhzhqTtJVxbiVixcq3OvvzRQ//+OW2Umt3aT9t27PawKv+Ku/YSPfmP23TTXQO1789sr8vxLXIqGq+94EYPS9DKNRs0auysQ8tq1ohU1uadkqT4llfqx5VrParOX8jq+PD6c0NO7k6F97327VurffvWR71N06aX6LHHXtCDD8YrKipSO3fu1t69+1S7do0iH79Fi6s0ffo8XXbZ+UpNTVOTJg1ljNGuXXuUkDBAPXo8oEaNYovr6fgO+3M35AQ/OuoggrX2VWPMZ5IukPSytfbnguVbJDUPQX0nbPxrj+uapheoetVKWrPodT0/4n2Nn/KF12X5UmFZ9ewcr9PKltEnk/pKkhZ/t0Zd+77tcaXeIic3vPbcXH3lX9S+bXMt+ylTC2fn/0r17NApujv+ajWMPVvWSr+t26LH+4z1uFLvkZU7Xn9uyMkN73vSli071LZtd+3Z84fCwsI0fvzHmjXrDdWrF6Nu3Tro4Yf7Ky/PqkyZcPXv/6jTIEK7dnHq2XOE4uISVKVKRY0cmf9r8sSJM5WZuVGJiZOVmDhZkjRu3EBFRUWW5FMMKfbnbsjJP2hECGRsyU89bMvF3FvS6yj19mUmi5yKti8zWZLIygHblBtyckdWbsjJDftzd2xTbvK3qVVel1FKNGCbcsBrz03B/vyk/p79085PQna+ngsib/V9li5zIgAAAAAAcEry/bf6ECvq7AwAAAAAAACS6EQAAAAAACCoMFoRAtCJAAAAAAAAnNCJAAAAAABAEDQiBKITAQAAAAAAOGEQAQAAAACAIIyxIbucWJ3mUmPMQmPM98aYJcaYxgXLjTFmlDFmjTHmB2PM5SeyHgYRAAAAAAAo/YZKGmCtvVRS/4J/S1IrSfULLgmS3jyRlTCIAAAAAABAECaElxNkJVUu+LuKpA0Ff8dLetfmWygp0hhT63hXwsSKAAAAAAD4gDEmQfndAgclWWuTHO/eTVKqMWa48hsGri5YXlvS2sNut65g2cbjqZFBBAAAAAAAfKBgwCDooIEx5jNJNQu5qp+kGyR1t9Z+YIy5W9Lbkm4s7hoZRAAAAAAAIAjjo3M8WmuDDgoYY96V9ETBP6dJGlvw93pJZx120zoFy44LcyIAAAAAAFD6bZB0bcHfLSStLvj7Y0kdC87S0ETS79ba4zqUQaITAQAAAACAoErRL+9/l/SqMSZC0p/6v7kVZkm6RdIaSX9IeuhEVsIgAgAAAAAApZy19itJjQpZbiV1Lq71MIgAAAAAAEAQfpoTwQ9KUWcGAAAAAADwksnvbChRJb4CAAAAAIBnTurf6jP3zAjZd9qYirf5PsuQHM5QLubeUKymVNuXmUxODvZlJktim3LBNuWGnNyRlRtycsP+3B3blBtycpf/+lvldRmlQAO2KQcH9+c4dTAnAgAAAAAAQTAnQiDmRAAAAAAAAE7oRAAAAAAAIAgaEQLRiQAAAAAAAJzQiQAAAAAAQBBhtCIEoBMBAAAAAAA4oRMBAAAAAIAgaEQIRCcCAAAAAABwwiACAAAAAABwwuEMAAAAAAAEYYz1ugRfoRMBAAAAAAA4oRMBAAAAAIAgmFgxEJ0IAAAAAADACZ0IAAAAAAAEYWhFCEAnAgAAAAAAcEInAgAAAAAAQdCIEIhOBAAAAAAA4OSkH0QYPewR/fbtaC2ZO/TQsv497tLi1CFaOPtFzZjYR7Wiq3pYoX8UltWdra/Sfz4bpr2/TtLlDc/zsDr/IKeidX74Zi2ZO1T/+WyYunRqVehtrmlygRbOflH/+WyYPp3aP8QV+kth21TVKhX0yaS+WvblCH0yqa8iq1TwsEJ/qFOrmuZMflrfzhum/3w2TJ0fvjng+if+3lr7MpMVVbWSRxX6B+99bsjJDfsod4VlNbjvffp+/nAtTh2iKUlPqkrl8h5WWLIyMtbqnnv+qYsuaqO33/6wWB4zO/uAunUbori4BN11Vw+tW7dJkpSW9p3uvLObbruti+68s5vS05cWy/r8hPc9/wgL4aU0KC11HrcJ075UfMeXApaNHPOJGrd8Sk1a9dHsed+qzxN3elSdvxSW1YqVa/W3hBH6atHPHlXlP+R0dLEN6uihe1vomtueVuOWT6nVDZfpvLOjA25TpXJ5vTroYd3Vabga3dhT7f/xijfF+kRh29Q/O8fri7TluvjaJ/VF2nL987HbParOP3Jy89T7hYm6/Iaeujb+GT3S8SadX7+2pPwPWjc0v1iZ67Z4XKU/8N7nhpzcsI9yV1hW8xYsU6O4Xmrc8imt/u9G9ewc71F1JS8yspL69UtQp05tjvm+69ZtUocOfY5YPm3ap6pcuaLmzk3Sgw/Ga/jwdyRJVatW1ptvPqMZM17XSy91V69eI060fN/hfQ9+ddIPIqQt/lnbd+4JWLZ7z75Df5cvf7qstaEuy5cKy2rlmg1a/ctGjyryJ3I6uvPr19Y3363Rvj+zlZubpwULf9IdrRoH3Oae+GZKmf2N1m7YJknasm2XF6X6RmHb1K1xjTTx/X9Lkia+/2/ddtMVXpTmK1mbd+r75b9Kkvbs/VM/r1mvM2tWkyQNfbaj+g1+T+zO8/He54ac3LCPcldYVvMWLFNubp4kafG3q1W7YL91MoqKilTDhg0UEXHktGspKZ+rXbsnFR/fVf37v67c3Fynx5w/f5HatLlBktSyZTOlpy+VtVaxsXUVHR0lSapfP0b792crO/tA8T0ZH+B9zz+MCd2lNDjpBxGCea7n3Vq98HX97Y5mev7laV6XA5w0Vqxcq2aNz1e1yIoqd3pZ3Xz9papTKyrgNvXPq6XIKhWUOuUZpc0cpPvaXuNRtf5Vo3oVZW3eKSn/Q0SN6lW8LchnYupU16UXnqNvvlujW+MaaUPWdi37KdPrsnyP9z435FQ09lHHp+M91yn1i5Ov7b4oGRlrNXv2AiUnD1VKyiiFhYVpxowvne67adM21apVXZIUERGuSpUqaMeOwB8fUlO/VmxsXZUtW6bYa/cL3vfgJ0cdRDDGVDHGvGSM+dkYs90Ys80Y81PBssij3C/BGLPEGLMkKSmp2IsuDs8Nm6r6Tbpo8kdpevTBll6XA5w0Vq7ZoJff/FgzJvXRxxN6a+mPvyk3Ly/gNhHhYbr84nPV5sGhuv3+l9SnaxvVO7emRxWXDlb81HBQhfKnKXlMd/Uc8K5ycnLVq8sdGsgXPSe897khp2PHPqpovbrcodycPE2e/pXXpYRcevpSLV+ecagTIT39B61dmyVJ6tx5kOLjuyohYYCWL1+j+Piuio/vqg8++MzpsVev/k3Dh7+jgQM7l+RT8BTve35gQnjxv6I6EaZK2iHpOmttNWttlKTrC5ZNDXYna22StfYKa+0VCQkJxVdtCZgy/asjWq0BnJjxU75Qs9b9FHfXQO38fe8Rh3qsz9quuf/+QX/s269tO3brq0U/q2Hs2R5V60+bt/6umjUiJUk1a0Rqy9ZT+5CPgyIiwpU8prumTE9TypxvdN7Z0Tr7rDO0eM4Q/Zw2SrVrVVP6rMGKPoNfRY+G9z435BQc+6hjc3+75rrlhsv0YNfXvS6l2E2aNPPQF/9Nm7YVehtrrdq0aaGUlFFKSRml1NTRevzx+yRJiYn9lJIySklJz+qii+oduk3btjdKkqKjo7Rx41ZJUk5Ornbv3quqVStLkrKytqpLl8EaMqS7YmJqheDZhh7ve/CjogYRzrHWDrHWZh1cYK3NstYOkVRqP/HXPef/fvG89aYrtCpjg4fVACefM6Ly39zPOjNK8TdfqSkpaQHXz/h0ia6+8i8KDw9TudPL6srL6unn1eu9KNW3Zs79j+5v11xS/ofPT+b+x+OK/GH0sAStXLNBo8bOkpR/+MzZlz+q85t11fnNumr9xu1qektfbdryu8eV+g/vfW7IyQ37KHdx116iJ/9xm9p1Gq59f2Z7XU6xa9++9aEv/gfnKPhfTZteotTUNG3btlOStHPnbq1fv9np8Vu0uErTp8+TJKWmpqlJk4YyxmjXrj1KSBigHj0eUKNGscXyXPyI9z1/MCH8X2lw5KwngX4zxvSSNN5au0mSjDHRkh6UtLaEaysW4197XNc0vUDVq1bSmkWv6/kR7+vm6y9V/bpnKi/PKnP9FnXt87bXZfpCYVnt2LlHIwY+qOrVKuvDf/XSDz/+qts7vFT0g53EyKloyWO6q1rVijpwIFfdnvmXft/1h/7f/fm/KIyd+JlWrtmguV8s1TefDlFentU7kz/Xj6vWeVy1dwrbpoa/8bEmvvmEHrjnOmWu36r7//Gq12V67uor/6L2bZtr2U+ZWjj7RUnSs0OnKPXz770tzId473NDTm7YR7krLKueneN1Wtky+mRSX0nS4u/WqGvfk3O72rJlh9q27a49e/5QWFiYxo//WLNmvaF69WLUrVsHPfxwf+XlWZUpE67+/R9V7do1inzMdu3i1LPnCMXFJahKlYoaObKXJGnixJnKzNyoxMTJSkycLEkaN26goqIiS/IphhTve/Arc7RZh40xVSX1lhQv6eCrfJOkjyW9ZK3d4bAOWy7m3hOt86S3LzNZ5FS0fZnJkkRWDtim3JCTO7JyQ05u2J+7Y5tyQ07u8l9/q7wuoxRowDbloGB/Xjp+Qj9OO/Z/ErKJX6qedqvvszxqJ0LBIMFTBZcAxpiHJP2rhOoCAAAAAMBzxpyyJzUs1ImkMaDYqgAAAAAAAL531E4EY8wPwa6SFF385QAAAAAA4Ce+P8IgpIqaWDFaUkvln9LxcEbS1yVSEQAAAAAA8KWiBhE+kVTRWvv9/15hjPmiJAoCAAAAAMAvSsupF0OlqIkVOx3luvuKvxwAAAAAAOBXRXUiAAAAAABwCqMT4XCcqwIAAAAAADihEwEAAAAAgCCM4bf3w5EGAAAAAABwQicCAAAAAABBMSfC4ehEAAAAAAAATuhEAAAAAAAgCEMnQgA6EQAAAAAAgBM6EQAAAAAACIJOhEB0IgAAAAAAACcMIgAAAAAAACfGWlvS6yjxFQAAAAAAPHNS9/vvOfBFyL7TVixzne+zDMmcCOVi7g3Fakq1fZnJ5ORgX2ayJLYpF2xTbvZlJitt00yvyygVmkW3lrTK6zJKgQa89hywP3fH/twNObkjKzf5+yne94rWwOsCEGJMrAgAAAAAQBDG+L45IKSYEwEAAAAAADihEwEAAAAAgKDoRDgcnQgAAAAAAMAJnQgAAAAAAARh6EQIQCcCAAAAAABwQicCAAAAAABB8dv74UgDAAAAAAA4oRMBAAAAAIAgmBMhEJ0IAAAAAADACZ0IAAAAAAAEYQydCIejEwEAAAAAADihEwEAAAAAgKDoRDgcnQgAAAAAAMAJgwgAAAAAAMAJhzMAAAAAABCE4bf3AKQBAAAAAACcnPSdCKOHPaJWN1ymLdt26Yq4XpKk/j3u0q03XaG8vDxt2bZLCT1Ga+OmHR5X6r3CsurXva0evreFtmzbJUl6dugUpX7+vYdVeq+wnCYkdlX982pJkiIrV9DOXXvVpFUfL8v0XGE5NYw9W68N7qTTTiujnNw8des3TkuWZnhcqfdmJ8/XwrnfSpLycvO04bdNevXjgcrel62xg9/T79v3yBjp2tuaKu6u5h5XW/wyMtaqb99XtWJFhrp376BOne484cfMzj6gXr1GaMWKDEVGVtLIkb1Up0600tK+08svj9eBAzkqUyZCPXs+pKZNLymGZ+Evhb3+Bve9T7fceLmyD+Tqv79tUsI/R+v3XX94XKm3+Izghs8H7grLSpL+8WBLPdIxTrl5VnPmf6d+g9/zsErv8RmB977Sh4kVD3fSdyJMmPal4ju+FLBs5JhP1LjlU2rSqo9mz/tWfZ448RftyaCwrCTptbGz1KRVHzVp1YcPCCo8pw6dRx3K6KPZi5Uy5xuPqvOPwnIa1Pc+DXrlAzVp1UfPvzxNg/re51F1/tLq3hYaMO6fGjDun2qb0Fp/uaSuKlauoLDwcN3zWLwGTXhK/UY/ofnT07T+1yyvyy12kZGV1K9fgjp1anPM9123bpM6dDhywG7atE9VuXJFzZ2bpAcfjNfw4e9IkqpWraw333xGM2a8rpde6q5evUacaPm+VNjrb96CZWoU10uNWz6l1f/dqJ6d4z2qzj/4jOCGzwfuCsuqedNY3XpTIzW+ubca3dhTr4z5xKPq/IPPCLz3oXQ76QcR0hb/rO079wQs271n36G/y5c/XdbaUJflS4VlhSMVlVPbW5toasrXIazInwrLyVqrypXKSZKqVCp/yv+6V5hF877VVTdeJkmKrF5ZZ/+ljiSpXPnTVevsGtq55XcvyysRUVGRatiwgSIijmyOS0n5XO3aPan4+K7q3/915ebmOj3m/PmL1KbNDZKkli2bKT19qay1io2tq+joKElS/fox2r8/W9nZB4rvyfhEYa+/eQuWKTc3T5K0+NvVql2zmhel+QqfEdzw+cBdYVkldIjT8Dc+VnZ2jiQd6t44lfEZgfe+0sYYE7JLaXDSDyIE81zPu7V64ev62x3N9PzL07wux9cefaClFqcO0ehhjyiySgWvy/G1Zo3P16atvyvjJPy1uDj0HPCuBvdtr9ULX9eLT7dX/yGTvS7JV/b/ma3li35Wo2sbHnHd1o3blbl6vc6LPduDyryRkbFWs2cvUHLyUKWkjFJYWJhmzPjS6b6bNm1TrVrVJUkREeGqVKmCduwI/OCemvq1YmPrqmzZMsVeu991vOc6pX6x1OsyfIvPCG74fOCm3rk11azx+fp3yvP6dGp/NWp4ntcl+RKfEfLx3ofSoEQGEYwxCcaYJcaYJUlJSSWxihP23LCpqt+kiyZ/lKZHH2zpdTm+9daEzxR7zRO66ubeytq8Qy89fb/XJfna3fFXaxpdCEEldIhTr4ETVL9JF/UaOEFvDkvwuiRfWZq2QvUuPlcVKwd+GP/zj/1KfOYd3fv4HSpX4XSPqgu99PSlWr4849CvMenpP2jt2vwBus6dByk+vqsSEgZo+fI1io/vqvj4rvrgg8+cHnv16t80fPg7Gjiwc0k+BV/q1eUO5ebkafL0r7wuxbf4jFA0Ph+4i4gIV7UqFdU8/hn1HTRJE994wuuSfInPCPl47/MrE8KL/x33xIrGmNnW2laFXWetTZJ0cPTAPvHC58e7mhI3ZfpXmj7+Kb0w4n2vS/GlzVv/r3V6XPJ8ffivXke59aktPDxM8Tc3VrPWfb0uxbfat22uHs+OlyR98MlCvTHk7x5X5J15H36lf3+yUJLUbejfVbV6FS2a/52uuuGygNvl5OQq8Zl31CTu8kI7FEqrSZNmaurUVElSUtKzh9osD2etVZs2LdSjxwNHXJeY2E9S/nGhffq8ogkTXgy4Pjo6Shs3blXNmtWVk5Or3bv3qmrVypKkrKyt6tJlsIYM6a6YmFrF/dR87f52zXXLDZep1b2DvC6lVOAzQnB8PnC3fuN2fTRnsSRpydIM5Vmr6tUqaev23R5X5i+nwmcE3vtwsjhqJ4Ix5vIgl0aSLg1NicWv7jk1D/19601XaFXGBg+r8beaNSIP/R3f8kr9uHKtd8X4XIu/XqxVGRu0Pmu716X41sZNO3RNkwskSdc1u1BrTuHDPm6486+HJlOsWr2K/tizT6u+z9Blf73o0G2stfrXkCmqdXYNtbznOu+KLQHt27dWSsoopaSMKvRDlCQ1bXqJUlPTtG3bTkn/v717DbarrO84/v3lAnJxgCKmkEDFcqnRARSKpFSsYEq8lFSBDnRqmUJN7YSbUjrBFwjti15k0A5NHRGomQFjKco0BYaAFLTjtFyEtCQgkKqEpEgyIxQplFv+fbEX7YHuc84qCVlr53w/M3vO3mvvs/cv/6yz9rOf/TzPgqee+ikbNmxs9fzHHvterr/+NgBWrvwuRx11CEl4+ulnWLToYs477zQOP3zuVvm3jIr57z+Uz/z+r3HSGZfw3H+90HWc3rKN0I7tg/b+/pZ7eP+8wfHmgP1/lh1mzrADYYip0EbwvW90hWnb7DIKJhuJcDfwbYaPq9h9q6d5Ayy77CzeN+8dvGWPN7P2zr/kjy+9jgUfOIwDf34fNm8u1m3YxNkXXNl1zF4YVqtj5s3lkLk/RxU8un4TZ11wRdcxOzesTsv+5g5OPmEe165wKsMrhtVp8ZKv8PmLfpsZ06fz/PMvcuYS96dX3PuP9/POXzyYHXfa8X+2PXL/D/mnlfcw5+1787nTLwHgxE9+mEPmbV8NgE2bnuTEEz/NM888y7Rp01i2bAU33fRXHHDAfpx77ic4/fQL2by5mDlzOhde+Clmz37rpM950knzOf/8S5k/fxG77bYrX/jC4FvSq6++kXXrHmfp0q+zdOlgvu1VV/0Re+65+xv5T9zmhv39nb94ITvuMJMbrhmMlrrrvrWc/dmp/f5nG6Ed2wftDW8j3M6XP/8p7rn1z3nhhZf43c98qeuYnbON4HufRlsmWnU4yWrgY1X1yJD7HquqfVu8Ru2036lbEHFqeG7dcqzT5J5btxzAWrXgPtXOc+uW890nbuw6xkg4etZHgIe7jjECDvJvrwWP5+15PG/HOrVnrdoZHKd835vcQTAqk/lfpxc3r9pmp+qZOe2w3tdysvESF03wmLO2bhRJkiRJktRnE05nqKqJVhLaYytnkSRJkiSpV7J9D7T4f9uSlRsu3mopJEmSJElS7004EiHJv453FzBr68eRJEmSJKk/EkcijDXZ2RlmAccDT75mewCXoZckSZIkaQqZrBPhBmDXqlr12juS3PFGBJIkSZIkSf002cKKZ0xw329u/TiSJEmSJPXJliwluP2xGpIkSZIkqZXJpjNIkiRJkjRleYrHV3MkgiRJkiRJasWRCJIkSZIkjcuRCGM5EkGSJEmSJLXiSARJkiRJksaROBJhLEciSJIkSZKkVhyJIEmSJEnSuPzufSyrIUmSJEnSiEtycpI1STYnOeI1912QZG2Sh5IcP2b7gmbb2iRL2ryOIxEkSZIkSRpHRufsDKuBjwNfHrsxyVzgFOCdwD7At5Ic1Ny9FJgPrAfuTrKiqh6Y6EVSVVs7+Gu94S8gSZIkSerMyHzKfn0e3oafaQ/a4lomuQP4g6q6p7l9AUBV/UlzeyVwUfPwi6rq+GGPG8+2GInQux0qyaKqurzrHKPAWrVjndqzVu1Yp3asU3vWqh3r1J61asc6tWOd2rNWXdjyD/ZtJVkELBqz6fKt8P89G/jnMbfXN9sAHnvN9vdO9mRTdU2ERZM/RA1r1Y51as9atWOd2rFO7VmrdqxTe9aqHevUjnVqz1ptx6rq8qo6YszlVR0ISb6VZPWQy8JtldE1ESRJkiRJGgFV9cHX8WsbgH3H3J7TbGOC7eOaqiMRJEmSJEmaClYApyTZMcn+wIHAXcDdwIFJ9k+yA4PFF1dM9mRTdSSCc4jas1btWKf2rFU71qkd69SetWrHOrVnrdqxTu1Yp/aslYZK8jHgMmAv4MYkq6rq+Kpak+Ra4AHgJWBxVb3c/M6ZwEpgOnBVVa2Z9HW2wdkZJEmSJEnSdsDpDJIkSZIkqRU7ESRJkiRJUitTrhMhyYIkDyVZm2RJ13n6KslVSTYmWd11lj5Lsm+S25M8kGRNknO6ztRHSd6U5K4k/9LU6eKuM/VZkulJ7ktyQ9dZ+izJj5Lcn2RVknu6ztNXSXZPcl2S7yd5MMm8rjP1UZKDm33plcvTSc7tOlcfJfl0cyxfnWR5kjd1namPkpzT1GiN+9KrDWtnJvmZJLcmeaT5uUeXGftinFqd3OxXm5Mc0WU+TU1TqhMhyXRgKfAhYC5wapK53abqra8CC7oOMQJeAs6rqrnAUcBi96mhngeOrapDgcOABUmO6jZSr50DPNh1iBHxgao6rKpsRI3vL4Cbq+oXgENx3xqqqh5q9qXDgMOBZ4Hru03VP0lmA2cDR1TVuxgsxHVKt6n6J8m7gE8CRzL4u/tokgO6TdUrX+X/tjOXALdV1YHAbc1tDa/VauDjwHe2eRqJKdaJwOBAvraqflBVLwBfBxZ2nKmXquo7wE+6ztF3VfV4Vd3bXP8pg8b57G5T9U8NPNPcnNlcXNV1iCRzgI8AV3SdRaMvyW7AMcCVAFX1QlU91Wmo0XAc8G9V9WjXQXpqBrBTkhnAzsC/d5ynj94B3FlVz1bVS8C3GXzoE+O2MxcCy5rry4Bf35aZ+mpYrarqwap6qKNI0pTrRJgNPDbm9nr8wKetJMnbgHcDd3YcpZeaIfqrgI3ArVVlnYb7IvCHwOaOc4yCAm5J8r0ki7oO01P7A5uAv26myFyRZJeuQ42AU4DlXYfoo6raAFwCrAMeB/6jqm7pNlUvrQbel2TPJDsDHwb27ThT382qqseb6z8GZnUZRtL4plongvSGSLIr8A3g3Kp6uus8fVRVLzfDhOcARzZDPTVGko8CG6vqe11nGRG/XFXvYTBFbXGSY7oO1EMzgPcAX6qqdwP/iUOEJ5RkB+AE4G+7ztJHzTz1hQw6qPYBdknyW92m6p+qehD4M+AW4GZgFfByl5lGSQ3OQe+IRamnplonwgZe3Qs8p9kmvW5JZjLoQLimqr7ZdZ6+a4ZS345rbgxzNHBCkh8xmG51bJKru43UX803olTVRgZz14/sNlEvrQfWjxn5cx2DTgWN70PAvVX1RNdBeuqDwA+ralNVvQh8E/iljjP1UlVdWVWHV9UxwJPAw11n6rknkuwN0Pzc2HEeSeOYap0IdwMHJtm/+abhFGBFx5k0wpKEwVzjB6vq0q7z9FWSvZLs3lzfCZgPfL/TUD1UVRdU1ZyqehuD49M/VJXf8A2RZJckb37lOvCrDIYPa4yq+jHwWJKDm03HAQ90GGkUnIpTGSayDjgqyc7Ne+BxuFjnUEne2vzcj8F6CF/rNlHvrQBOa66fBvxdh1kkTWBG1wG2pap6KcmZwEoGqwlfVVVrOo7VS0mWA78CvCXJeuBzVXVlt6l66WjgE8D9zXx/gM9W1U3dReqlvYFlzRlSpgHXVpWnL9SWmAVcP/gMwwzga1V1c7eReuss4Jqm8/wHwO90nKe3mg6p+cDvdZ2lr6rqziTXAfcyOEPRfcDl3abqrW8k2RN4EVjsoqb/a1g7E/hT4NokZwCPAr/RXcL+GKdWPwEuA/YCbkyyqqqO7y6lppoMphxJkiRJkiRNbKpNZ5AkSZIkSa+TnQiSJEmSJKkVOxEkSZIkSVIrdiJIkiRJkqRW7ESQJEmSJEmt2IkgSZIkSZJasRNBkiRJkiS18t+EY62btzSo0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "intensity = np.array(list(optimal_V.values())).reshape(12,12)\n",
    "f, ax = plt.subplots(figsize=(20, 10))\n",
    "sns.heatmap(intensity, vmin=-100, vmax=10, annot=True, linewidths=0.1, cmap=\"YlGnBu\")\n",
    "ax.set_title('Value Iteration results:')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Policy evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def policy_eval(policy):\n",
    "    V={}\n",
    "    for (i,j) in all_states:\n",
    "        if (i,j) in actions.keys():\n",
    "            V[(i,j)] = step\n",
    "        if(i == 0): # blue agent in a1\n",
    "            V[(i,j)] = richToDestination\n",
    "        if(i == j): # crushed\n",
    "            V[(i,j)] = crushing\n",
    "    while True:\n",
    "        biggest_change = 0\n",
    "        for s in all_states:\n",
    "            if s in Policy:  \n",
    "                old_v = V[s]\n",
    "                nxt = nextStateDistibution(s, policy[s])\n",
    "                v=0\n",
    "                #Calculate the value\n",
    "                for next_state, prob in nxt.items():\n",
    "                    v += prob * (rewards[s] + GAMMA * V[next_state])\n",
    "        \n",
    "                #Save the best of all actions for the state                                \n",
    "                V[s] = v\n",
    "                biggest_change = max(biggest_change, np.abs(old_v - V[s]))\n",
    "        #See if the loop should stop now         \n",
    "        if biggest_change < SMALL_ENOUGH:\n",
    "            return V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "valueIteration_policy = Policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.63945021546267"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "policy_eval(valueIteration_policy)[(5,0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# evaluate other behaviors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def randomBehavior():\n",
    "    Policy={}\n",
    "    for s in actions.keys():\n",
    "        Policy[s] = np.random.choice(actions[s])\n",
    "    return Policy\n",
    "\n",
    "random_policy = randomBehavior()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomBehavior_intensity = np.array(list(policy_eval(random_policy).values())).reshape(12,12)\n",
    "# f, ax = plt.subplots(figsize=(20, 10))\n",
    "# sns.heatmap(randomBehavior_intensity, vmin=-100, vmax=10, annot=True, linewidths=0.1, cmap=\"YlGnBu\")\n",
    "# ax.set_title('Value function for: randomBehavior')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def carefulBehavior():\n",
    "    Policy={}\n",
    "    for s in actions.keys():\n",
    "        blueState = s[0]\n",
    "        redState = s[1]\n",
    "        if redState + 1 == blueState or redState == blueState + 5 or redState + 6 == blueState or redState + 7 == blueState:\n",
    "            if blueState < 6:\n",
    "                Policy[s] = \"down\"\n",
    "            else:\n",
    "                Policy[s] = \"stay\"\n",
    "        elif redState + 2 == blueState:\n",
    "            if blueState < 6:\n",
    "                Policy[s] = \"down\"\n",
    "            else:\n",
    "                Policy[s] = \"up\"\n",
    "        else:\n",
    "            if blueState < 6:\n",
    "                Policy[s] = \"left\"\n",
    "            else:\n",
    "                Policy[s] = \"up\"\n",
    "    return Policy\n",
    "\n",
    "careful_policy = carefulBehavior()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "careful_intensity = np.array(list(policy_eval(careful_policy).values())).reshape(12,12)\n",
    "# f, ax = plt.subplots(figsize=(20, 10))\n",
    "# sns.heatmap(careful_intensity, vmin=-100, vmax=10, annot=True, linewidths=0.1, cmap=\"YlGnBu\")\n",
    "# ax.set_title('Value function for: carefulBehavior')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def semiCarefulBehavior():\n",
    "    Policy={}\n",
    "    for s in actions.keys():\n",
    "        blueState = s[0]\n",
    "        redState = s[1]\n",
    "        if redState + 1 == blueState:\n",
    "            if blueState < 6:\n",
    "                Policy[s] = \"down\"\n",
    "            else:\n",
    "                Policy[s] = \"up\"\n",
    "        elif redState + 2 == blueState:\n",
    "            if blueState < 6:\n",
    "                Policy[s] = \"stay\"\n",
    "            else:\n",
    "                Policy[s] = \"up\"\n",
    "        elif redState == blueState + 5 or redState + 6 == blueState or redState + 7 == blueState:\n",
    "            if redState == 11 and blueState == 6:\n",
    "                Policy[s] = \"up\"\n",
    "            else:\n",
    "                Policy[s] = \"stay\"\n",
    "        else:\n",
    "            if blueState < 6:\n",
    "                Policy[s] = \"left\"\n",
    "            else:\n",
    "                Policy[s] = \"up\"\n",
    "    return Policy\n",
    "\n",
    "semiCareful_policy = semiCarefulBehavior()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "semiCareful_intensity = np.array(list(policy_eval(semiCareful_policy).values())).reshape(12,12)\n",
    "# f, ax = plt.subplots(figsize=(20, 10))\n",
    "# sns.heatmap(semiCareful_intensity, vmin=-100, vmax=10, annot=True, linewidths=0.1, cmap=\"YlGnBu\")\n",
    "# ax.set_title('Value function for: carefulBehavior')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def semiAggressiveBehavior():\n",
    "    Policy={}\n",
    "    for s in actions.keys():\n",
    "        blueState = s[0]\n",
    "        redState = s[1]\n",
    "        if redState + 1 == blueState:\n",
    "            if blueState < 6:\n",
    "                Policy[s] = \"stay\"\n",
    "            else:\n",
    "                Policy[s] = \"up\"\n",
    "        elif redState == blueState + 5 or redState + 6 == blueState or redState + 7 == blueState:\n",
    "            if blueState < 6:\n",
    "                Policy[s] = \"left\"\n",
    "            else:\n",
    "                Policy[s] = \"stay\"\n",
    "        else:\n",
    "            if blueState < 6:\n",
    "                Policy[s] = \"left\"\n",
    "            else:\n",
    "                Policy[s] = \"up\"\n",
    "    return Policy\n",
    "\n",
    "semiAggressive_policy = semiAggressiveBehavior()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "semiAggressive_intensity = np.array(list(policy_eval(semiAggressive_policy).values())).reshape(12,12)\n",
    "# f, ax = plt.subplots(figsize=(20, 10))\n",
    "# sns.heatmap(semiAggressive_intensity, vmin=-100, vmax=10, annot=True, linewidths=0.1, cmap=\"YlGnBu\")\n",
    "# ax.set_title('Value function for: semiAggressiveBehavior')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aggressiveBehavior():\n",
    "    Policy={}\n",
    "    for s in actions.keys():\n",
    "        blueState = s[0]\n",
    "        redState = s[1]\n",
    "        if blueState > 5: #blue is in bX\n",
    "            Policy[s] = \"up\"\n",
    "        else:\n",
    "            Policy[s] = \"left\"\n",
    "    return Policy\n",
    "\n",
    "aggressive_policy = aggressiveBehavior()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggressive_intensity = np.array(list(policy_eval(aggressive_policy).values())).reshape(12,12)\n",
    "# f, ax = plt.subplots(figsize=(20, 10))\n",
    "# sns.heatmap(aggressive_intensity, vmin=-100, vmax=10, annot=True, linewidths=0.1, cmap=\"YlGnBu\")\n",
    "# ax.set_title('Value function for: aggressiveBehavior')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# diff = pd.DataFrame({'Real': df.groupby(['behavior']).mean()['blueScore'],\n",
    "#                       'Prediction': [aggressive_intensity[5][0], careful_intensity[5][0], randomBehavior_intensity[5][0] , semiAggressive_intensity[5][0]]},\n",
    "#                      index= df.groupby(['behavior']).mean()['blueScore'].index)\n",
    "# diff['different'] = abs(diff['Real']-diff['Prediction'])\n",
    "# diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Value iteration with taking into account the previous state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OutOfBoard(Exception):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pos_after_action(s, a):\n",
    "    if a == 'U' or a == \"up\":\n",
    "        if(s[0] == 'a'):\n",
    "            raise OutOfBoard(\"An error occurred, state: \"+ s + \" action: \" + a)\n",
    "        return \"a\" + s[1]\n",
    "    if a == 'D' or a == \"down\":\n",
    "        if(s[0] == 'b'):\n",
    "            raise OutOfBoard(\"An error occurred, state: \"+ s + \" action: \" + a)\n",
    "        return \"b\" + s[1]\n",
    "    if a == 'L' or a == \"left\":\n",
    "        if(s[0] == 'b' or s[1] == '1'):\n",
    "            raise OutOfBoard(\"An error occurred, state: \"+ s + \" action: \" + a)\n",
    "        return \"a\" + str(int(s[1])-1)\n",
    "    if a == 'R' or a == \"right\":\n",
    "        if(s[0] == 'b' or s[1] == '6'):\n",
    "            raise OutOfBoard(\"An error occurred, state: \"+ s + \" action: \" + a)\n",
    "        return \"a\" + str(int(s[1])+1)\n",
    "    if a == 'S' or a == \"stay\":\n",
    "        return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_next_state(c_s):\n",
    "    n_s = c_s.split()\n",
    "    blueAction = n_s[3]\n",
    "    blueState = n_s[2]\n",
    "    redAction = n_s[1]\n",
    "    redState = n_s[0]\n",
    "    \n",
    "    newPosBlue = get_pos_after_action(blueState, blueAction)\n",
    "    newPosRed = get_pos_after_action(redState, redAction)\n",
    "    \n",
    "    return newPosBlue + ' ' + newPosRed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>action</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a1 a2 nxt: a1 a2</th>\n",
       "      <td>right</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a2 nxt: a1 a3</th>\n",
       "      <td>down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a2 nxt: a1 a3</th>\n",
       "      <td>right</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a2 nxt: a1 a3</th>\n",
       "      <td>stay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a2 nxt: a1 b2</th>\n",
       "      <td>stay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b5 nxt: b6 a5</th>\n",
       "      <td>down</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b5 nxt: b6 a5</th>\n",
       "      <td>right</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b5 nxt: b6 a5</th>\n",
       "      <td>stay</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b5 nxt: b6 b5</th>\n",
       "      <td>stay</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b5 nxt: b6 b5</th>\n",
       "      <td>up</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>487 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 action  value\n",
       "state                         \n",
       "a1 a2 nxt: a1 a2  right      1\n",
       "a1 a2 nxt: a1 a3   down      1\n",
       "a1 a2 nxt: a1 a3  right     30\n",
       "a1 a2 nxt: a1 a3   stay      1\n",
       "a1 a2 nxt: a1 b2   stay      1\n",
       "...                 ...    ...\n",
       "b6 b5 nxt: b6 a5   down      4\n",
       "b6 b5 nxt: b6 a5  right     27\n",
       "b6 b5 nxt: b6 a5   stay      3\n",
       "b6 b5 nxt: b6 b5   stay      2\n",
       "b6 b5 nxt: b6 b5     up      4\n",
       "\n",
       "[487 rows x 2 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# s_r = df['log'].apply(pd.Series).iloc[:,1:]\n",
    "# s_r.columns = s_r.columns.astype(str)\n",
    "# log_red = flat_table.normalize(s_r).drop('index', axis=1)\n",
    "# log_red = log_red.dropna(how='all')\n",
    "\n",
    "# log_len = int(len(log_red.columns)/2)+1\n",
    "# for i in range(1,log_len):\n",
    "#     log_red[str(i)] = log_red[str(i)+'.red'] + ' ' + log_red[str(i)+'.blue']\n",
    "#     log_red = log_red.drop([str(i)+'.red', str(i)+'.blue'],axis=1)\n",
    "#     for substr in ['from: ', ', action:']:\n",
    "#         log_red[str(i)] = log_red[str(i)].apply(lambda x: x.replace(substr, '', 2) if type(x) != float else x)\n",
    "    \n",
    "# log_len = len(log_red.columns)\n",
    "# for i in range(1,log_len):\n",
    "#     log_red[str(i) + '&' + str(i+1)] = log_red[str(i)] + '&' + log_red[str(i+1)]\n",
    "#     log_red[str(i) + '&' + str(i+1)] = log_red[str(i) + '&' + str(i+1)].apply(lambda x: x.rsplit(' ', 2)[0] if type(x) != float else x)\n",
    "# log_red = log_red.drop([str(i) for i in range(1,log_len+1)],axis=1)\n",
    "\n",
    "# log_red = pd.DataFrame(log_red.to_numpy().flatten(), columns=['state'])\n",
    "# log_red = log_red.dropna()\n",
    "\n",
    "# log_red['action'] = log_red['state'].apply(lambda x: x.rsplit('&', 1)[1].rsplit(' ', 1)[1])\n",
    "# log_red['state'] = log_red['state'].apply(lambda x: x.rsplit('&', 1)[0])\n",
    "\n",
    "# log_red['nxt'] = log_red['state'].apply(get_next_state)\n",
    "# log_red['state'] = log_red['state'].str.split().apply(reversed).apply(' '.join)\n",
    "\n",
    "# log_red['value']=1\n",
    "\n",
    "# for a in ['right', 'stay', 'up', 'down', 'left']:\n",
    "#     log_red['state'] = log_red['state'].apply(lambda x: x.replace(a+' ', '', 2))\n",
    "\n",
    "# log_red['state'] = log_red['state'] + ' nxt: ' + log_red['nxt']\n",
    "# log_red = log_red.drop('nxt',axis=1)\n",
    "\n",
    "# log_red = log_red.groupby(['state', 'action']).count()\n",
    "# log_red.reset_index(inplace=True)\n",
    "# log_red = log_red.set_index('state')\n",
    "\n",
    "# log_red\n",
    "\n",
    "s_r = df['log'].apply(pd.Series).iloc[:,1:]\n",
    "s_r.columns = s_r.columns.astype(str)\n",
    "s_r.set_index( df['index'], inplace=True)\n",
    "log_red = flat_table.normalize(s_r)\n",
    "log_red = log_red.dropna(how='all')\n",
    "log_red.set_index(log_red['index'], inplace=True)\n",
    "log_red = log_red.drop('index', axis=1)\n",
    "\n",
    "log_len = int(len(log_red.columns)/2)+1\n",
    "for i in range(1,log_len):\n",
    "    log_red[str(i)] = log_red[str(i)+'.red'] + ' ' + log_red[str(i)+'.blue']\n",
    "    log_red = log_red.drop([str(i)+'.red', str(i)+'.blue'],axis=1)\n",
    "    for substr in ['from: ', ', action:']:\n",
    "        log_red[str(i)] = log_red[str(i)].apply(lambda x: x.replace(substr, '', 2) if type(x) != float else x)\n",
    "\n",
    "log_red_copy = log_red.copy(deep=True)\n",
    "\n",
    "log_len = len(log_red.columns)\n",
    "for i in range(1,log_len):\n",
    "    log_red[str(i) + '&' + str(i+1)] = log_red[str(i)] + '&' + log_red[str(i+1)]\n",
    "    log_red[str(i) + '&' + str(i+1)] = log_red[str(i) + '&' + str(i+1)].apply(lambda x: x.rsplit(' ', 2)[0] if type(x) != float else x)\n",
    "log_red = log_red.drop([str(i) for i in range(1,log_len+1)],axis=1)\n",
    "\n",
    "log_red = pd.DataFrame(log_red.to_numpy().flatten(), columns=['state'])\n",
    "log_red = log_red.dropna()\n",
    "\n",
    "log_red['action'] = log_red['state'].apply(lambda x: x.rsplit('&', 1)[1].rsplit(' ', 1)[1])\n",
    "log_red['state'] = log_red['state'].apply(lambda x: x.rsplit('&', 1)[0])\n",
    "\n",
    "log_red['nxt'] = log_red['state'].apply(get_next_state)\n",
    "log_red['state'] = log_red['state'].str.split().apply(reversed).apply(' '.join)\n",
    "\n",
    "log_red['value']=1\n",
    "\n",
    "for a in ['right', 'stay', 'up', 'down', 'left']:\n",
    "    log_red['state'] = log_red['state'].apply(lambda x: x.replace(a+' ', '', 2))\n",
    "\n",
    "log_red['state'] = log_red['state'] + ' nxt: ' + log_red['nxt']\n",
    "log_red = log_red.drop('nxt',axis=1)\n",
    "\n",
    "log_red = log_red.groupby(['state', 'action']).count()\n",
    "log_red.reset_index(inplace=True)\n",
    "log_red = log_red.set_index('state')\n",
    "\n",
    "log_red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>action</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a4 b3 nxt: a4 b3</th>\n",
       "      <td>stay</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a4 b3 nxt: a4 b3</th>\n",
       "      <td>up</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 action  value\n",
       "state                         \n",
       "a4 b3 nxt: a4 b3   stay     23\n",
       "a4 b3 nxt: a4 b3     up     11"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_red[log_red.index == 'a4 b3 nxt: a4 b3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df[['index','win']]\n",
    "a.set_index('index', inplace=True)\n",
    "\n",
    "b = df[['index','behavior']]\n",
    "b.set_index('index', inplace=True)\n",
    "\n",
    "c = df[['index','blueScore']]\n",
    "c.set_index('index', inplace=True)\n",
    "\n",
    "d = df[['index','redScore']]\n",
    "d.set_index('index', inplace=True)\n",
    "\n",
    "e = df[['index','answer-the-quiz']]\n",
    "e.set_index('index', inplace=True)\n",
    "\n",
    "log_red_copy = pd.concat([log_red_copy,a, b, c ,d, e],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log_red_copy[log_red_copy['win'] == False][log_red_copy['blueScore'] != log_red_copy['redScore']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# c = log_red_copy[log_red_copy['win'] == True][log_red_copy['answer-the-quiz'].notnull()][log_red_copy['behavior'] == 'valueIterationBasedBehavior_v2']\n",
    "# c = log_red_copy[log_red_copy['answer-the-quiz'].notnull()][log_red_copy['behavior'] == 'semiCarefulBehavior']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def numberOfStepsRemaining(col):\n",
    "    split_col = col.split(' ')\n",
    "    assert split_col[0] == 'a5' and split_col[1] == 'right', \"col:\" + col\n",
    "    bluePos = split_col[2]\n",
    "    blueAction = split_col[3]\n",
    "    newBluePos = getActionGivenState(bluePos, blueAction)\n",
    "    if newBluePos == 'a1':\n",
    "        return 0\n",
    "    if newBluePos[0] == 'b':\n",
    "        return int(newBluePos[1])\n",
    "    else:\n",
    "        return int(newBluePos[1])-1\n",
    "\n",
    "def getActionGivenState(s,a):\n",
    "    if a == 'stay':\n",
    "        return s\n",
    "    elif a == 'down':\n",
    "        assert s[0] == 'a'\n",
    "        return 'b' + s[1]\n",
    "    elif a == 'up':\n",
    "        assert s[0] == 'b'\n",
    "        return 'a' + s[1]\n",
    "    elif a == 'left':\n",
    "        assert s[0] == 'a'\n",
    "        return 'a' + str(int(s[1]) - 1)\n",
    "    elif a == 'right':\n",
    "        assert s[0] == 'a'\n",
    "        return 'a' + str(int(s[1]) + 1)\n",
    "        \n",
    "def isColliding(col):\n",
    "    split_col = col.split(' ')\n",
    "    bluePos = split_col[2]\n",
    "    blueAction = split_col[3]\n",
    "    redPos = split_col[0]\n",
    "    redAction = split_col[1]\n",
    "    \n",
    "    newBluePos = getActionGivenState(bluePos, blueAction)\n",
    "    newRedPos = getActionGivenState(redPos, redAction)\n",
    "    if newBluePos == newRedPos:\n",
    "        return True\n",
    "    if bluePos[0] == 'a' and redPos[0] == 'a' and int(bluePos[1]) -1 == int(redPos[1]) and blueAction == 'left' and redAction == 'right':\n",
    "            return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "def calcBlueRedScore(df):\n",
    "    blueRedScoreById = pd.DataFrame(index=df.index, columns=['blueScore', 'redScore'])\n",
    "\n",
    "    blueScores = []\n",
    "    redScores = []\n",
    "    df_len = len(df.columns)\n",
    "    for i, row in df.iterrows():\n",
    "        blueScore = -1\n",
    "        redScore = -1\n",
    "        if type(row[str(1)]) == float:\n",
    "            redScore = np.NaN\n",
    "            blueScore = np.NaN\n",
    "            blueScores.append(blueScore)\n",
    "            redScores.append(redScore)\n",
    "            continue\n",
    "        else:\n",
    "            NotNullLastCol = row[str(1)]\n",
    "        for i in range(2,df_len -4):\n",
    "            if type(row[str(i)]) != float: # game not ended\n",
    "                redScore = redScore-1\n",
    "                if row[str(i)].split(' ')[2] != 'a1': #blueBall not get to 'a1'\n",
    "                    blueScore = blueScore-1\n",
    "                NotNullLastCol = row[str(i)]\n",
    "            else:\n",
    "#                 print('\\nrow: ' + str(row) + ' col: ' + str(NotNullLastCol))\n",
    "                if isColliding(NotNullLastCol):\n",
    "                    redScore += -100\n",
    "                    blueScore = redScore\n",
    "                else:\n",
    "                    redScore += 30\n",
    "                    try:\n",
    "                        stepsRemaining = numberOfStepsRemaining(NotNullLastCol)\n",
    "                        blueScore += 30 + (-1)*stepsRemaining\n",
    "                    except AssertionError:\n",
    "                        print(\"Game not done! last col is: \" + str(NotNullLastCol))\n",
    "                        redScore = np.NaN\n",
    "                        blueScore = np.NaN\n",
    "                              \n",
    "                break\n",
    "        blueScores.append(blueScore)\n",
    "        redScores.append(redScore)\n",
    "#         print(redScore)\n",
    "#         print(blueScore)\n",
    "    blueRedScoreById['blueScore'] = blueScores\n",
    "    blueRedScoreById['redScore'] = redScores\n",
    "    return blueRedScoreById"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Game not done! last col is: b1 stay a1 stay\n",
      "Game not done! last col is: b2 stay a2 left\n",
      "Game not done! last col is: b1 stay b2 stay\n",
      "Game not done! last col is: a2 stay a5 left\n",
      "Game not done! last col is: b3 stay a1 stay\n",
      "Game not done! last col is: b1 stay a5 stay\n",
      "Game not done! last col is: b1 stay a3 left\n",
      "Game not done! last col is: a2 right a5 left\n",
      "Game not done! last col is: b3 up a6 left\n",
      "Game not done! last col is: a1 stay a5 stay\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>aggressively_value</th>\n",
       "      <th>computer_value</th>\n",
       "      <th>generously_value</th>\n",
       "      <th>steps</th>\n",
       "      <th>wisely_value</th>\n",
       "      <th>predictable_value</th>\n",
       "      <th>blueScore</th>\n",
       "      <th>redScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavior</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aggressiveBehavior</th>\n",
       "      <td>5.05</td>\n",
       "      <td>5.83</td>\n",
       "      <td>3.29</td>\n",
       "      <td>6.87</td>\n",
       "      <td>4.59</td>\n",
       "      <td>4.97</td>\n",
       "      <td>-15.00</td>\n",
       "      <td>-17.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carefulBehavior</th>\n",
       "      <td>2.96</td>\n",
       "      <td>5.21</td>\n",
       "      <td>5.07</td>\n",
       "      <td>7.47</td>\n",
       "      <td>4.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.00</td>\n",
       "      <td>18.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>randomBehavior</th>\n",
       "      <td>3.52</td>\n",
       "      <td>5.65</td>\n",
       "      <td>4.01</td>\n",
       "      <td>11.52</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.57</td>\n",
       "      <td>-58.18</td>\n",
       "      <td>-56.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semiAggressiveBehavior</th>\n",
       "      <td>4.58</td>\n",
       "      <td>5.73</td>\n",
       "      <td>3.22</td>\n",
       "      <td>6.43</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.52</td>\n",
       "      <td>-60.97</td>\n",
       "      <td>-62.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semiCarefulBehavior</th>\n",
       "      <td>3.94</td>\n",
       "      <td>5.70</td>\n",
       "      <td>4.24</td>\n",
       "      <td>12.72</td>\n",
       "      <td>4.92</td>\n",
       "      <td>4.29</td>\n",
       "      <td>-2.29</td>\n",
       "      <td>-0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valueIterationBasedBehavior_v2</th>\n",
       "      <td>4.82</td>\n",
       "      <td>6.07</td>\n",
       "      <td>3.57</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4.70</td>\n",
       "      <td>4.77</td>\n",
       "      <td>-4.13</td>\n",
       "      <td>-6.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                aggressively_value  computer_value  \\\n",
       "behavior                                                             \n",
       "aggressiveBehavior                            5.05            5.83   \n",
       "carefulBehavior                               2.96            5.21   \n",
       "randomBehavior                                3.52            5.65   \n",
       "semiAggressiveBehavior                        4.58            5.73   \n",
       "semiCarefulBehavior                           3.94            5.70   \n",
       "valueIterationBasedBehavior_v2                4.82            6.07   \n",
       "\n",
       "                                generously_value  steps  wisely_value  \\\n",
       "behavior                                                                \n",
       "aggressiveBehavior                          3.29   6.87          4.59   \n",
       "carefulBehavior                             5.07   7.47          4.04   \n",
       "randomBehavior                              4.01  11.52          3.73   \n",
       "semiAggressiveBehavior                      3.22   6.43          4.34   \n",
       "semiCarefulBehavior                         4.24  12.72          4.92   \n",
       "valueIterationBasedBehavior_v2              3.57   9.00          4.70   \n",
       "\n",
       "                                predictable_value  blueScore  redScore  \n",
       "behavior                                                                \n",
       "aggressiveBehavior                           4.97     -15.00    -17.15  \n",
       "carefulBehavior                               NaN      16.00     18.47  \n",
       "randomBehavior                               3.57     -58.18    -56.40  \n",
       "semiAggressiveBehavior                       4.52     -60.97    -62.11  \n",
       "semiCarefulBehavior                          4.29      -2.29     -0.86  \n",
       "valueIterationBasedBehavior_v2               4.77      -4.13     -6.32  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blueRedScoreById = calcBlueRedScore(log_red_copy)\n",
    "# blueRedScoreById['behavior'] = log_red_copy['behavior']\n",
    "blueRedScoreById.reset_index(inplace=True)\n",
    "\n",
    "joined_df= df.merge(blueRedScoreById, on='index', how='left')\n",
    "joined_df = joined_df.rename(columns={'blueScore_y': 'blueScore', 'redScore_y': 'redScore'})\n",
    "joined_df.set_index(joined_df['index'], inplace=True)\n",
    "joined_df = joined_df.drop(['index','blueScore_x','redScore_x', 'WorkerId'], axis=1)\n",
    "joined_df.groupby('behavior').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_df.to_json('data/test1.json', orient = \"index\", indent=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:63: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
      "<>:104: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
      "<>:63: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
      "<>:104: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
      "<ipython-input-71-0ac9bf46e357>:63: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
      "  assert(1 == 1, 'not suppose to get here: ', row_i)\n",
      "<ipython-input-71-0ac9bf46e357>:104: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n",
      "  assert(1 == 1, \"equilibrum_kind can not be None!\")\n"
     ]
    }
   ],
   "source": [
    "def checkIsEquilibirum(row_i, equilibrum_kind):\n",
    "    red_pos, red_action, blue_pos, blue_action = parseRow(row_i)\n",
    "#     print('red_pos: ', red_pos)\n",
    "#     print('red_action:', red_action)\n",
    "    \n",
    "#     print('blue_pos: ', blue_pos)\n",
    "#     print('blue_action:', blue_action)\n",
    "    \n",
    "    x_red, y_red = get_x_y(red_pos)\n",
    "    x_blue, y_blue = get_x_y(blue_pos)\n",
    "    \n",
    "#     print('x_red: ', x_red)\n",
    "#     print('y_red:', y_red)\n",
    "    \n",
    "#     print('x_blue: ', x_blue)\n",
    "#     print('y_blue:', y_blue)\n",
    "    \n",
    "    d = x_blue - x_red\n",
    "    \n",
    "#     print('d: ', d)\n",
    "    \n",
    "    if y_red == 1:\n",
    "        if d >= 3 or d < 0:\n",
    "            if red_action == 'right':\n",
    "                return equilibrum_kind\n",
    "            else:\n",
    "                return 'none'\n",
    "        if y_blue == 1 and d == 1:\n",
    "            if red_action == 'down':\n",
    "                return 'week'\n",
    "            elif red_action == 'right':\n",
    "                return 'strong'\n",
    "            else:\n",
    "                return 'none'\n",
    "        if y_blue == 1 and d == 2:\n",
    "            if red_action == 'right':\n",
    "                return 'strong'\n",
    "            else:\n",
    "                return 'week'\n",
    "    if y_red == 2:\n",
    "        if d <= 0:\n",
    "            if red_action == 'up':\n",
    "                return equilibrum_kind\n",
    "            else:\n",
    "                return 'none'\n",
    "        if y_blue == 1 and d == 1:\n",
    "            if red_action == 'stay':\n",
    "                return 'week'\n",
    "            else:\n",
    "                return 'strong'\n",
    "        if y_blue == 1 and d >= 4:\n",
    "            if red_action == 'up':\n",
    "                return equilibrum_kind\n",
    "            else:\n",
    "                return 'none'\n",
    "        if y_blue == 2 and d >= 3:\n",
    "            if red_action == 'up':\n",
    "                return equilibrum_kind\n",
    "            else:\n",
    "                return 'none'\n",
    "#         otherwise:\n",
    "\n",
    "        assert(1 == 1, 'not suppose to get here: ', row_i)\n",
    "            \n",
    "def get_x_y(pos):\n",
    "    if pos[0] == 'a':\n",
    "         y = 1\n",
    "    else:\n",
    "        y = 2\n",
    "    x = int(pos[1])\n",
    "    return x, y\n",
    "    \n",
    "def parseRow(row_i):\n",
    "    split_row = row_i.split(' ')\n",
    "    return split_row[0], split_row[1], split_row[2], split_row[3]\n",
    "\n",
    "def countEquilibirum(df):\n",
    "    count_storng = 0\n",
    "    count_week = 0\n",
    "    count_none = 0\n",
    "        \n",
    "    strong_strategy = ['right', 'right', 'right', 'right', 'right']\n",
    "    week_strategy = ['right', 'right', 'down', 'stay', 'up']\n",
    "    \n",
    "    df_len = len(df.columns)\n",
    "    for i, row in df.iterrows():\n",
    "        if type(row[str(1)]) == float:\n",
    "            continue\n",
    "        equilibrum_kind = None\n",
    "        for i in range(1,df_len -4):\n",
    "            if type(row[str(i)]) != float: # game not ended\n",
    "                kind_temp = checkIsEquilibirum(row[str(i)], equilibrum_kind)\n",
    "#                 print('kind_temp: ', kind_temp)\n",
    "#                 print('equilibrum_kind: ', equilibrum_kind)\n",
    "                if equilibrum_kind == None:\n",
    "                    equilibrum_kind = kind_temp \n",
    "                else:\n",
    "                    if equilibrum_kind == 'none' or equilibrum_kind != kind_temp:\n",
    "                        count_none = count_none+1 \n",
    "                        break\n",
    "                        \n",
    "            else:\n",
    "                if equilibrum_kind == None:\n",
    "                    assert(1 == 1, \"equilibrum_kind can not be None!\")\n",
    "#                     count_none = count_none+1\n",
    "                if equilibrum_kind == 'strong':\n",
    "                    count_storng = count_storng +1\n",
    "                elif equilibrum_kind == 'week':\n",
    "                    count_week = count_week+1\n",
    "                break\n",
    "                    \n",
    "    return count_storng, count_week, count_none"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log_red_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aggressive     32\n",
       "Careful        52\n",
       "None          386\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strong, week, none = countEquilibirum(log_red_copy)\n",
    "counts = pd.Series([strong, week, none], index=['Aggressive', 'Careful', 'None'])\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.ylabel('Frequency [%]')\n",
    "\n",
    "ax = (pd.Series([strong, week, none], index=['Aggressive', 'Careful', 'None'])/ 470 * 100).plot(kind=\"bar\", width=0.9, color=['C0', 'C1', 'C2'])\n",
    "ax.set_yticks(np.arange(0, 110, 10))\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.set_yticks(np.arange(0, 110, 10) * 470 /100)\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate('{:.2f}%'.format(p.get_height()), (p.get_x()+0.35, p.get_height()+1))\n",
    "    \n",
    "plt.savefig('count_NE.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>state</th>\n",
       "      <th>right</th>\n",
       "      <th>stay</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a1 a2 nxt: a1 a2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a1 a2 nxt: a1 a3</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a1 a2 nxt: a1 b2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a1 a3 nxt: a1 a3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a1 a3 nxt: a1 a4</td>\n",
       "      <td>106.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>b6 b1 nxt: b6 a1</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>b6 b1 nxt: b6 b1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>b6 b4 nxt: a6 a4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>b6 b5 nxt: b6 a5</td>\n",
       "      <td>27.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>b6 b5 nxt: b6 b5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                state  right  stay   up  down\n",
       "0    a1 a2 nxt: a1 a2   1.00  0.00 0.00  0.00\n",
       "1    a1 a2 nxt: a1 a3  30.00  1.00 0.00  1.00\n",
       "2    a1 a2 nxt: a1 b2   0.00  1.00 1.00  0.00\n",
       "3    a1 a3 nxt: a1 a3   1.00  6.00 0.00  3.00\n",
       "4    a1 a3 nxt: a1 a4 106.00  0.00 0.00  0.00\n",
       "..                ...    ...   ...  ...   ...\n",
       "283  b6 b1 nxt: b6 a1   2.00  0.00 0.00  0.00\n",
       "284  b6 b1 nxt: b6 b1   0.00  2.00 0.00  0.00\n",
       "285  b6 b4 nxt: a6 a4   1.00  0.00 0.00  0.00\n",
       "286  b6 b5 nxt: b6 a5  27.00  3.00 0.00  4.00\n",
       "287  b6 b5 nxt: b6 b5   0.00  2.00 4.00  0.00\n",
       "\n",
       "[288 rows x 5 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame(index=log_red.index.drop_duplicates(), columns=['right', 'stay', 'up', 'down'])\n",
    "# result.reset_index(level=0, inplace=True)\n",
    "for action in ['right', 'stay', 'up', 'down']:\n",
    "    result[action] = log_red[log_red['action'] == action]['value']\n",
    "result = result.fillna(0)\n",
    "result.reset_index(inplace=True)\n",
    "result.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>right</th>\n",
       "      <th>stay</th>\n",
       "      <th>up</th>\n",
       "      <th>down</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a1 a2 nxt: a1 a2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a2 nxt: a1 a3</th>\n",
       "      <td>31.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a2 nxt: a1 b2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a3 nxt: a1 a3</th>\n",
       "      <td>2.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a1 a3 nxt: a1 a4</th>\n",
       "      <td>107.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</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>b6 b1 nxt: b6 a1</th>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b1 nxt: b6 b1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b4 nxt: a6 a4</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b5 nxt: b6 a5</th>\n",
       "      <td>28.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b6 b5 nxt: b6 b5</th>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  right  stay   up  down\n",
       "state                                   \n",
       "a1 a2 nxt: a1 a2   2.00  1.00 0.00  1.00\n",
       "a1 a2 nxt: a1 a3  31.00  2.00 0.00  2.00\n",
       "a1 a2 nxt: a1 b2   0.00  2.00 2.00  0.00\n",
       "a1 a3 nxt: a1 a3   2.00  7.00 0.00  4.00\n",
       "a1 a3 nxt: a1 a4 107.00  1.00 0.00  1.00\n",
       "...                 ...   ...  ...   ...\n",
       "b6 b1 nxt: b6 a1   3.00  1.00 0.00  1.00\n",
       "b6 b1 nxt: b6 b1   0.00  3.00 1.00  0.00\n",
       "b6 b4 nxt: a6 a4   2.00  1.00 0.00  1.00\n",
       "b6 b5 nxt: b6 a5  28.00  4.00 0.00  5.00\n",
       "b6 b5 nxt: b6 b5   0.00  3.00 5.00  0.00\n",
       "\n",
       "[288 rows x 4 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# laplace smoving:\n",
    "for i in range(1,7):\n",
    "    result.loc[result['state'].str.endswith('a'+str(i)), ['stay', 'right', 'down']] += 1\n",
    "    \n",
    "for i in range(1,7):\n",
    "    result.loc[result['state'].str.endswith('b'+str(i)), ['stay', 'up']] += 1\n",
    "\n",
    "result = result.set_index('state')\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert it to Probability table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "pd.options.display.float_format = \"{:,.2f}\".format\n",
    "result_probs = result.div(result.sum(axis=1), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save data on computer for the policy iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_probs.to_json('data/probs2.json' , orient='index', indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def possibleFutureStates(s):\n",
    "    n_l = []\n",
    "    for a in actions[s]:\n",
    "#         print('s: ', adapt_state((3,2)))\n",
    "#         print(a)\n",
    "        n = nextStateDistibution(s, a)\n",
    "        n_l.extend([elm for elm in n])\n",
    "#         print('n: ', n)\n",
    "#         print('n_l: ', n_l)\n",
    "    return n_l\n",
    "\n",
    "possibleStatesForState = {}\n",
    "for s in all_states:\n",
    "    if s in Policy:\n",
    "        poss_states = possibleFutureStates(s)\n",
    "        possibleStatesForState[s] = [s for s in poss_states]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''==================================================\n",
    "Initial set up                              version 2\n",
    "=================================================='''\n",
    "#Define all states\n",
    "all_states=[]\n",
    "for prev_state in possibleStatesForState:\n",
    "    poss_states = possibleStatesForState[prev_state]\n",
    "    for curr_state in poss_states:\n",
    "        # current state: (i,j), previous state: (l,k)\n",
    "        all_states.append((curr_state,prev_state))\n",
    "all_states.append(((5,0),(-10, -10)))\n",
    "\n",
    "#Define rewards for all states\n",
    "rewards = {}\n",
    "for ((i,j),(l,k)) in all_states:\n",
    "    if(i == j): # crushed\n",
    "        rewards[((i,j),(l,k))] = crushing\n",
    "    elif(i == 0 and j != 0): # blue agent in a1\n",
    "        rewards[((i,j),(l,k))] = richToDestination\n",
    "    else:\n",
    "        rewards[((i,j),(l,k))] = step\n",
    "\n",
    "#Dictionnary of possible actions. We have two \"end\" states (1,2 and 2,2)\n",
    "actions = {}\n",
    "for ((i,j),(l,k)) in all_states:\n",
    "    if i != 0 and i != j: #if s=((i, j),(l, k)) is not an \"end\" state\n",
    "        actions[((i,j),(l,k))] = (\"stay\", \"left\", \"down\") if (i < 6) else (\"up\", \"stay\")\n",
    "\n",
    "probs = json.load(open('data/probs2.json',))\n",
    "\n",
    "#Define an initial policy\n",
    "Policy={}\n",
    "for s in actions.keys():\n",
    "    Policy[s] = np.random.choice(actions[s])\n",
    "    \n",
    "#Define initial value function \n",
    "V={}\n",
    "for s in all_states:\n",
    "     V[s] = rewards[s]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nextStateDistibution2(s, a):\n",
    "    # given state s and action a, return distibution of possibles new states by probs\n",
    "    nxt = {}\n",
    "#     print('s: ', s)\n",
    "#     print('a: ', a)\n",
    "    bluePossition = newPosGivenAction(s[0], a, 0)\n",
    "#     print('bluePossition: ', bluePossition)\n",
    "    \n",
    "    # red possible action given state\n",
    "    if s[0][1] == 5:\n",
    "        actions=[\"stay\"]\n",
    "    elif s[0][1] < 6:\n",
    "        actions=[\"right\",\"stay\", \"down\"]\n",
    "    else:\n",
    "        actions=[\"stay\", \"up\"]\n",
    "#     print('actions: ', actions)\n",
    "    for r_a in actions:\n",
    "        redPossition = newPosGivenAction(s[0], r_a, 1)\n",
    "#         print('r_a: ', r_a)\n",
    "#         print('redPossition: ', redPossition)\n",
    "        newState = ((bluePossition, redPossition),s[0])\n",
    "#         print('newState: ', newState)\n",
    "        try:\n",
    "            nxt[newState] = probs[adapt_state(s[1]) + ' nxt: ' + adapt_state(s[0])][r_a]\n",
    "        except KeyError:\n",
    "#             print(\"Warning! State not found: assume random behavior. \", s[0])\n",
    "            nxt[newState] = randomProbForAction(s[0])\n",
    "    assert abs(sum(nxt.values()) - 1.0) < 0.0001, \"probs not sum to 1.0. \\nstate: \" + str(s) + \" action: \" + str(a) + \" nxt: \" + str(nxt) + \"\\nsum of probs: \" + str(sum(nxt.values()))\n",
    "    return nxt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{((0, 4), (0, 4)): 0.25, ((0, 5), (0, 4)): 0.5, ((0, 10), (0, 4)): 0.25}\n",
      "{((3, 1), (4, 1)): 0.0296052632,\n",
      " ((3, 2), (4, 1)): 0.8355263158,\n",
      " ((3, 7), (4, 1)): 0.1348684211}\n"
     ]
    }
   ],
   "source": [
    "pp.pprint(nextStateDistibution2(((0, 4),(1, 4)), \"stay\"))\n",
    "pp.pprint(nextStateDistibution2(((4, 1),(5, 0)), \"left\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('a2 b3', 'a2 b3')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(adapt_state((1, 8)), adapt_state((1, 8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('a2 a3', 'a2 b3')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(adapt_state((1, 2)), adapt_state((1, 8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('a2 b3', 'a3 b3')\n",
      "a3 b3 nxt: a2 b3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'right': 0.0, 'stay': 0.1232876712, 'up': 0.8767123288, 'down': 0.0}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = ((1, 8), (2, 8))\n",
    "print((adapt_state((1, 8)), adapt_state((2, 8))))\n",
    "print(adapt_state(s[1]) + ' nxt: ' + adapt_state(s[0]))\n",
    "probs[adapt_state(s[1]) + ' nxt: ' + adapt_state(s[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{((1, 2), (1, 8)): 0.8767123288, ((1, 8), (1, 8)): 0.1232876712}\n"
     ]
    }
   ],
   "source": [
    "pp.pprint(nextStateDistibution2(s, \"stay\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 ) \n",
      "converge after: 140 iterations\n"
     ]
    }
   ],
   "source": [
    "'''==================================================\n",
    "Value Iteration\n",
    "=================================================='''\n",
    "\n",
    "iteration = 1\n",
    "\n",
    "while True:\n",
    "    biggest_change = 0\n",
    "    for s in all_states:            \n",
    "        if s in Policy:\n",
    "            \n",
    "            old_v = V[s]\n",
    "            new_v = np.NINF\n",
    "            \n",
    "            for a in actions[s]:\n",
    "#                 print(\"actions: \", actions[s])\n",
    "#                 print(\"action: \", a)\n",
    "#                 print(\"state: \", s)\n",
    "                nxt = nextStateDistibution2(s, a)\n",
    "#                 print(\"nxt:\", nxt)\n",
    "                \n",
    "                v=0\n",
    "                #Calculate the value\n",
    "                for next_state, prob in nxt.items():\n",
    "#                     print(\"next_state: \", next_state)\n",
    "#                     print(\"prob: \", prob)\n",
    "#                     print('V[next_state]: ', V[next_state])\n",
    "                    assert rewards[s] == -1, \"rewards is not -1\"\n",
    "                    v += prob * (rewards[s] + GAMMA * V[next_state])\n",
    "#                 print('v: ', v)\n",
    "#                 print()\n",
    "                if v > new_v: #Is this the best action so far? If so, keep it\n",
    "                    new_v = v\n",
    "                    Policy[s] = a\n",
    "\n",
    "           #Save the best of all actions for the state                                \n",
    "            V[s] = new_v\n",
    "            biggest_change = max(biggest_change, np.abs(old_v - V[s]))\n",
    "\n",
    "            \n",
    "   #See if the loop should stop now         \n",
    "    if biggest_change < SMALL_ENOUGH:\n",
    "        print('converge after: ' + str(iteration) + ' iterations')\n",
    "        break\n",
    "    iteration += 1\n",
    "    \n",
    "    #Print every 100 iterations\n",
    "    if(iteration % 100 == 0):\n",
    "        print(iteration, ') ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convertStates(Policy):\n",
    "    P_convert= {}\n",
    "    V_convert = {}\n",
    "    for s,v in V.items():\n",
    "        V_convert[adapt_state(s[0]) + ',' + adapt_state(s[1])] = v\n",
    "    \n",
    "#     print()\n",
    "    for s,p in Policy.items():\n",
    "        P_convert[adapt_state(s[0]) + ',' + adapt_state(s[1])] = p\n",
    "    \n",
    "#     pp.pprint(V_convert)\n",
    "#     pp.pprint(P_convert)\n",
    "    return P_convert, V_convert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iterations:  140\n"
     ]
    }
   ],
   "source": [
    "# print the results:\n",
    "print(\"iterations: \", iteration)\n",
    "optimal_policy_v2, optimal_V_v2 = convertStates(Policy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a2 a1,a2 a1': 'down',\n",
       " 'a2 b1,a2 a1': 'down',\n",
       " 'b2 a2,a2 a1': 'up',\n",
       " 'b2 a1,a2 a1': 'stay',\n",
       " 'b2 b1,a2 a1': 'up',\n",
       " 'a2 a4,a2 a3': 'left',\n",
       " 'a2 a3,a2 a3': 'left',\n",
       " 'a2 b3,a2 a3': 'left',\n",
       " 'b2 a4,a2 a3': 'up',\n",
       " 'b2 a3,a2 a3': 'up',\n",
       " 'b2 b3,a2 a3': 'up',\n",
       " 'a2 a5,a2 a4': 'left',\n",
       " 'a2 a4,a2 a4': 'left',\n",
       " 'a2 b4,a2 a4': 'left',\n",
       " 'b2 a5,a2 a4': 'up',\n",
       " 'b2 a4,a2 a4': 'up',\n",
       " 'b2 b4,a2 a4': 'up',\n",
       " 'a2 a6,a2 a5': 'left',\n",
       " 'a2 a5,a2 a5': 'left',\n",
       " 'a2 b5,a2 a5': 'left',\n",
       " 'b2 a6,a2 a5': 'up',\n",
       " 'b2 a5,a2 a5': 'up',\n",
       " 'b2 b5,a2 a5': 'up',\n",
       " 'a2 a6,a2 a6': 'left',\n",
       " 'b2 a6,a2 a6': 'up',\n",
       " 'a2 b1,a2 b1': 'down',\n",
       " 'a2 a1,a2 b1': 'down',\n",
       " 'b2 b1,a2 b1': 'up',\n",
       " 'b2 a1,a2 b1': 'stay',\n",
       " 'a2 b2,a2 b2': 'left',\n",
       " 'b2 a2,a2 b2': 'up',\n",
       " 'a2 b3,a2 b3': 'left',\n",
       " 'a2 a3,a2 b3': 'left',\n",
       " 'b2 b3,a2 b3': 'up',\n",
       " 'b2 a3,a2 b3': 'up',\n",
       " 'a2 b4,a2 b4': 'left',\n",
       " 'a2 a4,a2 b4': 'left',\n",
       " 'b2 b4,a2 b4': 'up',\n",
       " 'b2 a4,a2 b4': 'up',\n",
       " 'a2 b5,a2 b5': 'left',\n",
       " 'a2 a5,a2 b5': 'left',\n",
       " 'b2 b5,a2 b5': 'up',\n",
       " 'b2 a5,a2 b5': 'up',\n",
       " 'a2 b6,a2 b6': 'left',\n",
       " 'a2 a6,a2 b6': 'left',\n",
       " 'b2 b6,a2 b6': 'up',\n",
       " 'b2 a6,a2 b6': 'up',\n",
       " 'a3 a2,a3 a1': 'down',\n",
       " 'a3 a1,a3 a1': 'down',\n",
       " 'a3 b1,a3 a1': 'down',\n",
       " 'a2 a1,a3 a1': 'down',\n",
       " 'a2 b1,a3 a1': 'down',\n",
       " 'b3 a2,a3 a1': 'stay',\n",
       " 'b3 a1,a3 a1': 'stay',\n",
       " 'b3 b1,a3 a1': 'stay',\n",
       " 'a3 a2,a3 a2': 'down',\n",
       " 'a3 b2,a3 a2': 'stay',\n",
       " 'a2 a3,a3 a2': 'left',\n",
       " 'a2 b2,a3 a2': 'left',\n",
       " 'b3 a3,a3 a2': 'up',\n",
       " 'b3 a2,a3 a2': 'stay',\n",
       " 'b3 b2,a3 a2': 'up',\n",
       " 'a3 a5,a3 a4': 'left',\n",
       " 'a3 a4,a3 a4': 'left',\n",
       " 'a3 b4,a3 a4': 'left',\n",
       " 'a2 a5,a3 a4': 'left',\n",
       " 'a2 a4,a3 a4': 'left',\n",
       " 'a2 b4,a3 a4': 'left',\n",
       " 'b3 a5,a3 a4': 'up',\n",
       " 'b3 a4,a3 a4': 'up',\n",
       " 'b3 b4,a3 a4': 'up',\n",
       " 'a3 a6,a3 a5': 'left',\n",
       " 'a3 a5,a3 a5': 'left',\n",
       " 'a3 b5,a3 a5': 'left',\n",
       " 'a2 a6,a3 a5': 'left',\n",
       " 'a2 a5,a3 a5': 'left',\n",
       " 'a2 b5,a3 a5': 'left',\n",
       " 'b3 a6,a3 a5': 'up',\n",
       " 'b3 a5,a3 a5': 'up',\n",
       " 'b3 b5,a3 a5': 'up',\n",
       " 'a3 a6,a3 a6': 'left',\n",
       " 'a2 a6,a3 a6': 'left',\n",
       " 'b3 a6,a3 a6': 'up',\n",
       " 'a3 b1,a3 b1': 'down',\n",
       " 'a3 a1,a3 b1': 'down',\n",
       " 'a2 b1,a3 b1': 'left',\n",
       " 'a2 a1,a3 b1': 'left',\n",
       " 'b3 b1,a3 b1': 'stay',\n",
       " 'b3 a1,a3 b1': 'stay',\n",
       " 'a3 b2,a3 b2': 'down',\n",
       " 'a3 a2,a3 b2': 'down',\n",
       " 'a2 b2,a3 b2': 'left',\n",
       " 'b3 b2,a3 b2': 'up',\n",
       " 'b3 a2,a3 b2': 'stay',\n",
       " 'a3 b3,a3 b3': 'left',\n",
       " 'a2 b3,a3 b3': 'left',\n",
       " 'a2 a3,a3 b3': 'left',\n",
       " 'b3 a3,a3 b3': 'up',\n",
       " 'a3 b4,a3 b4': 'left',\n",
       " 'a3 a4,a3 b4': 'left',\n",
       " 'a2 b4,a3 b4': 'left',\n",
       " 'a2 a4,a3 b4': 'left',\n",
       " 'b3 b4,a3 b4': 'up',\n",
       " 'b3 a4,a3 b4': 'up',\n",
       " 'a3 b5,a3 b5': 'left',\n",
       " 'a3 a5,a3 b5': 'left',\n",
       " 'a2 b5,a3 b5': 'left',\n",
       " 'a2 a5,a3 b5': 'left',\n",
       " 'b3 b5,a3 b5': 'up',\n",
       " 'b3 a5,a3 b5': 'up',\n",
       " 'a3 b6,a3 b6': 'left',\n",
       " 'a3 a6,a3 b6': 'left',\n",
       " 'a2 b6,a3 b6': 'left',\n",
       " 'a2 a6,a3 b6': 'left',\n",
       " 'b3 b6,a3 b6': 'up',\n",
       " 'b3 a6,a3 b6': 'up',\n",
       " 'a4 a2,a4 a1': 'stay',\n",
       " 'a4 a1,a4 a1': 'left',\n",
       " 'a4 b1,a4 a1': 'stay',\n",
       " 'a3 a2,a4 a1': 'left',\n",
       " 'a3 a1,a4 a1': 'down',\n",
       " 'a3 b1,a4 a1': 'down',\n",
       " 'b4 a2,a4 a1': 'up',\n",
       " 'b4 a1,a4 a1': 'up',\n",
       " 'b4 b1,a4 a1': 'up',\n",
       " 'a4 a3,a4 a2': 'down',\n",
       " 'a4 a2,a4 a2': 'stay',\n",
       " 'a4 b2,a4 a2': 'stay',\n",
       " 'a3 a2,a4 a2': 'down',\n",
       " 'a3 b2,a4 a2': 'stay',\n",
       " 'b4 a3,a4 a2': 'stay',\n",
       " 'b4 a2,a4 a2': 'up',\n",
       " 'b4 b2,a4 a2': 'up',\n",
       " 'a4 a3,a4 a3': 'down',\n",
       " 'a4 b3,a4 a3': 'down',\n",
       " 'a3 a4,a4 a3': 'left',\n",
       " 'a3 b3,a4 a3': 'left',\n",
       " 'b4 a4,a4 a3': 'up',\n",
       " 'b4 a3,a4 a3': 'stay',\n",
       " 'b4 b3,a4 a3': 'stay',\n",
       " 'a4 a6,a4 a5': 'left',\n",
       " 'a4 a5,a4 a5': 'left',\n",
       " 'a4 b5,a4 a5': 'left',\n",
       " 'a3 a6,a4 a5': 'left',\n",
       " 'a3 a5,a4 a5': 'left',\n",
       " 'a3 b5,a4 a5': 'left',\n",
       " 'b4 a6,a4 a5': 'up',\n",
       " 'b4 a5,a4 a5': 'up',\n",
       " 'b4 b5,a4 a5': 'up',\n",
       " 'a4 a6,a4 a6': 'left',\n",
       " 'a3 a6,a4 a6': 'left',\n",
       " 'b4 a6,a4 a6': 'up',\n",
       " 'a4 b1,a4 b1': 'down',\n",
       " 'a4 a1,a4 b1': 'left',\n",
       " 'a3 b1,a4 b1': 'down',\n",
       " 'a3 a1,a4 b1': 'stay',\n",
       " 'b4 b1,a4 b1': 'up',\n",
       " 'b4 a1,a4 b1': 'up',\n",
       " 'a4 b2,a4 b2': 'left',\n",
       " 'a4 a2,a4 b2': 'stay',\n",
       " 'a3 b2,a4 b2': 'down',\n",
       " 'a3 a2,a4 b2': 'left',\n",
       " 'b4 b2,a4 b2': 'up',\n",
       " 'b4 a2,a4 b2': 'up',\n",
       " 'a4 b3,a4 b3': 'down',\n",
       " 'a4 a3,a4 b3': 'down',\n",
       " 'a3 b3,a4 b3': 'left',\n",
       " 'b4 b3,a4 b3': 'stay',\n",
       " 'b4 a3,a4 b3': 'stay',\n",
       " 'a4 b4,a4 b4': 'left',\n",
       " 'a3 b4,a4 b4': 'left',\n",
       " 'a3 a4,a4 b4': 'left',\n",
       " 'b4 a4,a4 b4': 'up',\n",
       " 'a4 b5,a4 b5': 'left',\n",
       " 'a4 a5,a4 b5': 'left',\n",
       " 'a3 b5,a4 b5': 'left',\n",
       " 'a3 a5,a4 b5': 'left',\n",
       " 'b4 b5,a4 b5': 'up',\n",
       " 'b4 a5,a4 b5': 'up',\n",
       " 'a4 b6,a4 b6': 'left',\n",
       " 'a4 a6,a4 b6': 'left',\n",
       " 'a3 b6,a4 b6': 'left',\n",
       " 'a3 a6,a4 b6': 'left',\n",
       " 'b4 b6,a4 b6': 'up',\n",
       " 'b4 a6,a4 b6': 'up',\n",
       " 'a5 a2,a5 a1': 'left',\n",
       " 'a5 a1,a5 a1': 'stay',\n",
       " 'a5 b1,a5 a1': 'left',\n",
       " 'a4 a2,a5 a1': 'stay',\n",
       " 'a4 a1,a5 a1': 'left',\n",
       " 'a4 b1,a5 a1': 'stay',\n",
       " 'b5 a2,a5 a1': 'up',\n",
       " 'b5 a1,a5 a1': 'up',\n",
       " 'b5 b1,a5 a1': 'up',\n",
       " 'a5 a3,a5 a2': 'stay',\n",
       " 'a5 a2,a5 a2': 'left',\n",
       " 'a5 b2,a5 a2': 'stay',\n",
       " 'a4 a3,a5 a2': 'left',\n",
       " 'a4 a2,a5 a2': 'stay',\n",
       " 'a4 b2,a5 a2': 'left',\n",
       " 'b5 a3,a5 a2': 'up',\n",
       " 'b5 a2,a5 a2': 'up',\n",
       " 'b5 b2,a5 a2': 'up',\n",
       " 'a5 a4,a5 a3': 'left',\n",
       " 'a5 a3,a5 a3': 'stay',\n",
       " 'a5 b3,a5 a3': 'left',\n",
       " 'a4 a3,a5 a3': 'down',\n",
       " 'a4 b3,a5 a3': 'down',\n",
       " 'b5 a4,a5 a3': 'stay',\n",
       " 'b5 a3,a5 a3': 'up',\n",
       " 'b5 b3,a5 a3': 'up',\n",
       " 'a5 a4,a5 a4': 'down',\n",
       " 'a5 b4,a5 a4': 'down',\n",
       " 'a4 a5,a5 a4': 'left',\n",
       " 'a4 b4,a5 a4': 'left',\n",
       " 'b5 a5,a5 a4': 'up',\n",
       " 'b5 a4,a5 a4': 'stay',\n",
       " 'b5 b4,a5 a4': 'stay',\n",
       " 'a5 a6,a5 a6': 'left',\n",
       " 'a4 a6,a5 a6': 'left',\n",
       " 'b5 a6,a5 a6': 'up',\n",
       " 'a5 b1,a5 b1': 'stay',\n",
       " 'a5 a1,a5 b1': 'stay',\n",
       " 'a4 b1,a5 b1': 'stay',\n",
       " 'a4 a1,a5 b1': 'left',\n",
       " 'b5 b1,a5 b1': 'up',\n",
       " 'b5 a1,a5 b1': 'up',\n",
       " 'a5 b2,a5 b2': 'stay',\n",
       " 'a5 a2,a5 b2': 'left',\n",
       " 'a4 b2,a5 b2': 'left',\n",
       " 'a4 a2,a5 b2': 'stay',\n",
       " 'b5 b2,a5 b2': 'up',\n",
       " 'b5 a2,a5 b2': 'up',\n",
       " 'a5 b3,a5 b3': 'left',\n",
       " 'a5 a3,a5 b3': 'stay',\n",
       " 'a4 b3,a5 b3': 'down',\n",
       " 'a4 a3,a5 b3': 'down',\n",
       " 'b5 b3,a5 b3': 'up',\n",
       " 'b5 a3,a5 b3': 'up',\n",
       " 'a5 b4,a5 b4': 'down',\n",
       " 'a5 a4,a5 b4': 'down',\n",
       " 'a4 b4,a5 b4': 'left',\n",
       " 'b5 b4,a5 b4': 'stay',\n",
       " 'b5 a4,a5 b4': 'stay',\n",
       " 'a5 b5,a5 b5': 'left',\n",
       " 'a4 b5,a5 b5': 'left',\n",
       " 'a4 a5,a5 b5': 'left',\n",
       " 'b5 a5,a5 b5': 'up',\n",
       " 'a5 b6,a5 b6': 'left',\n",
       " 'a5 a6,a5 b6': 'left',\n",
       " 'a4 b6,a5 b6': 'left',\n",
       " 'a4 a6,a5 b6': 'left',\n",
       " 'b5 b6,a5 b6': 'up',\n",
       " 'b5 a6,a5 b6': 'up',\n",
       " 'a6 a2,a6 a1': 'stay',\n",
       " 'a6 a1,a6 a1': 'left',\n",
       " 'a6 b1,a6 a1': 'left',\n",
       " 'a5 a2,a6 a1': 'left',\n",
       " 'a5 a1,a6 a1': 'stay',\n",
       " 'a5 b1,a6 a1': 'left',\n",
       " 'b6 a2,a6 a1': 'up',\n",
       " 'b6 a1,a6 a1': 'up',\n",
       " 'b6 b1,a6 a1': 'up',\n",
       " 'a6 a3,a6 a2': 'stay',\n",
       " 'a6 a2,a6 a2': 'stay',\n",
       " 'a6 b2,a6 a2': 'left',\n",
       " 'a5 a3,a6 a2': 'stay',\n",
       " 'a5 a2,a6 a2': 'left',\n",
       " 'a5 b2,a6 a2': 'stay',\n",
       " 'b6 a3,a6 a2': 'up',\n",
       " 'b6 a2,a6 a2': 'up',\n",
       " 'b6 b2,a6 a2': 'up',\n",
       " 'a6 a4,a6 a3': 'stay',\n",
       " 'a6 a3,a6 a3': 'stay',\n",
       " 'a6 b3,a6 a3': 'stay',\n",
       " 'a5 a4,a6 a3': 'down',\n",
       " 'a5 a3,a6 a3': 'stay',\n",
       " 'a5 b3,a6 a3': 'left',\n",
       " 'b6 a4,a6 a3': 'up',\n",
       " 'b6 a3,a6 a3': 'up',\n",
       " 'b6 b3,a6 a3': 'up',\n",
       " 'a6 a5,a6 a4': 'left',\n",
       " 'a6 a4,a6 a4': 'stay',\n",
       " 'a6 b4,a6 a4': 'stay',\n",
       " 'a5 a4,a6 a4': 'down',\n",
       " 'a5 b4,a6 a4': 'down',\n",
       " 'b6 a5,a6 a4': 'up',\n",
       " 'b6 a4,a6 a4': 'up',\n",
       " 'b6 b4,a6 a4': 'up',\n",
       " 'a6 a5,a6 a5': 'left',\n",
       " 'a6 b5,a6 a5': 'stay',\n",
       " 'a5 a6,a6 a5': 'left',\n",
       " 'a5 b5,a6 a5': 'left',\n",
       " 'b6 a6,a6 a5': 'up',\n",
       " 'b6 a5,a6 a5': 'up',\n",
       " 'b6 b5,a6 a5': 'up',\n",
       " 'a6 b1,a6 b1': 'left',\n",
       " 'a6 a1,a6 b1': 'left',\n",
       " 'a5 b1,a6 b1': 'stay',\n",
       " 'a5 a1,a6 b1': 'stay',\n",
       " 'b6 b1,a6 b1': 'up',\n",
       " 'b6 a1,a6 b1': 'up',\n",
       " 'a6 b2,a6 b2': 'left',\n",
       " 'a6 a2,a6 b2': 'stay',\n",
       " 'a5 b2,a6 b2': 'stay',\n",
       " 'a5 a2,a6 b2': 'left',\n",
       " 'b6 b2,a6 b2': 'up',\n",
       " 'b6 a2,a6 b2': 'up',\n",
       " 'a6 b3,a6 b3': 'stay',\n",
       " 'a6 a3,a6 b3': 'stay',\n",
       " 'a5 b3,a6 b3': 'left',\n",
       " 'a5 a3,a6 b3': 'stay',\n",
       " 'b6 b3,a6 b3': 'up',\n",
       " 'b6 a3,a6 b3': 'up',\n",
       " 'a6 b4,a6 b4': 'stay',\n",
       " 'a6 a4,a6 b4': 'stay',\n",
       " 'a5 b4,a6 b4': 'down',\n",
       " 'a5 a4,a6 b4': 'down',\n",
       " 'b6 b4,a6 b4': 'up',\n",
       " 'b6 a4,a6 b4': 'up',\n",
       " 'a6 b5,a6 b5': 'stay',\n",
       " 'a6 a5,a6 b5': 'left',\n",
       " 'a5 b5,a6 b5': 'left',\n",
       " 'b6 b5,a6 b5': 'up',\n",
       " 'b6 a5,a6 b5': 'up',\n",
       " 'a6 b6,a6 b6': 'left',\n",
       " 'a5 b6,a6 b6': 'left',\n",
       " 'a5 a6,a6 b6': 'left',\n",
       " 'b6 a6,a6 b6': 'up',\n",
       " 'b1 a2,b1 a1': 'up',\n",
       " 'b1 a1,b1 a1': 'up',\n",
       " 'b1 a3,b1 a2': 'up',\n",
       " 'b1 a2,b1 a2': 'up',\n",
       " 'b1 b2,b1 a2': 'up',\n",
       " 'b1 a4,b1 a3': 'up',\n",
       " 'b1 a3,b1 a3': 'up',\n",
       " 'b1 b3,b1 a3': 'up',\n",
       " 'b1 a5,b1 a4': 'up',\n",
       " 'b1 a4,b1 a4': 'up',\n",
       " 'b1 b4,b1 a4': 'up',\n",
       " 'b1 a6,b1 a5': 'up',\n",
       " 'b1 a5,b1 a5': 'up',\n",
       " 'b1 b5,b1 a5': 'up',\n",
       " 'b1 a6,b1 a6': 'up',\n",
       " 'b1 b2,b1 b2': 'up',\n",
       " 'b1 a2,b1 b2': 'up',\n",
       " 'b1 b3,b1 b3': 'up',\n",
       " 'b1 a3,b1 b3': 'up',\n",
       " 'b1 b4,b1 b4': 'up',\n",
       " 'b1 a4,b1 b4': 'up',\n",
       " 'b1 b5,b1 b5': 'up',\n",
       " 'b1 a5,b1 b5': 'up',\n",
       " 'b1 b6,b1 b6': 'up',\n",
       " 'b1 a6,b1 b6': 'up',\n",
       " 'a2 a1,b2 a1': 'down',\n",
       " 'a2 b1,b2 a1': 'down',\n",
       " 'b2 a2,b2 a1': 'up',\n",
       " 'b2 a1,b2 a1': 'stay',\n",
       " 'b2 b1,b2 a1': 'up',\n",
       " 'a2 a3,b2 a2': 'left',\n",
       " 'a2 b2,b2 a2': 'left',\n",
       " 'b2 a3,b2 a2': 'up',\n",
       " 'b2 a2,b2 a2': 'up',\n",
       " 'a2 a4,b2 a3': 'left',\n",
       " 'a2 a3,b2 a3': 'left',\n",
       " 'a2 b3,b2 a3': 'left',\n",
       " 'b2 a4,b2 a3': 'up',\n",
       " 'b2 a3,b2 a3': 'up',\n",
       " 'b2 b3,b2 a3': 'up',\n",
       " 'a2 a5,b2 a4': 'left',\n",
       " 'a2 a4,b2 a4': 'left',\n",
       " 'a2 b4,b2 a4': 'left',\n",
       " 'b2 a5,b2 a4': 'up',\n",
       " 'b2 a4,b2 a4': 'up',\n",
       " 'b2 b4,b2 a4': 'up',\n",
       " 'a2 a6,b2 a5': 'left',\n",
       " 'a2 a5,b2 a5': 'left',\n",
       " 'a2 b5,b2 a5': 'left',\n",
       " 'b2 a6,b2 a5': 'up',\n",
       " 'b2 a5,b2 a5': 'up',\n",
       " 'b2 b5,b2 a5': 'up',\n",
       " 'a2 a6,b2 a6': 'left',\n",
       " 'b2 a6,b2 a6': 'up',\n",
       " 'a2 b1,b2 b1': 'down',\n",
       " 'a2 a1,b2 b1': 'left',\n",
       " 'b2 b1,b2 b1': 'up',\n",
       " 'b2 a1,b2 b1': 'stay',\n",
       " 'a2 b3,b2 b3': 'left',\n",
       " 'a2 a3,b2 b3': 'left',\n",
       " 'b2 b3,b2 b3': 'up',\n",
       " 'b2 a3,b2 b3': 'up',\n",
       " 'a2 b4,b2 b4': 'left',\n",
       " 'a2 a4,b2 b4': 'left',\n",
       " 'b2 b4,b2 b4': 'up',\n",
       " 'b2 a4,b2 b4': 'up',\n",
       " 'a2 b5,b2 b5': 'left',\n",
       " 'a2 a5,b2 b5': 'left',\n",
       " 'b2 b5,b2 b5': 'up',\n",
       " 'b2 a5,b2 b5': 'up',\n",
       " 'a2 b6,b2 b6': 'left',\n",
       " 'a2 a6,b2 b6': 'left',\n",
       " 'b2 b6,b2 b6': 'up',\n",
       " 'b2 a6,b2 b6': 'up',\n",
       " 'a3 a2,b3 a1': 'down',\n",
       " 'a3 a1,b3 a1': 'down',\n",
       " 'a3 b1,b3 a1': 'down',\n",
       " 'b3 a2,b3 a1': 'stay',\n",
       " 'b3 a1,b3 a1': 'up',\n",
       " 'b3 b1,b3 a1': 'stay',\n",
       " 'a3 a2,b3 a2': 'down',\n",
       " 'a3 b2,b3 a2': 'stay',\n",
       " 'b3 a3,b3 a2': 'up',\n",
       " 'b3 a2,b3 a2': 'stay',\n",
       " 'b3 b2,b3 a2': 'up',\n",
       " 'a3 a4,b3 a3': 'left',\n",
       " 'a3 b3,b3 a3': 'left',\n",
       " 'b3 a4,b3 a3': 'up',\n",
       " 'b3 a3,b3 a3': 'up',\n",
       " 'a3 a5,b3 a4': 'left',\n",
       " 'a3 a4,b3 a4': 'left',\n",
       " 'a3 b4,b3 a4': 'left',\n",
       " 'b3 a5,b3 a4': 'up',\n",
       " 'b3 a4,b3 a4': 'up',\n",
       " 'b3 b4,b3 a4': 'up',\n",
       " 'a3 a6,b3 a5': 'left',\n",
       " 'a3 a5,b3 a5': 'left',\n",
       " 'a3 b5,b3 a5': 'left',\n",
       " 'b3 a6,b3 a5': 'up',\n",
       " 'b3 a5,b3 a5': 'up',\n",
       " 'b3 b5,b3 a5': 'up',\n",
       " 'a3 a6,b3 a6': 'left',\n",
       " 'b3 a6,b3 a6': 'up',\n",
       " 'a3 b1,b3 b1': 'down',\n",
       " 'a3 a1,b3 b1': 'stay',\n",
       " 'b3 b1,b3 b1': 'stay',\n",
       " 'b3 a1,b3 b1': 'stay',\n",
       " 'a3 b2,b3 b2': 'stay',\n",
       " 'a3 a2,b3 b2': 'left',\n",
       " 'b3 b2,b3 b2': 'up',\n",
       " 'b3 a2,b3 b2': 'stay',\n",
       " 'a3 b4,b3 b4': 'left',\n",
       " 'a3 a4,b3 b4': 'left',\n",
       " 'b3 b4,b3 b4': 'up',\n",
       " 'b3 a4,b3 b4': 'up',\n",
       " 'a3 b5,b3 b5': 'left',\n",
       " 'a3 a5,b3 b5': 'left',\n",
       " 'b3 b5,b3 b5': 'up',\n",
       " 'b3 a5,b3 b5': 'up',\n",
       " 'a3 b6,b3 b6': 'left',\n",
       " 'a3 a6,b3 b6': 'left',\n",
       " 'b3 b6,b3 b6': 'up',\n",
       " 'b3 a6,b3 b6': 'up',\n",
       " 'a4 a2,b4 a1': 'stay',\n",
       " 'a4 a1,b4 a1': 'left',\n",
       " 'a4 b1,b4 a1': 'stay',\n",
       " 'b4 a2,b4 a1': 'up',\n",
       " 'b4 a1,b4 a1': 'up',\n",
       " 'b4 b1,b4 a1': 'up',\n",
       " 'a4 a3,b4 a2': 'down',\n",
       " 'a4 a2,b4 a2': 'stay',\n",
       " 'a4 b2,b4 a2': 'left',\n",
       " 'b4 a3,b4 a2': 'stay',\n",
       " 'b4 a2,b4 a2': 'up',\n",
       " 'b4 b2,b4 a2': 'up',\n",
       " 'a4 a3,b4 a3': 'down',\n",
       " 'a4 b3,b4 a3': 'down',\n",
       " 'b4 a4,b4 a3': 'up',\n",
       " 'b4 a3,b4 a3': 'stay',\n",
       " 'b4 b3,b4 a3': 'stay',\n",
       " 'a4 a5,b4 a4': 'left',\n",
       " 'a4 b4,b4 a4': 'left',\n",
       " 'b4 a5,b4 a4': 'up',\n",
       " 'b4 a4,b4 a4': 'stay',\n",
       " 'a4 a6,b4 a5': 'left',\n",
       " 'a4 a5,b4 a5': 'left',\n",
       " 'a4 b5,b4 a5': 'left',\n",
       " 'b4 a6,b4 a5': 'up',\n",
       " 'b4 a5,b4 a5': 'up',\n",
       " 'b4 b5,b4 a5': 'up',\n",
       " 'a4 a6,b4 a6': 'left',\n",
       " 'b4 a6,b4 a6': 'up',\n",
       " 'a4 b1,b4 b1': 'stay',\n",
       " 'a4 a1,b4 b1': 'left',\n",
       " 'b4 b1,b4 b1': 'up',\n",
       " 'b4 a1,b4 b1': 'up',\n",
       " 'a4 b2,b4 b2': 'left',\n",
       " 'a4 a2,b4 b2': 'stay',\n",
       " 'b4 b2,b4 b2': 'up',\n",
       " 'b4 a2,b4 b2': 'up',\n",
       " 'a4 b3,b4 b3': 'down',\n",
       " 'a4 a3,b4 b3': 'down',\n",
       " 'b4 b3,b4 b3': 'stay',\n",
       " 'b4 a3,b4 b3': 'stay',\n",
       " 'a4 b5,b4 b5': 'left',\n",
       " 'a4 a5,b4 b5': 'left',\n",
       " 'b4 b5,b4 b5': 'up',\n",
       " 'b4 a5,b4 b5': 'up',\n",
       " 'a4 b6,b4 b6': 'left',\n",
       " 'a4 a6,b4 b6': 'left',\n",
       " 'b4 b6,b4 b6': 'up',\n",
       " 'b4 a6,b4 b6': 'up',\n",
       " 'a5 a2,b5 a1': 'left',\n",
       " 'a5 a1,b5 a1': 'stay',\n",
       " 'a5 b1,b5 a1': 'stay',\n",
       " 'b5 a2,b5 a1': 'up',\n",
       " 'b5 a1,b5 a1': 'up',\n",
       " 'b5 b1,b5 a1': 'up',\n",
       " 'a5 a3,b5 a2': 'stay',\n",
       " 'a5 a2,b5 a2': 'left',\n",
       " 'a5 b2,b5 a2': 'stay',\n",
       " 'b5 a3,b5 a2': 'up',\n",
       " 'b5 a2,b5 a2': 'up',\n",
       " 'b5 b2,b5 a2': 'up',\n",
       " 'a5 a4,b5 a3': 'left',\n",
       " 'a5 a3,b5 a3': 'stay',\n",
       " 'a5 b3,b5 a3': 'left',\n",
       " 'b5 a4,b5 a3': 'stay',\n",
       " 'b5 a3,b5 a3': 'up',\n",
       " 'b5 b3,b5 a3': 'up',\n",
       " 'a5 a4,b5 a4': 'down',\n",
       " 'a5 b4,b5 a4': 'down',\n",
       " 'b5 a5,b5 a4': 'up',\n",
       " 'b5 a4,b5 a4': 'stay',\n",
       " 'b5 b4,b5 a4': 'stay',\n",
       " 'a5 a6,b5 a5': 'left',\n",
       " 'a5 b5,b5 a5': 'left',\n",
       " 'b5 a6,b5 a5': 'up',\n",
       " 'b5 a5,b5 a5': 'up',\n",
       " 'a5 a6,b5 a6': 'left',\n",
       " 'b5 a6,b5 a6': 'up',\n",
       " 'a5 b1,b5 b1': 'stay',\n",
       " 'a5 a1,b5 b1': 'stay',\n",
       " 'b5 b1,b5 b1': 'up',\n",
       " 'b5 a1,b5 b1': 'up',\n",
       " 'a5 b2,b5 b2': 'stay',\n",
       " 'a5 a2,b5 b2': 'left',\n",
       " 'b5 b2,b5 b2': 'up',\n",
       " 'b5 a2,b5 b2': 'up',\n",
       " 'a5 b3,b5 b3': 'left',\n",
       " 'a5 a3,b5 b3': 'stay',\n",
       " 'b5 b3,b5 b3': 'up',\n",
       " 'b5 a3,b5 b3': 'up',\n",
       " 'a5 b4,b5 b4': 'down',\n",
       " 'a5 a4,b5 b4': 'down',\n",
       " 'b5 b4,b5 b4': 'stay',\n",
       " 'b5 a4,b5 b4': 'stay',\n",
       " 'a5 b6,b5 b6': 'left',\n",
       " 'a5 a6,b5 b6': 'left',\n",
       " 'b5 b6,b5 b6': 'up',\n",
       " 'b5 a6,b5 b6': 'up',\n",
       " 'a6 a2,b6 a1': 'stay',\n",
       " 'a6 a1,b6 a1': 'left',\n",
       " 'a6 b1,b6 a1': 'stay',\n",
       " 'b6 a2,b6 a1': 'up',\n",
       " 'b6 a1,b6 a1': 'up',\n",
       " 'b6 b1,b6 a1': 'up',\n",
       " 'a6 a3,b6 a2': 'stay',\n",
       " 'a6 a2,b6 a2': 'stay',\n",
       " 'a6 b2,b6 a2': 'left',\n",
       " 'b6 a3,b6 a2': 'up',\n",
       " 'b6 a2,b6 a2': 'up',\n",
       " 'b6 b2,b6 a2': 'up',\n",
       " 'a6 a4,b6 a3': 'stay',\n",
       " 'a6 a3,b6 a3': 'stay',\n",
       " 'a6 b3,b6 a3': 'stay',\n",
       " 'b6 a4,b6 a3': 'up',\n",
       " 'b6 a3,b6 a3': 'up',\n",
       " 'b6 b3,b6 a3': 'up',\n",
       " 'a6 a5,b6 a4': 'left',\n",
       " 'a6 a4,b6 a4': 'stay',\n",
       " 'a6 b4,b6 a4': 'stay',\n",
       " 'b6 a5,b6 a4': 'up',\n",
       " 'b6 a4,b6 a4': 'up',\n",
       " 'b6 b4,b6 a4': 'up',\n",
       " 'a6 a5,b6 a5': 'left',\n",
       " 'a6 b5,b6 a5': 'stay',\n",
       " 'b6 a6,b6 a5': 'up',\n",
       " 'b6 a5,b6 a5': 'up',\n",
       " 'b6 b5,b6 a5': 'up',\n",
       " 'b6 a6,b6 a6': 'up',\n",
       " 'a6 b1,b6 b1': 'left',\n",
       " 'a6 a1,b6 b1': 'left',\n",
       " 'b6 b1,b6 b1': 'up',\n",
       " 'b6 a1,b6 b1': 'up',\n",
       " 'a6 b2,b6 b2': 'left',\n",
       " 'a6 a2,b6 b2': 'stay',\n",
       " 'b6 b2,b6 b2': 'up',\n",
       " 'b6 a2,b6 b2': 'up',\n",
       " 'a6 b3,b6 b3': 'stay',\n",
       " 'a6 a3,b6 b3': 'stay',\n",
       " 'b6 b3,b6 b3': 'up',\n",
       " 'b6 a3,b6 b3': 'up',\n",
       " 'a6 b4,b6 b4': 'stay',\n",
       " 'a6 a4,b6 b4': 'stay',\n",
       " 'b6 b4,b6 b4': 'up',\n",
       " 'b6 a4,b6 b4': 'up',\n",
       " 'a6 b5,b6 b5': 'stay',\n",
       " 'a6 a5,b6 b5': 'left',\n",
       " 'b6 b5,b6 b5': 'up',\n",
       " 'b6 a5,b6 b5': 'up',\n",
       " 'a6 a1,a-9 a-9': 'left'}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimal_policy_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def policy_eval2(policy):\n",
    "    V={}\n",
    "    for s in all_states:\n",
    "        V[s] = rewards[s]\n",
    "    while True:\n",
    "        biggest_change = 0\n",
    "        for s in all_states:\n",
    "            if s in policy:\n",
    "                old_v = V[s]\n",
    "                nxt = nextStateDistibution2(s, policy[s])\n",
    "                v=0\n",
    "                #Calculate the value\n",
    "                for next_state, prob in nxt.items():\n",
    "                    assert rewards[s] == -1, \"rewards[s] is not -1\"\n",
    "                    v += prob * (rewards[s] + GAMMA * V[next_state])\n",
    "        \n",
    "                #Save the best of all actions for the state                                \n",
    "                V[s] = v\n",
    "                biggest_change = max(biggest_change, np.abs(old_v - V[s]))\n",
    "        #See if the loop should stop now         \n",
    "        if biggest_change < SMALL_ENOUGH:\n",
    "            return V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{((1, 0), (1, 0)): 'down',\n",
       " ((1, 6), (1, 0)): 'down',\n",
       " ((7, 1), (1, 0)): 'up',\n",
       " ((7, 0), (1, 0)): 'stay',\n",
       " ((7, 6), (1, 0)): 'up',\n",
       " ((1, 3), (1, 2)): 'left',\n",
       " ((1, 2), (1, 2)): 'left',\n",
       " ((1, 8), (1, 2)): 'left',\n",
       " ((7, 3), (1, 2)): 'up',\n",
       " ((7, 2), (1, 2)): 'up',\n",
       " ((7, 8), (1, 2)): 'up',\n",
       " ((1, 4), (1, 3)): 'left',\n",
       " ((1, 3), (1, 3)): 'left',\n",
       " ((1, 9), (1, 3)): 'left',\n",
       " ((7, 4), (1, 3)): 'up',\n",
       " ((7, 3), (1, 3)): 'up',\n",
       " ((7, 9), (1, 3)): 'up',\n",
       " ((1, 5), (1, 4)): 'left',\n",
       " ((1, 4), (1, 4)): 'left',\n",
       " ((1, 10), (1, 4)): 'left',\n",
       " ((7, 5), (1, 4)): 'up',\n",
       " ((7, 4), (1, 4)): 'up',\n",
       " ((7, 10), (1, 4)): 'up',\n",
       " ((1, 5), (1, 5)): 'left',\n",
       " ((7, 5), (1, 5)): 'up',\n",
       " ((1, 6), (1, 6)): 'down',\n",
       " ((1, 0), (1, 6)): 'down',\n",
       " ((7, 6), (1, 6)): 'up',\n",
       " ((7, 0), (1, 6)): 'stay',\n",
       " ((1, 7), (1, 7)): 'left',\n",
       " ((7, 1), (1, 7)): 'up',\n",
       " ((1, 8), (1, 8)): 'left',\n",
       " ((1, 2), (1, 8)): 'left',\n",
       " ((7, 8), (1, 8)): 'up',\n",
       " ((7, 2), (1, 8)): 'up',\n",
       " ((1, 9), (1, 9)): 'left',\n",
       " ((1, 3), (1, 9)): 'left',\n",
       " ((7, 9), (1, 9)): 'up',\n",
       " ((7, 3), (1, 9)): 'up',\n",
       " ((1, 10), (1, 10)): 'left',\n",
       " ((1, 4), (1, 10)): 'left',\n",
       " ((7, 10), (1, 10)): 'up',\n",
       " ((7, 4), (1, 10)): 'up',\n",
       " ((1, 11), (1, 11)): 'left',\n",
       " ((1, 5), (1, 11)): 'left',\n",
       " ((7, 11), (1, 11)): 'up',\n",
       " ((7, 5), (1, 11)): 'up',\n",
       " ((2, 1), (2, 0)): 'down',\n",
       " ((2, 0), (2, 0)): 'down',\n",
       " ((2, 6), (2, 0)): 'down',\n",
       " ((1, 0), (2, 0)): 'down',\n",
       " ((1, 6), (2, 0)): 'down',\n",
       " ((8, 1), (2, 0)): 'stay',\n",
       " ((8, 0), (2, 0)): 'stay',\n",
       " ((8, 6), (2, 0)): 'stay',\n",
       " ((2, 1), (2, 1)): 'down',\n",
       " ((2, 7), (2, 1)): 'stay',\n",
       " ((1, 2), (2, 1)): 'left',\n",
       " ((1, 7), (2, 1)): 'left',\n",
       " ((8, 2), (2, 1)): 'up',\n",
       " ((8, 1), (2, 1)): 'stay',\n",
       " ((8, 7), (2, 1)): 'up',\n",
       " ((2, 4), (2, 3)): 'left',\n",
       " ((2, 3), (2, 3)): 'left',\n",
       " ((2, 9), (2, 3)): 'left',\n",
       " ((1, 4), (2, 3)): 'left',\n",
       " ((1, 3), (2, 3)): 'left',\n",
       " ((1, 9), (2, 3)): 'left',\n",
       " ((8, 4), (2, 3)): 'up',\n",
       " ((8, 3), (2, 3)): 'up',\n",
       " ((8, 9), (2, 3)): 'up',\n",
       " ((2, 5), (2, 4)): 'left',\n",
       " ((2, 4), (2, 4)): 'left',\n",
       " ((2, 10), (2, 4)): 'left',\n",
       " ((1, 5), (2, 4)): 'left',\n",
       " ((1, 4), (2, 4)): 'left',\n",
       " ((1, 10), (2, 4)): 'left',\n",
       " ((8, 5), (2, 4)): 'up',\n",
       " ((8, 4), (2, 4)): 'up',\n",
       " ((8, 10), (2, 4)): 'up',\n",
       " ((2, 5), (2, 5)): 'left',\n",
       " ((1, 5), (2, 5)): 'left',\n",
       " ((8, 5), (2, 5)): 'up',\n",
       " ((2, 6), (2, 6)): 'down',\n",
       " ((2, 0), (2, 6)): 'down',\n",
       " ((1, 6), (2, 6)): 'left',\n",
       " ((1, 0), (2, 6)): 'left',\n",
       " ((8, 6), (2, 6)): 'stay',\n",
       " ((8, 0), (2, 6)): 'stay',\n",
       " ((2, 7), (2, 7)): 'down',\n",
       " ((2, 1), (2, 7)): 'down',\n",
       " ((1, 7), (2, 7)): 'left',\n",
       " ((8, 7), (2, 7)): 'up',\n",
       " ((8, 1), (2, 7)): 'stay',\n",
       " ((2, 8), (2, 8)): 'left',\n",
       " ((1, 8), (2, 8)): 'left',\n",
       " ((1, 2), (2, 8)): 'left',\n",
       " ((8, 2), (2, 8)): 'up',\n",
       " ((2, 9), (2, 9)): 'left',\n",
       " ((2, 3), (2, 9)): 'left',\n",
       " ((1, 9), (2, 9)): 'left',\n",
       " ((1, 3), (2, 9)): 'left',\n",
       " ((8, 9), (2, 9)): 'up',\n",
       " ((8, 3), (2, 9)): 'up',\n",
       " ((2, 10), (2, 10)): 'left',\n",
       " ((2, 4), (2, 10)): 'left',\n",
       " ((1, 10), (2, 10)): 'left',\n",
       " ((1, 4), (2, 10)): 'left',\n",
       " ((8, 10), (2, 10)): 'up',\n",
       " ((8, 4), (2, 10)): 'up',\n",
       " ((2, 11), (2, 11)): 'left',\n",
       " ((2, 5), (2, 11)): 'left',\n",
       " ((1, 11), (2, 11)): 'left',\n",
       " ((1, 5), (2, 11)): 'left',\n",
       " ((8, 11), (2, 11)): 'up',\n",
       " ((8, 5), (2, 11)): 'up',\n",
       " ((3, 1), (3, 0)): 'stay',\n",
       " ((3, 0), (3, 0)): 'left',\n",
       " ((3, 6), (3, 0)): 'stay',\n",
       " ((2, 1), (3, 0)): 'left',\n",
       " ((2, 0), (3, 0)): 'down',\n",
       " ((2, 6), (3, 0)): 'down',\n",
       " ((9, 1), (3, 0)): 'up',\n",
       " ((9, 0), (3, 0)): 'up',\n",
       " ((9, 6), (3, 0)): 'up',\n",
       " ((3, 2), (3, 1)): 'down',\n",
       " ((3, 1), (3, 1)): 'stay',\n",
       " ((3, 7), (3, 1)): 'stay',\n",
       " ((2, 1), (3, 1)): 'down',\n",
       " ((2, 7), (3, 1)): 'stay',\n",
       " ((9, 2), (3, 1)): 'stay',\n",
       " ((9, 1), (3, 1)): 'up',\n",
       " ((9, 7), (3, 1)): 'up',\n",
       " ((3, 2), (3, 2)): 'down',\n",
       " ((3, 8), (3, 2)): 'down',\n",
       " ((2, 3), (3, 2)): 'left',\n",
       " ((2, 8), (3, 2)): 'left',\n",
       " ((9, 3), (3, 2)): 'up',\n",
       " ((9, 2), (3, 2)): 'stay',\n",
       " ((9, 8), (3, 2)): 'stay',\n",
       " ((3, 5), (3, 4)): 'left',\n",
       " ((3, 4), (3, 4)): 'left',\n",
       " ((3, 10), (3, 4)): 'left',\n",
       " ((2, 5), (3, 4)): 'left',\n",
       " ((2, 4), (3, 4)): 'left',\n",
       " ((2, 10), (3, 4)): 'left',\n",
       " ((9, 5), (3, 4)): 'up',\n",
       " ((9, 4), (3, 4)): 'up',\n",
       " ((9, 10), (3, 4)): 'up',\n",
       " ((3, 5), (3, 5)): 'left',\n",
       " ((2, 5), (3, 5)): 'left',\n",
       " ((9, 5), (3, 5)): 'up',\n",
       " ((3, 6), (3, 6)): 'down',\n",
       " ((3, 0), (3, 6)): 'left',\n",
       " ((2, 6), (3, 6)): 'down',\n",
       " ((2, 0), (3, 6)): 'stay',\n",
       " ((9, 6), (3, 6)): 'up',\n",
       " ((9, 0), (3, 6)): 'up',\n",
       " ((3, 7), (3, 7)): 'left',\n",
       " ((3, 1), (3, 7)): 'stay',\n",
       " ((2, 7), (3, 7)): 'down',\n",
       " ((2, 1), (3, 7)): 'left',\n",
       " ((9, 7), (3, 7)): 'up',\n",
       " ((9, 1), (3, 7)): 'up',\n",
       " ((3, 8), (3, 8)): 'down',\n",
       " ((3, 2), (3, 8)): 'down',\n",
       " ((2, 8), (3, 8)): 'left',\n",
       " ((9, 8), (3, 8)): 'stay',\n",
       " ((9, 2), (3, 8)): 'stay',\n",
       " ((3, 9), (3, 9)): 'left',\n",
       " ((2, 9), (3, 9)): 'left',\n",
       " ((2, 3), (3, 9)): 'left',\n",
       " ((9, 3), (3, 9)): 'up',\n",
       " ((3, 10), (3, 10)): 'left',\n",
       " ((3, 4), (3, 10)): 'left',\n",
       " ((2, 10), (3, 10)): 'left',\n",
       " ((2, 4), (3, 10)): 'left',\n",
       " ((9, 10), (3, 10)): 'up',\n",
       " ((9, 4), (3, 10)): 'up',\n",
       " ((3, 11), (3, 11)): 'left',\n",
       " ((3, 5), (3, 11)): 'left',\n",
       " ((2, 11), (3, 11)): 'left',\n",
       " ((2, 5), (3, 11)): 'left',\n",
       " ((9, 11), (3, 11)): 'up',\n",
       " ((9, 5), (3, 11)): 'up',\n",
       " ((4, 1), (4, 0)): 'left',\n",
       " ((4, 0), (4, 0)): 'stay',\n",
       " ((4, 6), (4, 0)): 'left',\n",
       " ((3, 1), (4, 0)): 'stay',\n",
       " ((3, 0), (4, 0)): 'left',\n",
       " ((3, 6), (4, 0)): 'stay',\n",
       " ((10, 1), (4, 0)): 'up',\n",
       " ((10, 0), (4, 0)): 'up',\n",
       " ((10, 6), (4, 0)): 'up',\n",
       " ((4, 2), (4, 1)): 'stay',\n",
       " ((4, 1), (4, 1)): 'left',\n",
       " ((4, 7), (4, 1)): 'stay',\n",
       " ((3, 2), (4, 1)): 'left',\n",
       " ((3, 1), (4, 1)): 'stay',\n",
       " ((3, 7), (4, 1)): 'left',\n",
       " ((10, 2), (4, 1)): 'up',\n",
       " ((10, 1), (4, 1)): 'up',\n",
       " ((10, 7), (4, 1)): 'up',\n",
       " ((4, 3), (4, 2)): 'left',\n",
       " ((4, 2), (4, 2)): 'stay',\n",
       " ((4, 8), (4, 2)): 'left',\n",
       " ((3, 2), (4, 2)): 'down',\n",
       " ((3, 8), (4, 2)): 'down',\n",
       " ((10, 3), (4, 2)): 'stay',\n",
       " ((10, 2), (4, 2)): 'up',\n",
       " ((10, 8), (4, 2)): 'up',\n",
       " ((4, 3), (4, 3)): 'down',\n",
       " ((4, 9), (4, 3)): 'down',\n",
       " ((3, 4), (4, 3)): 'left',\n",
       " ((3, 9), (4, 3)): 'left',\n",
       " ((10, 4), (4, 3)): 'up',\n",
       " ((10, 3), (4, 3)): 'stay',\n",
       " ((10, 9), (4, 3)): 'stay',\n",
       " ((4, 5), (4, 5)): 'left',\n",
       " ((3, 5), (4, 5)): 'left',\n",
       " ((10, 5), (4, 5)): 'up',\n",
       " ((4, 6), (4, 6)): 'stay',\n",
       " ((4, 0), (4, 6)): 'stay',\n",
       " ((3, 6), (4, 6)): 'stay',\n",
       " ((3, 0), (4, 6)): 'left',\n",
       " ((10, 6), (4, 6)): 'up',\n",
       " ((10, 0), (4, 6)): 'up',\n",
       " ((4, 7), (4, 7)): 'stay',\n",
       " ((4, 1), (4, 7)): 'left',\n",
       " ((3, 7), (4, 7)): 'left',\n",
       " ((3, 1), (4, 7)): 'stay',\n",
       " ((10, 7), (4, 7)): 'up',\n",
       " ((10, 1), (4, 7)): 'up',\n",
       " ((4, 8), (4, 8)): 'left',\n",
       " ((4, 2), (4, 8)): 'stay',\n",
       " ((3, 8), (4, 8)): 'down',\n",
       " ((3, 2), (4, 8)): 'down',\n",
       " ((10, 8), (4, 8)): 'up',\n",
       " ((10, 2), (4, 8)): 'up',\n",
       " ((4, 9), (4, 9)): 'down',\n",
       " ((4, 3), (4, 9)): 'down',\n",
       " ((3, 9), (4, 9)): 'left',\n",
       " ((10, 9), (4, 9)): 'stay',\n",
       " ((10, 3), (4, 9)): 'stay',\n",
       " ((4, 10), (4, 10)): 'left',\n",
       " ((3, 10), (4, 10)): 'left',\n",
       " ((3, 4), (4, 10)): 'left',\n",
       " ((10, 4), (4, 10)): 'up',\n",
       " ((4, 11), (4, 11)): 'left',\n",
       " ((4, 5), (4, 11)): 'left',\n",
       " ((3, 11), (4, 11)): 'left',\n",
       " ((3, 5), (4, 11)): 'left',\n",
       " ((10, 11), (4, 11)): 'up',\n",
       " ((10, 5), (4, 11)): 'up',\n",
       " ((5, 1), (5, 0)): 'stay',\n",
       " ((5, 0), (5, 0)): 'left',\n",
       " ((5, 6), (5, 0)): 'left',\n",
       " ((4, 1), (5, 0)): 'left',\n",
       " ((4, 0), (5, 0)): 'stay',\n",
       " ((4, 6), (5, 0)): 'left',\n",
       " ((11, 1), (5, 0)): 'up',\n",
       " ((11, 0), (5, 0)): 'up',\n",
       " ((11, 6), (5, 0)): 'up',\n",
       " ((5, 2), (5, 1)): 'stay',\n",
       " ((5, 1), (5, 1)): 'stay',\n",
       " ((5, 7), (5, 1)): 'left',\n",
       " ((4, 2), (5, 1)): 'stay',\n",
       " ((4, 1), (5, 1)): 'left',\n",
       " ((4, 7), (5, 1)): 'stay',\n",
       " ((11, 2), (5, 1)): 'up',\n",
       " ((11, 1), (5, 1)): 'up',\n",
       " ((11, 7), (5, 1)): 'up',\n",
       " ((5, 3), (5, 2)): 'stay',\n",
       " ((5, 2), (5, 2)): 'stay',\n",
       " ((5, 8), (5, 2)): 'stay',\n",
       " ((4, 3), (5, 2)): 'down',\n",
       " ((4, 2), (5, 2)): 'stay',\n",
       " ((4, 8), (5, 2)): 'left',\n",
       " ((11, 3), (5, 2)): 'up',\n",
       " ((11, 2), (5, 2)): 'up',\n",
       " ((11, 8), (5, 2)): 'up',\n",
       " ((5, 4), (5, 3)): 'left',\n",
       " ((5, 3), (5, 3)): 'stay',\n",
       " ((5, 9), (5, 3)): 'stay',\n",
       " ((4, 3), (5, 3)): 'down',\n",
       " ((4, 9), (5, 3)): 'down',\n",
       " ((11, 4), (5, 3)): 'up',\n",
       " ((11, 3), (5, 3)): 'up',\n",
       " ((11, 9), (5, 3)): 'up',\n",
       " ((5, 4), (5, 4)): 'left',\n",
       " ((5, 10), (5, 4)): 'stay',\n",
       " ((4, 5), (5, 4)): 'left',\n",
       " ((4, 10), (5, 4)): 'left',\n",
       " ((11, 5), (5, 4)): 'up',\n",
       " ((11, 4), (5, 4)): 'up',\n",
       " ((11, 10), (5, 4)): 'up',\n",
       " ((5, 6), (5, 6)): 'left',\n",
       " ((5, 0), (5, 6)): 'left',\n",
       " ((4, 6), (5, 6)): 'stay',\n",
       " ((4, 0), (5, 6)): 'stay',\n",
       " ((11, 6), (5, 6)): 'up',\n",
       " ((11, 0), (5, 6)): 'up',\n",
       " ((5, 7), (5, 7)): 'left',\n",
       " ((5, 1), (5, 7)): 'stay',\n",
       " ((4, 7), (5, 7)): 'stay',\n",
       " ((4, 1), (5, 7)): 'left',\n",
       " ((11, 7), (5, 7)): 'up',\n",
       " ((11, 1), (5, 7)): 'up',\n",
       " ((5, 8), (5, 8)): 'stay',\n",
       " ((5, 2), (5, 8)): 'stay',\n",
       " ((4, 8), (5, 8)): 'left',\n",
       " ((4, 2), (5, 8)): 'stay',\n",
       " ((11, 8), (5, 8)): 'up',\n",
       " ((11, 2), (5, 8)): 'up',\n",
       " ((5, 9), (5, 9)): 'stay',\n",
       " ((5, 3), (5, 9)): 'stay',\n",
       " ((4, 9), (5, 9)): 'down',\n",
       " ((4, 3), (5, 9)): 'down',\n",
       " ((11, 9), (5, 9)): 'up',\n",
       " ((11, 3), (5, 9)): 'up',\n",
       " ((5, 10), (5, 10)): 'stay',\n",
       " ((5, 4), (5, 10)): 'left',\n",
       " ((4, 10), (5, 10)): 'left',\n",
       " ((11, 10), (5, 10)): 'up',\n",
       " ((11, 4), (5, 10)): 'up',\n",
       " ((5, 11), (5, 11)): 'left',\n",
       " ((4, 11), (5, 11)): 'left',\n",
       " ((4, 5), (5, 11)): 'left',\n",
       " ((11, 5), (5, 11)): 'up',\n",
       " ((6, 1), (6, 0)): 'up',\n",
       " ((6, 0), (6, 0)): 'up',\n",
       " ((6, 2), (6, 1)): 'up',\n",
       " ((6, 1), (6, 1)): 'up',\n",
       " ((6, 7), (6, 1)): 'up',\n",
       " ((6, 3), (6, 2)): 'up',\n",
       " ((6, 2), (6, 2)): 'up',\n",
       " ((6, 8), (6, 2)): 'up',\n",
       " ((6, 4), (6, 3)): 'up',\n",
       " ((6, 3), (6, 3)): 'up',\n",
       " ((6, 9), (6, 3)): 'up',\n",
       " ((6, 5), (6, 4)): 'up',\n",
       " ((6, 4), (6, 4)): 'up',\n",
       " ((6, 10), (6, 4)): 'up',\n",
       " ((6, 5), (6, 5)): 'up',\n",
       " ((6, 7), (6, 7)): 'up',\n",
       " ((6, 1), (6, 7)): 'up',\n",
       " ((6, 8), (6, 8)): 'up',\n",
       " ((6, 2), (6, 8)): 'up',\n",
       " ((6, 9), (6, 9)): 'up',\n",
       " ((6, 3), (6, 9)): 'up',\n",
       " ((6, 10), (6, 10)): 'up',\n",
       " ((6, 4), (6, 10)): 'up',\n",
       " ((6, 11), (6, 11)): 'up',\n",
       " ((6, 5), (6, 11)): 'up',\n",
       " ((1, 0), (7, 0)): 'down',\n",
       " ((1, 6), (7, 0)): 'down',\n",
       " ((7, 1), (7, 0)): 'up',\n",
       " ((7, 0), (7, 0)): 'stay',\n",
       " ((7, 6), (7, 0)): 'up',\n",
       " ((1, 2), (7, 1)): 'left',\n",
       " ((1, 7), (7, 1)): 'left',\n",
       " ((7, 2), (7, 1)): 'up',\n",
       " ((7, 1), (7, 1)): 'up',\n",
       " ((1, 3), (7, 2)): 'left',\n",
       " ((1, 2), (7, 2)): 'left',\n",
       " ((1, 8), (7, 2)): 'left',\n",
       " ((7, 3), (7, 2)): 'up',\n",
       " ((7, 2), (7, 2)): 'up',\n",
       " ((7, 8), (7, 2)): 'up',\n",
       " ((1, 4), (7, 3)): 'left',\n",
       " ((1, 3), (7, 3)): 'left',\n",
       " ((1, 9), (7, 3)): 'left',\n",
       " ((7, 4), (7, 3)): 'up',\n",
       " ((7, 3), (7, 3)): 'up',\n",
       " ((7, 9), (7, 3)): 'up',\n",
       " ((1, 5), (7, 4)): 'left',\n",
       " ((1, 4), (7, 4)): 'left',\n",
       " ((1, 10), (7, 4)): 'left',\n",
       " ((7, 5), (7, 4)): 'up',\n",
       " ((7, 4), (7, 4)): 'up',\n",
       " ((7, 10), (7, 4)): 'up',\n",
       " ((1, 5), (7, 5)): 'left',\n",
       " ((7, 5), (7, 5)): 'up',\n",
       " ((1, 6), (7, 6)): 'down',\n",
       " ((1, 0), (7, 6)): 'left',\n",
       " ((7, 6), (7, 6)): 'up',\n",
       " ((7, 0), (7, 6)): 'stay',\n",
       " ((1, 8), (7, 8)): 'left',\n",
       " ((1, 2), (7, 8)): 'left',\n",
       " ((7, 8), (7, 8)): 'up',\n",
       " ((7, 2), (7, 8)): 'up',\n",
       " ((1, 9), (7, 9)): 'left',\n",
       " ((1, 3), (7, 9)): 'left',\n",
       " ((7, 9), (7, 9)): 'up',\n",
       " ((7, 3), (7, 9)): 'up',\n",
       " ((1, 10), (7, 10)): 'left',\n",
       " ((1, 4), (7, 10)): 'left',\n",
       " ((7, 10), (7, 10)): 'up',\n",
       " ((7, 4), (7, 10)): 'up',\n",
       " ((1, 11), (7, 11)): 'left',\n",
       " ((1, 5), (7, 11)): 'left',\n",
       " ((7, 11), (7, 11)): 'up',\n",
       " ((7, 5), (7, 11)): 'up',\n",
       " ((2, 1), (8, 0)): 'down',\n",
       " ((2, 0), (8, 0)): 'down',\n",
       " ((2, 6), (8, 0)): 'down',\n",
       " ((8, 1), (8, 0)): 'stay',\n",
       " ((8, 0), (8, 0)): 'up',\n",
       " ((8, 6), (8, 0)): 'stay',\n",
       " ((2, 1), (8, 1)): 'down',\n",
       " ((2, 7), (8, 1)): 'stay',\n",
       " ((8, 2), (8, 1)): 'up',\n",
       " ((8, 1), (8, 1)): 'stay',\n",
       " ((8, 7), (8, 1)): 'up',\n",
       " ((2, 3), (8, 2)): 'left',\n",
       " ((2, 8), (8, 2)): 'left',\n",
       " ((8, 3), (8, 2)): 'up',\n",
       " ((8, 2), (8, 2)): 'up',\n",
       " ((2, 4), (8, 3)): 'left',\n",
       " ((2, 3), (8, 3)): 'left',\n",
       " ((2, 9), (8, 3)): 'left',\n",
       " ((8, 4), (8, 3)): 'up',\n",
       " ((8, 3), (8, 3)): 'up',\n",
       " ((8, 9), (8, 3)): 'up',\n",
       " ((2, 5), (8, 4)): 'left',\n",
       " ((2, 4), (8, 4)): 'left',\n",
       " ((2, 10), (8, 4)): 'left',\n",
       " ((8, 5), (8, 4)): 'up',\n",
       " ((8, 4), (8, 4)): 'up',\n",
       " ((8, 10), (8, 4)): 'up',\n",
       " ((2, 5), (8, 5)): 'left',\n",
       " ((8, 5), (8, 5)): 'up',\n",
       " ((2, 6), (8, 6)): 'down',\n",
       " ((2, 0), (8, 6)): 'stay',\n",
       " ((8, 6), (8, 6)): 'stay',\n",
       " ((8, 0), (8, 6)): 'stay',\n",
       " ((2, 7), (8, 7)): 'stay',\n",
       " ((2, 1), (8, 7)): 'left',\n",
       " ((8, 7), (8, 7)): 'up',\n",
       " ((8, 1), (8, 7)): 'stay',\n",
       " ((2, 9), (8, 9)): 'left',\n",
       " ((2, 3), (8, 9)): 'left',\n",
       " ((8, 9), (8, 9)): 'up',\n",
       " ((8, 3), (8, 9)): 'up',\n",
       " ((2, 10), (8, 10)): 'left',\n",
       " ((2, 4), (8, 10)): 'left',\n",
       " ((8, 10), (8, 10)): 'up',\n",
       " ((8, 4), (8, 10)): 'up',\n",
       " ((2, 11), (8, 11)): 'left',\n",
       " ((2, 5), (8, 11)): 'left',\n",
       " ((8, 11), (8, 11)): 'up',\n",
       " ((8, 5), (8, 11)): 'up',\n",
       " ((3, 1), (9, 0)): 'stay',\n",
       " ((3, 0), (9, 0)): 'left',\n",
       " ((3, 6), (9, 0)): 'stay',\n",
       " ((9, 1), (9, 0)): 'up',\n",
       " ((9, 0), (9, 0)): 'up',\n",
       " ((9, 6), (9, 0)): 'up',\n",
       " ((3, 2), (9, 1)): 'down',\n",
       " ((3, 1), (9, 1)): 'stay',\n",
       " ((3, 7), (9, 1)): 'left',\n",
       " ((9, 2), (9, 1)): 'stay',\n",
       " ((9, 1), (9, 1)): 'up',\n",
       " ((9, 7), (9, 1)): 'up',\n",
       " ((3, 2), (9, 2)): 'down',\n",
       " ((3, 8), (9, 2)): 'down',\n",
       " ((9, 3), (9, 2)): 'up',\n",
       " ((9, 2), (9, 2)): 'stay',\n",
       " ((9, 8), (9, 2)): 'stay',\n",
       " ((3, 4), (9, 3)): 'left',\n",
       " ((3, 9), (9, 3)): 'left',\n",
       " ((9, 4), (9, 3)): 'up',\n",
       " ((9, 3), (9, 3)): 'stay',\n",
       " ((3, 5), (9, 4)): 'left',\n",
       " ((3, 4), (9, 4)): 'left',\n",
       " ((3, 10), (9, 4)): 'left',\n",
       " ((9, 5), (9, 4)): 'up',\n",
       " ((9, 4), (9, 4)): 'up',\n",
       " ((9, 10), (9, 4)): 'up',\n",
       " ((3, 5), (9, 5)): 'left',\n",
       " ((9, 5), (9, 5)): 'up',\n",
       " ((3, 6), (9, 6)): 'stay',\n",
       " ((3, 0), (9, 6)): 'left',\n",
       " ((9, 6), (9, 6)): 'up',\n",
       " ((9, 0), (9, 6)): 'up',\n",
       " ((3, 7), (9, 7)): 'left',\n",
       " ((3, 1), (9, 7)): 'stay',\n",
       " ((9, 7), (9, 7)): 'up',\n",
       " ((9, 1), (9, 7)): 'up',\n",
       " ((3, 8), (9, 8)): 'down',\n",
       " ((3, 2), (9, 8)): 'down',\n",
       " ((9, 8), (9, 8)): 'stay',\n",
       " ((9, 2), (9, 8)): 'stay',\n",
       " ((3, 10), (9, 10)): 'left',\n",
       " ((3, 4), (9, 10)): 'left',\n",
       " ((9, 10), (9, 10)): 'up',\n",
       " ((9, 4), (9, 10)): 'up',\n",
       " ((3, 11), (9, 11)): 'left',\n",
       " ((3, 5), (9, 11)): 'left',\n",
       " ((9, 11), (9, 11)): 'up',\n",
       " ((9, 5), (9, 11)): 'up',\n",
       " ((4, 1), (10, 0)): 'left',\n",
       " ((4, 0), (10, 0)): 'stay',\n",
       " ((4, 6), (10, 0)): 'stay',\n",
       " ((10, 1), (10, 0)): 'up',\n",
       " ((10, 0), (10, 0)): 'up',\n",
       " ((10, 6), (10, 0)): 'up',\n",
       " ((4, 2), (10, 1)): 'stay',\n",
       " ((4, 1), (10, 1)): 'left',\n",
       " ((4, 7), (10, 1)): 'stay',\n",
       " ((10, 2), (10, 1)): 'up',\n",
       " ((10, 1), (10, 1)): 'up',\n",
       " ((10, 7), (10, 1)): 'up',\n",
       " ((4, 3), (10, 2)): 'left',\n",
       " ((4, 2), (10, 2)): 'stay',\n",
       " ((4, 8), (10, 2)): 'left',\n",
       " ((10, 3), (10, 2)): 'stay',\n",
       " ((10, 2), (10, 2)): 'up',\n",
       " ((10, 8), (10, 2)): 'up',\n",
       " ((4, 3), (10, 3)): 'down',\n",
       " ((4, 9), (10, 3)): 'down',\n",
       " ((10, 4), (10, 3)): 'up',\n",
       " ((10, 3), (10, 3)): 'stay',\n",
       " ((10, 9), (10, 3)): 'stay',\n",
       " ((4, 5), (10, 4)): 'left',\n",
       " ((4, 10), (10, 4)): 'left',\n",
       " ((10, 5), (10, 4)): 'up',\n",
       " ((10, 4), (10, 4)): 'up',\n",
       " ((4, 5), (10, 5)): 'left',\n",
       " ((10, 5), (10, 5)): 'up',\n",
       " ((4, 6), (10, 6)): 'stay',\n",
       " ((4, 0), (10, 6)): 'stay',\n",
       " ((10, 6), (10, 6)): 'up',\n",
       " ((10, 0), (10, 6)): 'up',\n",
       " ((4, 7), (10, 7)): 'stay',\n",
       " ((4, 1), (10, 7)): 'left',\n",
       " ((10, 7), (10, 7)): 'up',\n",
       " ((10, 1), (10, 7)): 'up',\n",
       " ((4, 8), (10, 8)): 'left',\n",
       " ((4, 2), (10, 8)): 'stay',\n",
       " ((10, 8), (10, 8)): 'up',\n",
       " ((10, 2), (10, 8)): 'up',\n",
       " ((4, 9), (10, 9)): 'down',\n",
       " ((4, 3), (10, 9)): 'down',\n",
       " ((10, 9), (10, 9)): 'stay',\n",
       " ((10, 3), (10, 9)): 'stay',\n",
       " ((4, 11), (10, 11)): 'left',\n",
       " ((4, 5), (10, 11)): 'left',\n",
       " ((10, 11), (10, 11)): 'up',\n",
       " ((10, 5), (10, 11)): 'up',\n",
       " ((5, 1), (11, 0)): 'stay',\n",
       " ((5, 0), (11, 0)): 'left',\n",
       " ((5, 6), (11, 0)): 'stay',\n",
       " ((11, 1), (11, 0)): 'up',\n",
       " ((11, 0), (11, 0)): 'up',\n",
       " ((11, 6), (11, 0)): 'up',\n",
       " ((5, 2), (11, 1)): 'stay',\n",
       " ((5, 1), (11, 1)): 'stay',\n",
       " ((5, 7), (11, 1)): 'left',\n",
       " ((11, 2), (11, 1)): 'up',\n",
       " ((11, 1), (11, 1)): 'up',\n",
       " ((11, 7), (11, 1)): 'up',\n",
       " ((5, 3), (11, 2)): 'stay',\n",
       " ((5, 2), (11, 2)): 'stay',\n",
       " ((5, 8), (11, 2)): 'stay',\n",
       " ((11, 3), (11, 2)): 'up',\n",
       " ((11, 2), (11, 2)): 'up',\n",
       " ((11, 8), (11, 2)): 'up',\n",
       " ((5, 4), (11, 3)): 'left',\n",
       " ((5, 3), (11, 3)): 'stay',\n",
       " ((5, 9), (11, 3)): 'stay',\n",
       " ((11, 4), (11, 3)): 'up',\n",
       " ((11, 3), (11, 3)): 'up',\n",
       " ((11, 9), (11, 3)): 'up',\n",
       " ((5, 4), (11, 4)): 'left',\n",
       " ((5, 10), (11, 4)): 'stay',\n",
       " ((11, 5), (11, 4)): 'up',\n",
       " ((11, 4), (11, 4)): 'up',\n",
       " ((11, 10), (11, 4)): 'up',\n",
       " ((11, 5), (11, 5)): 'up',\n",
       " ((5, 6), (11, 6)): 'left',\n",
       " ((5, 0), (11, 6)): 'left',\n",
       " ((11, 6), (11, 6)): 'up',\n",
       " ((11, 0), (11, 6)): 'up',\n",
       " ((5, 7), (11, 7)): 'left',\n",
       " ((5, 1), (11, 7)): 'stay',\n",
       " ((11, 7), (11, 7)): 'up',\n",
       " ((11, 1), (11, 7)): 'up',\n",
       " ((5, 8), (11, 8)): 'stay',\n",
       " ((5, 2), (11, 8)): 'stay',\n",
       " ((11, 8), (11, 8)): 'up',\n",
       " ((11, 2), (11, 8)): 'up',\n",
       " ((5, 9), (11, 9)): 'stay',\n",
       " ((5, 3), (11, 9)): 'stay',\n",
       " ((11, 9), (11, 9)): 'up',\n",
       " ((11, 3), (11, 9)): 'up',\n",
       " ((5, 10), (11, 10)): 'stay',\n",
       " ((5, 4), (11, 10)): 'left',\n",
       " ((11, 10), (11, 10)): 'up',\n",
       " ((11, 4), (11, 10)): 'up',\n",
       " ((5, 0), (-10, -10)): 'left'}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extend_policy(policy):\n",
    "    # take policy without velocity and convert it to policy with velocity\n",
    "    policy_with_velocity = {}\n",
    "    for ((i,j),(l,k)) in actions.keys():\n",
    "        policy_with_velocity[((i,j),(l,k))] = policy[(i,j)]\n",
    "    return policy_with_velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "careful_eval_v2 = policy_eval2(extend_policy(careful_policy))\n",
    "random_eval_v2 = policy_eval2(extend_policy(random_policy))\n",
    "aggressive_eval_v2 = policy_eval2(extend_policy(aggressive_policy))\n",
    "semiAggressive_eval_v2 = policy_eval2(extend_policy(semiAggressive_policy))\n",
    "semiCareful_eval_v2 = policy_eval2(extend_policy(semiCareful_policy))\n",
    "valueIteration_eval_v2 = policy_eval2(extend_policy(valueIteration_policy))\n",
    "# [((5,0),(-10,-10))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Real</th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Prediction with velocity</th>\n",
       "      <th>different</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavior</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aggressiveBehavior</th>\n",
       "      <td>-16.28</td>\n",
       "      <td>1.14</td>\n",
       "      <td>-6.21</td>\n",
       "      <td>10.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>randomBehavior</th>\n",
       "      <td>-72.29</td>\n",
       "      <td>-112.39</td>\n",
       "      <td>-119.67</td>\n",
       "      <td>47.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semiAggressiveBehavior</th>\n",
       "      <td>-61.91</td>\n",
       "      <td>-47.81</td>\n",
       "      <td>-56.47</td>\n",
       "      <td>5.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>semiCarefulBehavior</th>\n",
       "      <td>-4.86</td>\n",
       "      <td>-4.21</td>\n",
       "      <td>-14.41</td>\n",
       "      <td>9.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valueIterationBasedBehavior_v2</th>\n",
       "      <td>-6.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.74</td>\n",
       "      <td>21.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Real  Prediction  Prediction with velocity  \\\n",
       "behavior                                                                      \n",
       "aggressiveBehavior             -16.28        1.14                     -6.21   \n",
       "randomBehavior                 -72.29     -112.39                   -119.67   \n",
       "semiAggressiveBehavior         -61.91      -47.81                    -56.47   \n",
       "semiCarefulBehavior             -4.86       -4.21                    -14.41   \n",
       "valueIterationBasedBehavior_v2  -6.00         NaN                     15.74   \n",
       "\n",
       "                                different  \n",
       "behavior                                   \n",
       "aggressiveBehavior                  10.07  \n",
       "randomBehavior                      47.38  \n",
       "semiAggressiveBehavior               5.44  \n",
       "semiCarefulBehavior                  9.55  \n",
       "valueIterationBasedBehavior_v2      21.74  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_col = df.groupby(['behavior']).mean()['blueScore'].drop('carefulBehavior')\n",
    "diff = pd.DataFrame({'Real': real_col,\n",
    "                      'Prediction': [aggressive_intensity[5][0], randomBehavior_intensity[5][0], semiAggressive_intensity[5][0], semiCareful_intensity[5][0], np.NaN],\n",
    "                     'Prediction with velocity': [aggressive_eval_v2[((5,0),(-10,-10))], random_eval_v2[((5,0),(-10,-10))], semiAggressive_eval_v2[((5,0),(-10,-10))], semiCareful_eval_v2[((5,0),(-10,-10))], optimal_V_v2['a6 a1,a-9 a-9']]},\n",
    "                     index= real_col.index)\n",
    "diff['different'] = abs(diff['Real']-diff['Prediction with velocity'])\n",
    "diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.736324082225005"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V[((5,0),(-10,-10))] # this is from the value iteration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# push to firebase:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyrebase\n",
    "\n",
    "firebaseConfig = {\n",
    "    \"apiKey\": \"AIzaSyDROtt5r3t5VL_iPabcgVgsWmUZHMw7Pv8\",\n",
    "    \"authDomain\": \"road-game.firebaseapp.com\",\n",
    "    \"databaseURL\": \"https://road-game-default-rtdb.firebaseio.com\",\n",
    "    \"projectId\": \"road-game\",\n",
    "    \"storageBucket\": \"road-game.appspot.com\",\n",
    "    \"messagingSenderId\": \"14363967441\",\n",
    "    \"appId\": \"1:14363967441:web:9efd1c7c3db93ab12ceaf0\"\n",
    "  }\n",
    "\n",
    "firebase=pyrebase.initialize_app(firebaseConfig)\n",
    "\n",
    "db=firebase.database()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Errno 401 Client Error: Unauthorized for url: https://road-game-default-rtdb.firebaseio.com/aggressiveBehavior.json] {\n",
      "  \"error\" : \"Permission denied\"\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    db.child(\"aggressiveBehavior\").set(convertStates(extend_policy(aggressive_policy))[0])\n",
    "    \n",
    "    db.child(\"carefulBehavior\").set(convertStates(extend_policy(careful_policy))[0])\n",
    "    \n",
    "    db.child(\"semiCarefulBehavior\").set(convertStates(extend_policy(semiCareful_policy))[0])\n",
    "    \n",
    "    db.child(\"randomBehavior\").set(convertStates(extend_policy(random_policy))[0])\n",
    "    \n",
    "    db.child(\"semiAggressiveBehavior\").set(convertStates(extend_policy(semiAggressive_policy))[0])\n",
    "    \n",
    "    db.child(\"valueIterationBasedBehavior_v2\").set(optimal_policy_v2)\n",
    "    \n",
    "    db.child(\"valueIterationBehavior\").set(convertStates(extend_policy(valueIteration_policy))[0])\n",
    "except Exception as err:\n",
    "    print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extend_policy(valueIteration_policy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "build opponent model:\n",
    "it uses the human model 50% of the time, and uses our mirrored model 50% of the time.\n",
    "\n",
    "Now find the maximum.\n",
    "I.e., find the optimal policy.\n",
    "Once you find a policy update the opponent model and find the optimal policy again. Hopefully it will converge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nextStateDistibution3(s, a, prev_action_at_prev_policy):\n",
    "    # prev_action_at_prev_policy = what was the blue agent action at state\n",
    "    # given state s and action a, return distibution of possibles new states by probs\n",
    "    nxt = {}\n",
    "#     print('s: ', s)\n",
    "#     print('a: ', a)\n",
    "    bluePossition = newPosGivenAction(s[0], a, 0)\n",
    "#     print('bluePossition: ', bluePossition)\n",
    "    \n",
    "    if(prev_action_at_prev_policy == \"left\"):\n",
    "        a_to_compare = \"right\"\n",
    "    else:\n",
    "        a_to_compare = prev_action_at_prev_policy\n",
    "#     print('a_to_compare: ', a_to_compare)\n",
    "    # red possible action given state\n",
    "    if s[0][1] == 5:\n",
    "        actions=[\"stay\"]\n",
    "    elif s[0][1] < 6:\n",
    "        actions=[\"right\",\"stay\", \"down\"]\n",
    "    else:\n",
    "        actions=[\"stay\", \"up\"]\n",
    "#     print('actions: ', actions)\n",
    "    for r_a in actions:\n",
    "        redPossition = newPosGivenAction(s[0], r_a, 1)\n",
    "#         print('r_a: ', r_a)\n",
    "#         print('redPossition: ', redPossition)\n",
    "        newState = ((bluePossition, redPossition),s[0])\n",
    "#         print('newState: ', newState)\n",
    "        try:\n",
    "            nxt[newState] = probs[adapt_state(s[1]) + ' nxt: ' + adapt_state(s[0])][r_a] * 0.75\n",
    "            if(r_a == a_to_compare):\n",
    "                nxt[newState] = nxt[newState] + 0.25 \n",
    "        except KeyError:\n",
    "#             print(\"Warning! State not found: assume random behavior. \", s[0])\n",
    "            nxt[newState] = randomProbForAction(s[0]) * 0.75\n",
    "            if(r_a == a_to_compare):\n",
    "                nxt[newState] = nxt[newState] + 0.25\n",
    "    assert abs(sum(nxt.values()) - 1.0) < 0.0001, \"probs not sum to 1.0. \\nstate: \" + str(s) + \" action: \" + str(a) + \" nxt: \" + str(nxt) + \"\\nsum of probs: \" + str(sum(nxt.values()))\n",
    "    return nxt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{((1, 1), (1, 0)): 0.16666666665000002,\n",
       " ((1, 0), (1, 0)): 0.750000000025,\n",
       " ((1, 6), (1, 0)): 0.08333333332500001}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = ((1, 0), (1, 0))\n",
    "a = \"stay\"\n",
    "nextStateDistibution3(s, a, \"stay\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mirror_dict_pos = {0:5,\n",
    "                  1:4,\n",
    "                  2:3,\n",
    "                  3:2,\n",
    "                  4:1,\n",
    "                  5:0,\n",
    "                  6:11,\n",
    "                  7:10,\n",
    "                  8:9,\n",
    "                  9:8,\n",
    "                  10:7,\n",
    "                  11:6,\n",
    "                  -10:-10}\n",
    "\n",
    "def mirror_pos(pos):\n",
    "    return mirror_dict_pos[pos]\n",
    "\n",
    "mirror_pos(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mirror_state(s):\n",
    "    ((i,j),(l,k)) = s\n",
    "    return ((mirror_pos(j), mirror_pos(i)),(mirror_pos(k), mirror_pos(l)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0, 4), (1, 4))"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mirror_state(((1, 5), (1, 4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prev_policy = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# while(prev_policy != Policy):\n",
    "#     prev_policy = copy.deepcopy(Policy)\n",
    "\n",
    "#     '''==================================================\n",
    "#     Value Iteration\n",
    "#     =================================================='''\n",
    "#     iteration = 1\n",
    "#     while True:\n",
    "#         biggest_change = 0\n",
    "#         for s in all_states:            \n",
    "#             if s in Policy:\n",
    "\n",
    "#                 old_v = V[s]\n",
    "#                 new_v = np.NINF\n",
    "\n",
    "#                 for a in actions[s]:\n",
    "#                     try:\n",
    "#                         blue_action_as_red = prev_policy[mirror_state(s)]\n",
    "#                     except KeyError: # it can fail only if blue in end-state\n",
    "#                         blue_action_as_red = \"stay\"\n",
    "\n",
    "#                     nxt = nextStateDistibution3(s, a, blue_action_as_red)\n",
    "#                     v=0\n",
    "#                     #Calculate the value\n",
    "#                     for next_state, prob in nxt.items():\n",
    "#                         assert rewards[s] == -1, \"rewards is not -1\"\n",
    "#                         v += prob * (rewards[s] + GAMMA * V[next_state])\n",
    "#                     if v > new_v: #Is this the best action so far? If so, keep it\n",
    "#                         new_v = v\n",
    "#                         Policy[s] = a\n",
    "\n",
    "#                #Save the best of all actions for the state                                \n",
    "#                 V[s] = new_v\n",
    "#                 biggest_change = max(biggest_change, np.abs(old_v - V[s]))\n",
    "\n",
    "\n",
    "#        #See if the loop should stop now         \n",
    "#         if biggest_change < SMALL_ENOUGH:\n",
    "#             print('converge after: ' + str(iteration) + ' iterations')\n",
    "#             break\n",
    "#         iteration += 1\n",
    "\n",
    "#         #Print every 100 iterations\n",
    "#         if(iteration % 100 == 0):\n",
    "#             print(iteration, \"iterations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# policy_eval2(Policy)[((5,0),(-10,-10))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def humanBehavior():\n",
    "#     # Behavior based on the data we gather\n",
    "#     Policy={}\n",
    "#     for s in actions.keys():\n",
    "#         try:\n",
    "#             row = probs[adapt_state(s[1]) + ' nxt: ' + adapt_state(s[0])]\n",
    "#             Policy[s] = np.random.choice(list(row.keys()), p=list(row.values()))\n",
    "#         except KeyError:\n",
    "# #             print(\"Warning! State not found: assume random behavior. \", s[0])\n",
    "#             Policy[s] = np.random.choice(actions[s])\n",
    "#         # take the action with higher prob\n",
    "#     return Policy\n",
    "\n",
    "# human_policy = humanBehavior()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# class Opponent_model:\n",
    "\n",
    "#     def __init__(self, human_policy, mirrored_policy, p):\n",
    "#         # humen_policy is made from the data\n",
    "#         # mirrored_policy is the old policy of Value Iteration based agent.\n",
    "#         self.human_policy = human_policy\n",
    "#         self.mirrored_policy = mirrored_policy\n",
    "#         self.p = p\n",
    "\n",
    "#     def updateMirroredPolicy(self, new_mirrored_policy):\n",
    "#         self.mirrored_policy = new_mirrored_policy\n",
    "        \n",
    "#     def getAction(self, state):\n",
    "#         human_action = self.human_policy[state]\n",
    "#         mirrored_action = self.mirrored_policy[state]\n",
    "#         return np.random.choice([human_action, mirrored_action], p=[self.p, 1-self.p])\n",
    "\n",
    "#     def __str__(self):\n",
    "#         return \"human_policy:\\n \" + str(self.human_policy) + \"\\n\" + str(self.mirrored_policy) + \"\\n p: \" + str(self.p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# V[((5,0),(-10,-10))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Policy[((5,0),(-10,-10))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# optimal_policy_v3, optimal_V_v3 = convertStates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try:\n",
    "#     db.child(\"valueIterationBasedBehavior_v3\").set(optimal_policy_v3)\n",
    "# except Exception as err:\n",
    "#     print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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