{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Deep Otello AI\n",
    "\n",
    "The game reversi is a very good game to apply deep learning methods to.\n",
    "\n",
    "Othello also known as reversi is a board game first published in 1883 by eiter Lewis Waterman or John W. Mollet in England (each one was denouncing the other as fraud).\n",
    "It is a strickt turn based zero-sum game with a clear Markov chain and now hidden states like in card games with an unknown distribution of cards or unknown player allegiance.\n",
    "There is like for the game go only one set of stones with two colors which is much easier to abstract than chess with its 6 unique pieces.\n",
    "The game has a symmetrical game board wich allows to play with rotating the state around an axis to allow for a breaking of sequences or interesting ANN architectures, quadruple the data generation by simulation or interesting test cases where a symetry in turns should be observable if the AI reaches an \"objective\" policy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Content\n",
    "\n",
    "* [The game rules](#the-game-rules) A short overview over the rules of the game.\n",
    "* [Some common Otello strategies](#some-common-otello-strategies) introduces some easy approaches to a classic Otello AI and defines some behavioral expectations.\n",
    "* [Initial design decisions](#initial-design-decisions) an explanation about some initial design decision and assumptions\n",
    "* [Imports and dependencies](#imports-and-dependencies) explains what libraries where used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## The game rules\n",
    "\n",
    "Othello is played on a board with 8 x 8 fields for two player.\n",
    "The board geometry is equal to a chess game.\n",
    "The game is played with game stones that are black on one siede and white on the other.\n",
    "![Othello game board example](reversi_example.png)\n",
    "The player take turns.\n",
    "A player places a stone with his or her color up on the game board.\n",
    "The player can only place stones when he surrounds a number of stones with the opponents color with the new stone and already placed stones of his color.\n",
    "Those surrounded stones can either be horizontally, vertically and/or diagonally be placed.\n",
    "All stones thus surrounded will be flipped to be of the players color.\n",
    "Turns are only possible if the player is also changing the color of the opponents stones. If a player can't act he is skipped.\n",
    "The game ends if both players can't act. The player with the most stones wins.\n",
    "If the score is counted in detail unclaimed fields go to the player with more stones of his or her color on the board.\n",
    "The game begins with four stones places in the center of the game. Each player gets two. They are placed diagonally to each other.\n",
    "\n",
    "\n",
    "<img alt=\"Startaufstellung.png\" src=\"Startaufstellung.png\"/>\n",
    "\n",
    "## Some common Othello strategies\n",
    "\n",
    "As can be easily understood the placement of stones and on the bord is always a careful balance of attack and defence.\n",
    "If the player occupies huge homogenous stretches on the board it can be attacked easier.\n",
    "The boards corners provide safety from wich occupied territory is impossible to loos but since it is only possible to reach the corners if the enemy is forced to allow this or calculates the cost of giving a stable base to the enemy it is difficult to obtain.\n",
    "There are some text on otello computer strategies which implement greedy algorithms for reversi based on a modified score to each field.\n",
    "Those different values are score modifiers for a traditional greedy algorithm.\n",
    "If a players stone has captured such a filed the score reached is multiplied by the modifier.\n",
    "The total score is the score reached by the player subtracted with the score of the enemy.\n",
    "The scores change in the course of the game and converges against one. This gives some indications of what to expect from an Othello AI.\n",
    "\n",
    "<img alt=\"ComputerPossitionScore\" src=\"computer-score.png\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial design decisions\n",
    "\n",
    "At the beginning of this project I made some design decisions.\n",
    "The first onw was that I do not want to use a gym library because it limits the data formats accessible.\n",
    "I choose to implement the hole game as entry in a stack in numpy arrays to be able to accommodate interfacing with a neural network easier and to use scipy pattern recognition tools to implement some game mechanics for a fast simulation cycle.\n",
    "I chose to ignore player colors as far as I could instead a player perspective was used. Which allowed to change the perspective with a flipping of the sign. (multiplying with -1).\n",
    "The array format should also allow for data multiplication or the breaking of strikt sequences by flipping the game along one the for axis, (horizontal, vertical, transpose along both diagonals).\n",
    "\n",
    "I wanted to implement different agents as classes that act on those game stacks.\n",
    "\n",
    "Since computation time is critical all computational have results are saved.\n",
    "The analysis of those is then repeated in real time. If a recalculation of such a section is required the save file can be deleted and the code should be executed again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import os.path\n",
    "import warnings\n",
    "\n",
    "\n",
    "%load_ext blackcellmagic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and dependencies\n",
    "\n",
    "The following direct dependencies where used for this project:\n",
    "```toml\n",
    "jupyter = \"^1.0.0\"\n",
    "matplotlib = \"^3.6.3\"\n",
    "numpy = \"^1.24.1\"\n",
    "pytest = \"^7.2.1\"\n",
    "python = \"3.10.*\"\n",
    "scipy = \"^1.10.0\"\n",
    "tqdm = \"^4.64.1\"\n",
    "jupyterlab = \"^3.6.1\"\n",
    "torchvision = \"^0.14.1\"\n",
    "torchaudio = \"^0.13.1\"\n",
    "```\n",
    "* `Jupyter` and `jupyterlab` on pycharm was used as a IDE / Ipython was used to implement this code.\n",
    "* `matplotlib` was used for visualisation and statistics.\n",
    "* `numpy` was used for array support and mathematical functions\n",
    "* `tqdm` was used for progress bars\n",
    "* `scipy` contains fast pattern recognition tools for images. It was used to make an initial estimation about where possible turns should be.\n",
    "* `torch` supplied the ANN functionalities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import abc\n",
    "from typing import Final\n",
    "from scipy.ndimage import binary_dilation\n",
    "from abc import ABC\n",
    "from tqdm.notebook import tqdm\n",
    "from ipywidgets import interact\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants\n",
    "\n",
    "Some general constants needed to be defined. Such as board game size and Player and Enemy representations. Also, directional offsets and the initial placement of blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "BOARD_SIZE: Final[int] = 8  # defines the board side length as 8\n",
    "PLAYER: Final[int] = 1  # defines the number symbolising the player as 1\n",
    "ENEMY: Final[int] = -1  # defines the number symbolising the enemy as -1\n",
    "EXAMPLE_STACK_SIZE: Final[int] = 1000  # defines the game stack size for examples\n",
    "IMPOSSIBLE: Final[np.ndarray] = np.array([-1, -1], dtype=int)\n",
    "IMPOSSIBLE.setflags(write=False)\n",
    "SIMULATE_TURNS: Final[int] = 70\n",
    "VERIFY_POLICY: Final[bool] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The directions array contains all the numerical offsets needed to move along one of the 8 directions in a 2 dimensional grid. This will allow an iteration over the game board.\n",
    "![8-directions.png](8-directions.png \"Offset in 8 directions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1, -1],\n",
       "       [-1,  0],\n",
       "       [-1,  1],\n",
       "       [ 0, -1],\n",
       "       [ 0,  1],\n",
       "       [ 1, -1],\n",
       "       [ 1,  0],\n",
       "       [ 1,  1]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DIRECTIONS: Final[np.ndarray] = np.array(\n",
    "    [[i, j] for i in range(-1, 2) for j in range(-1, 2) if j != 0 or i != 0],\n",
    "    dtype=int,\n",
    ")\n",
    "DIRECTIONS.setflags(write=False)\n",
    "DIRECTIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another constant needed is the initial start square at the center of the board."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1,  1],\n",
       "       [ 1, -1]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "START_SQUARE: Final[np.ndarray] = np.array(\n",
    "    [[ENEMY, PLAYER], [PLAYER, ENEMY]], dtype=int\n",
    ")\n",
    "START_SQUARE.setflags(write=False)\n",
    "START_SQUARE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new boards\n",
    "\n",
    "The first function implemented and tested is a function to generate the starting environment as a stack of games.\n",
    "As described above I simply placed a 2 by 2 square in the center of an empty stack of boards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, -1,  1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1, -1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_new_games(number_of_games: int) -> np.ndarray:\n",
    "    \"\"\"Generates a stack of initialised game boards.\n",
    "\n",
    "    Args:\n",
    "        number_of_games: The size of the board stack.\n",
    "\n",
    "    Returns: The generates stack of games as a stack n x 8 x 8.\n",
    "\n",
    "    \"\"\"\n",
    "    empty = np.zeros([number_of_games, BOARD_SIZE, BOARD_SIZE], dtype=int)\n",
    "    empty[:, 3:5, 3:5] = START_SQUARE\n",
    "    return empty\n",
    "\n",
    "\n",
    "get_new_games(1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_number_of_games = 3\n",
    "assert get_new_games(test_number_of_games).shape == (\n",
    "    test_number_of_games,\n",
    "    BOARD_SIZE,\n",
    "    BOARD_SIZE,\n",
    ")\n",
    "np.testing.assert_equal(\n",
    "    get_new_games(test_number_of_games).sum(axis=1),\n",
    "    np.zeros(\n",
    "        [\n",
    "            test_number_of_games,\n",
    "            8,\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "np.testing.assert_equal(\n",
    "    get_new_games(test_number_of_games).sum(axis=2),\n",
    "    np.zeros(\n",
    "        [\n",
    "            test_number_of_games,\n",
    "            8,\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "assert np.all(get_new_games(test_number_of_games)[:, 3:4, 3:4] != 0)\n",
    "del test_number_of_games"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation tools\n",
    "\n",
    "In this section a visualisation help was implemented for debugging of the game and a proper display of the results.\n",
    "For this visualisation ChatGPT was used as a prompted code generator that was later reviewed and refactored by hand to integrate seamlessly into the project as a whole.\n",
    "White stones represent the player, black stones the enemy. A single plot can be used as a subplot when the `ax` argument is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_othello_board(board: np.ndarray, ax=None) -> None:\n",
    "    \"\"\"Plots a single otello board.\n",
    "\n",
    "    If a matplot axis object is given the board will be plotted into that axis. If not an axis object will be generated.\n",
    "    The image generated will be shown directly.\n",
    "\n",
    "    Args:\n",
    "        board: The bord that should be plotted. Only a single games is allowed. A numpy array of the form 8x8 is expected.\n",
    "        ax: If needed a matplotlib axis object can be defined that is used to place the board as a sublot into a bigger context.\n",
    "    \"\"\"\n",
    "    assert board.shape == (8, 8)\n",
    "    plot_all = False\n",
    "    if ax is None:\n",
    "        fig_size = 3\n",
    "        plot_all = True\n",
    "        fig, ax = plt.subplots(figsize=(fig_size, fig_size))\n",
    "\n",
    "    ax.set_facecolor(\"#66FF00\")\n",
    "    for x_pos, y_pos in itertools.product(range(BOARD_SIZE), range(BOARD_SIZE)):\n",
    "        if board[x_pos, y_pos] == -1:\n",
    "            color = \"white\"\n",
    "        elif board[x_pos, y_pos] == 1:\n",
    "            color = \"black\"\n",
    "        else:\n",
    "            continue\n",
    "        ax.scatter(y_pos, x_pos, s=300 if plot_all else 150, c=color)\n",
    "    for x_pos in range(-1, 8):\n",
    "        ax.axhline(x_pos + 0.5, color=\"black\", lw=2)\n",
    "        ax.axvline(x_pos + 0.5, color=\"black\", lw=2)\n",
    "    ax.set_xlim(-0.5, 7.5)\n",
    "    ax.set_ylim(7.5, -0.5)\n",
    "    ax.set_xticks(np.arange(8))\n",
    "    ax.set_xticklabels(list(\"ABCDEFGH\"))\n",
    "    ax.set_yticks(np.arange(8))\n",
    "    ax.set_yticklabels(list(\"12345678\"))\n",
    "    if plot_all:\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "plot_othello_board(get_new_games(1)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_othello_boards(boards: np.ndarray) -> None:\n",
    "    \"\"\"Plots multiple boards into subplots.\n",
    "\n",
    "    The plots are shown directly.\n",
    "\n",
    "    Args:\n",
    "        boards: Plots the boards given into subplots. The maximum number of boards accepted is 70.\n",
    "    \"\"\"\n",
    "    assert len(boards.shape) == 3\n",
    "    assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n",
    "    assert boards.shape[0] < 70\n",
    "\n",
    "    plots_per_row = 4\n",
    "    rows = int(np.ceil(boards.shape[0] / plots_per_row))\n",
    "    fig, axs = plt.subplots(rows, plots_per_row, figsize=(12, 3 * rows))\n",
    "    for game_index, ax in enumerate(axs.flatten()):\n",
    "        if game_index >= boards.shape[0]:\n",
    "            fig.delaxes(ax)\n",
    "        else:\n",
    "            plot_othello_board(boards[game_index], ax)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_duplicate_boards(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Drop boards that follow each other and are duplicates will be dropped.\n",
    "\n",
    "    Args:\n",
    "        boards: A set of boards to be reduced.\n",
    "\n",
    "    Returns:\n",
    "        A sequence of boards where boards that where equal are dropped.\n",
    "    \"\"\"\n",
    "    return boards[~np.all(boards == np.roll(boards, axis=0, shift=1), axis=(1, 2))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find possible actions to take\n",
    "\n",
    "The frist step in the implementation of an AI like this is to get an overview over the possible actions that can be taken in a situation.\n",
    "Here was the design choice taken to first find fields that are empty and have at least one neighbouring enemy stone.\n",
    "This was implemented with element wise check for a stone and a binary dilation marking all fields neighboring an enemy stone.\n",
    "For that the `SURROUNDING` mask was used. Both aries are then element wise combined using and.\n",
    "The resulting array contains all filed where a turn could potentially be made. Those are then check in detail.\n",
    "The previous element wise operations on the numpy array increase the spead for this operation dramatically.\n",
    "\n",
    "The check for a possible turn is done in detail by following each direction step by step as long as there are enemy stones in that direction.\n",
    "If the board end is reached or en empty filed before reaching a field occupied by the player that direction does not surround enemy stones.\n",
    "If one direction surrounds enemy stone a turn is possible.\n",
    "This detailed step is implemented as a recursion and need to go at leas one step to return True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 1],\n",
       "        [1, 0, 1],\n",
       "        [1, 1, 1]]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SURROUNDING: Final = np.array(\n",
    "    [[[1, 1, 1], [1, 0, 1], [1, 1, 1]]]\n",
    ")  # defines the binary dilation mask to check if a field is next to an enemy stones\n",
    "SURROUNDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.86 ms ± 584 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "860 ms ± 12.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[False, False, False, False, False, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False],\n",
       "        [False, False, False,  True, False, False, False, False],\n",
       "        [False, False,  True, False, False, False, False, False],\n",
       "        [False, False, False, False, False,  True, False, False],\n",
       "        [False, False, False, False,  True, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False],\n",
       "        [False, False, False, False, False, False, False, False]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _recursive_steps(\n",
    "    board: np.ndarray,\n",
    "    rec_direction: np.ndarray,\n",
    "    rec_position: np.ndarray,\n",
    "    step_one: int = 0,\n",
    ") -> int:\n",
    "    \"\"\"Check if a player can place a stone on the board specified in the direction specified and direction specified.\n",
    "\n",
    "    Args:\n",
    "        board: The board that should be checked for a playable action.\n",
    "        rec_direction: The direction that should be checked.\n",
    "        rec_position: The position that should be checked.\n",
    "        step_one: Defines if the call of this function is the firs or not. Should be kept to the default value for proper functionality.\n",
    "\n",
    "    Returns:\n",
    "        True if a turn is possible for possition and direction on the board defined.\n",
    "    \"\"\"\n",
    "    rec_position = rec_position + rec_direction\n",
    "    if np.any((rec_position >= BOARD_SIZE) | (rec_position < 0)):\n",
    "        return 0\n",
    "    next_field = board[tuple(rec_position.tolist())]\n",
    "    if next_field == 0:\n",
    "        return 0\n",
    "    if next_field == -1:\n",
    "        return _recursive_steps(\n",
    "            board, rec_direction, rec_position, step_one=step_one + 1\n",
    "        )\n",
    "    if next_field == 1:\n",
    "        return step_one\n",
    "\n",
    "\n",
    "def get_possible_turns(boards: np.ndarray, tqdm_on: bool = False) -> np.ndarray:\n",
    "    \"\"\"Analyses a stack of boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards to check.\n",
    "\n",
    "    Returns:\n",
    "        A stack of game boards containing boolean values showing where turns are possible for the player.\n",
    "    \"\"\"\n",
    "    assert len(boards.shape) == 3, \"The number fo input dimensions does not fit.\"\n",
    "    assert boards.shape[1:] == (\n",
    "        BOARD_SIZE,\n",
    "        BOARD_SIZE,\n",
    "    ), \"The input dimensions do not fit.\"\n",
    "\n",
    "    _poss_turns = boards == 0  # checks where fields are empty.\n",
    "    _poss_turns &= binary_dilation(\n",
    "        boards == -1, SURROUNDING\n",
    "    )  # checks where fields are next to an enemy filed an empty\n",
    "    iterate_over = itertools.product(\n",
    "        range(boards.shape[0]), range(BOARD_SIZE), range(BOARD_SIZE)\n",
    "    )\n",
    "    if tqdm_on:\n",
    "        iterate_over = tqdm(iterate_over, total=np.prod(boards.shape))\n",
    "    for game, idx, idy in iterate_over:\n",
    "        if _poss_turns[game, idx, idy]:\n",
    "            position = idx, idy\n",
    "            _poss_turns[game, idx, idy] = any(\n",
    "                _recursive_steps(boards[game, :, :], direction, position) > 0\n",
    "                for direction in DIRECTIONS\n",
    "            )\n",
    "    return _poss_turns\n",
    "\n",
    "\n",
    "# some simple testing to ensure the function works after simple changes\n",
    "# this testing is complete, its more of a smoke-test\n",
    "test_array = get_new_games(3)\n",
    "expected_result = np.zeros_like(test_array, dtype=bool)\n",
    "expected_result[:, 4, 5] = expected_result[:, 2, 3] = True\n",
    "expected_result[:, 5, 4] = expected_result[:, 3, 2] = True\n",
    "np.testing.assert_equal(get_possible_turns(test_array), expected_result)\n",
    "\n",
    "\n",
    "%timeit get_possible_turns(get_new_games(10))  # checks turn possibility evaluation time for 10 initial games\n",
    "%timeit get_possible_turns(get_new_games(EXAMPLE_STACK_SIZE))  # check turn possibility evaluation time for EXAMPLE_STACK_SIZE initial games\n",
    "\n",
    "# shows a singe game\n",
    "get_possible_turns(get_new_games(3))[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides the ability to generate an array of possible turns there needs to be a functions that check if a given turn is possible.\n",
    "On is needed for the action space validation. The other is for validating a players turn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def move_possible(board: np.ndarray, move: np.ndarray) -> bool:\n",
    "    \"\"\"Checks if a turn is possible.\n",
    "\n",
    "    Checks if a turn is possible. If no turn is possible to input array [-1, -1] is expected.\n",
    "\n",
    "    Args:\n",
    "        board: A board where it should be checkt if a turn is possible.\n",
    "        move: The move that should be taken. Expected is the index of the filed where a stone should be placed [x, y]. If no placement is possible [-1, -1] is expected as an input.\n",
    "\n",
    "    Returns:\n",
    "        True if the move is possible\n",
    "    \"\"\"\n",
    "    if np.all(move == -1):\n",
    "        return not np.any(get_possible_turns(np.reshape(board, (1, 8, 8))))\n",
    "    return any(\n",
    "        _recursive_steps(board[:, :], direction, move) > 0 for direction in DIRECTIONS\n",
    "    )\n",
    "\n",
    "\n",
    "# Some testing for this function and the underlying recursive functions that are called.\n",
    "assert move_possible(get_new_games(1)[0], np.array([2, 3])) is True\n",
    "assert move_possible(get_new_games(1)[0], np.array([3, 2])) is True\n",
    "assert move_possible(get_new_games(1)[0], np.array([2, 2])) is False\n",
    "assert move_possible(np.zeros((8, 8)), np.array([3, 2])) is False\n",
    "assert move_possible(np.ones((8, 8)) * 1, np.array([-1, -1])) is True\n",
    "assert move_possible(np.ones((8, 8)) * -1, np.array([-1, -1])) is True\n",
    "assert move_possible(np.ones((8, 8)) * 0, np.array([-1, -1])) is True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moves_possible(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Checks if a stack of moves can be executed on a stack of boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A board where the next stone should be placed.\n",
    "        moves: A stack stones to be placed. Each move is formatted as an array in the form of [x, y] if no turn is possible the value [-1, -1] is expected.\n",
    "\n",
    "    Returns:\n",
    "        An array marking for each and every game and move in the stack if the move can be executed.\n",
    "    \"\"\"\n",
    "    arr_moves_possible = np.zeros(boards.shape[0], dtype=bool)\n",
    "    for game in range(boards.shape[0]):\n",
    "        if np.all(\n",
    "            moves[game] == -1\n",
    "        ):  # can be all or any. All should be faster since most times neither value will be -1.\n",
    "            arr_moves_possible[game] = not np.any(\n",
    "                get_possible_turns(np.reshape(boards[game], (1, 8, 8)))\n",
    "            )\n",
    "        else:\n",
    "            arr_moves_possible[game] = any(\n",
    "                _recursive_steps(boards[game, :, :], direction, moves[game]) > 0\n",
    "                for direction in DIRECTIONS\n",
    "            )\n",
    "    return arr_moves_possible\n",
    "\n",
    "\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.ones((3, 8, 8)) * 1, np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(get_new_games(3), np.array([[2, 3], [3, 2], [3, 2]])),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(get_new_games(3), np.array([[2, 2], [1, 1], [0, 0]])),\n",
    "    np.array([False] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.ones((3, 8, 8)) * -1, np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.zeros((3, 8, 8)), np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reword functions\n",
    "\n",
    "For any kind of reinforcement learning is a reword function needed.\n",
    "For otello this would be the final score, the information who won or changes to the score.\n",
    "A combination of those three would also be possible.\n",
    "It is probably not be possible to weight the current score to high in a reword function since that would be to close to a classic greedy algorithm.\n",
    "But some direct influence would increase the learning speed.\n",
    "In the next section are all three reword functions implemented to be combined and weight later on as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "182 µs ± 6.7 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
      "34.4 µs ± 1.82 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
      "32.2 µs ± 743 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def final_boards_evaluation(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Evaluates the board at the end of the game.\n",
    "\n",
    "    All unused fields are added to the score of the player that has more stones with his color up.\n",
    "    This score only applies to the end of the game.\n",
    "    Normally the score is represented by the number of stones each player has.\n",
    "    In this case the score was combined by building the difference.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of game bords ot the end of the game.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    score1, score2 = np.sum(boards == 1, axis=(1, 2)), np.sum(boards == -1, axis=(1, 2))\n",
    "    player_1_won = score1 > score2\n",
    "    player_2_won = score1 < score2\n",
    "    score1_final = 64 - score2[player_1_won]\n",
    "    score2_final = 64 - score1[player_2_won]\n",
    "    score1[player_1_won] = score1_final\n",
    "    score2[player_2_won] = score2_final\n",
    "    return score1 - score2\n",
    "\n",
    "\n",
    "def evaluate_boards(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Counts the stones each player has on the board.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    return np.sum(boards, axis=(1, 2))\n",
    "\n",
    "\n",
    "def evaluate_who_won(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Checks who won or is winning a game.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        The information who won for both player. 1 meaning the player won, -1 means the opponent lost. 0 represents a patt.\n",
    "    \"\"\"\n",
    "    return np.sign(np.sum(boards, axis=(1, 2)))\n",
    "\n",
    "\n",
    "_boards = get_new_games(EXAMPLE_STACK_SIZE)\n",
    "%timeit final_boards_evaluation(_boards)\n",
    "%timeit evaluate_boards(_boards)\n",
    "%timeit evaluate_who_won(_boards)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute a chosen action\n",
    "\n",
    "After an evaluation what turns are possible there needs to be a function that executes a turn.\n",
    "This next sections does that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InvalidTurn(ValueError):\n",
    "    \"\"\"\n",
    "    This error is thrown if a given turn is not valid.\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.7 ms ± 1.18 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def do_moves(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Executes a single move on a stack o Othello boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of Othello boards where the next stone should be placed.\n",
    "        moves: A stack of stone placement orders for the game. Formatted as coordinates in an array [x, y] of the place where the stone should be placed. Should contain [-1,-1] if no new placement is possible.\n",
    "\n",
    "    Returns:\n",
    "        The new state of the board.\n",
    "    \"\"\"\n",
    "\n",
    "    def _do_directional_move(\n",
    "        board: np.ndarray, rec_move: np.ndarray, rev_direction, step_one=True\n",
    "    ) -> bool:\n",
    "        \"\"\"Changes the color of enemy stones in one direction.\n",
    "\n",
    "        This function works recursive. The argument step_one should always be used in its default value.\n",
    "\n",
    "        Args:\n",
    "            board: A bord on which a stone was placed.\n",
    "            rec_move: The position on the board in x and y where this function is called from. Will be moved by recursive called.\n",
    "            rev_direction: The position where the stone was placed. Inside this recursion it will also be the last step that was checked.\n",
    "            step_one: Set to true if this is the first step in the recursion. False later on.\n",
    "\n",
    "        Returns:\n",
    "            True if a stone could be flipped.\n",
    "            All changes are made on the view of the numpy array and therefore not included in the return value.\n",
    "        \"\"\"\n",
    "        rec_position = rec_move + rev_direction\n",
    "        if np.any((rec_position >= 8) | (rec_position < 0)):\n",
    "            return False\n",
    "        next_field = board[tuple(rec_position.tolist())]\n",
    "        if next_field == 0:\n",
    "            return False\n",
    "        if next_field == 1:\n",
    "            return not step_one\n",
    "        if next_field == -1:\n",
    "            if _do_directional_move(board, rec_position, rev_direction, step_one=False):\n",
    "                board[tuple(rec_position.tolist())] = 1\n",
    "                return True\n",
    "            return False\n",
    "\n",
    "    def _do_move(_board: np.ndarray, move: np.ndarray) -> None:\n",
    "        \"\"\"Executes a turn on a board.\n",
    "\n",
    "        Args:\n",
    "            _board: The game board on wich to place a stone.\n",
    "            move: The coordinates of a stone that should be placed. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
    "\n",
    "        Returns:\n",
    "            All changes are made on the view of the numpy array.\n",
    "        \"\"\"\n",
    "        if np.all(move == -1):\n",
    "            if not move_possible(_board, move):\n",
    "                raise InvalidTurn(\"An action should be taken. A turn is possible.\")\n",
    "            return\n",
    "\n",
    "        # noinspection PyTypeChecker\n",
    "        if _board[tuple(move.tolist())] != 0:\n",
    "            raise InvalidTurn(\"This turn is not possible.\")\n",
    "\n",
    "        action = False\n",
    "        for direction in DIRECTIONS:\n",
    "            if _do_directional_move(_board, move, direction):\n",
    "                action = True\n",
    "        if not action:\n",
    "            raise InvalidTurn(\"This turn is not possible.\")\n",
    "\n",
    "        # noinspection PyTypeChecker\n",
    "        _board[tuple(move.tolist())] = 1\n",
    "\n",
    "    boards = boards.copy()\n",
    "    for game in range(boards.shape[0]):\n",
    "        _do_move(boards[game], moves[game])\n",
    "    return boards\n",
    "\n",
    "\n",
    "%timeit do_moves(get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE))[0]\n",
    "\n",
    "plot_othello_board(\n",
    "    do_moves(\n",
    "        get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE)\n",
    "    )[0]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An abstract reversi game policy\n",
    "\n",
    "For an easy use of policies an abstract class containing the policy generation / requests an action in an inherited instance of this class.\n",
    "This class filters the policy to only propose valid actions. Inherited instance do not need to care about this. This super class also manges exploration and exploitation with the epsilon value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GamePolicy(ABC):\n",
    "    \"\"\"\n",
    "    A game policy. Proposes where to place a stone next.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            epsilon: the epsilon / greedy value. Should be between zero and one. Set the mixture of policy and exploration. One means only the policy is used. Zero means only random policies are used. All mixtures inbetween between are possible.\n",
    "        \"\"\"\n",
    "        if 0 > epsilon > 1:\n",
    "            raise ValueError(\"Epsilon should be between zero and one.\")\n",
    "        self._epsilon: float = epsilon\n",
    "\n",
    "    @property\n",
    "    def epsilon(self):\n",
    "        return self._epsilon\n",
    "\n",
    "    @property\n",
    "    @abc.abstractmethod\n",
    "    def policy_name(self) -> str:\n",
    "        \"\"\"The name of this policy\"\"\"\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    @abc.abstractmethod\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        \"\"\"The internal policy is an unfiltered policy. It should only be called from inside this function\n",
    "\n",
    "        Args:\n",
    "            boards: A board where a policy should be calculated for.\n",
    "\n",
    "        Returns:\n",
    "            The policy for this board. Should have the same size as the boards array.\n",
    "        \"\"\"\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def get_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        \"\"\"Calculates the policy that should be followed.\n",
    "\n",
    "        Calculates the policy that should be followed.\n",
    "        This function does include the usage of epsilon to configure greediness and exploration.\n",
    "\n",
    "        Args:\n",
    "            boards: A set of boards that show the environment where the policy should be calculated for.\n",
    "\n",
    "        Returns:\n",
    "            A vector of indices. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
    "        \"\"\"\n",
    "        assert len(boards.shape) == 3\n",
    "        assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n",
    "\n",
    "        if self.epsilon <= 0:\n",
    "            policies = np.random.rand(*boards.shape)\n",
    "        else:\n",
    "            policies = self._internal_policy(boards)\n",
    "            if self.epsilon < 1:\n",
    "                policies = policies * self.epsilon + np.random.rand(*boards.shape) * (\n",
    "                    1 - self.epsilon\n",
    "                )\n",
    "\n",
    "        # todo talk to team about backpropagation of score and epsilon for greedy factor\n",
    "\n",
    "        # todo possibly change this function to only validate the purpose turn and not all turns\n",
    "        possible_turns = get_possible_turns(boards)\n",
    "        policies[possible_turns == False] = -1.0\n",
    "        max_indices = [\n",
    "            np.unravel_index(policy.argmax(), policy.shape) for policy in policies\n",
    "        ]\n",
    "        policy_vector = np.array(max_indices, dtype=int)\n",
    "        no_turn_possible = np.all(policy_vector == 0, 1) & (policies[:, 0, 0] == -1.0)\n",
    "\n",
    "        policy_vector[no_turn_possible, :] = IMPOSSIBLE\n",
    "        return policy_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A first policy\n",
    "\n",
    "To quantify the quality of a game AI there needs to be some benchmarks.\n",
    "The easiest benchmark is to play against a random player.\n",
    "The easiest player to use as a benchmark is the random player.\n",
    "For this and testing purpose the random policy was implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RandomPolicy(GamePolicy):\n",
    "    \"\"\"\n",
    "    A policy playing a random turn by setting epsilon to 0.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float = 0):\n",
    "        _ = epsilon\n",
    "        super().__init__(epsilon=0)\n",
    "\n",
    "    @property\n",
    "    def policy_name(self) -> str:\n",
    "        return \"random\"\n",
    "\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        pass\n",
    "\n",
    "\n",
    "rnd_policy = RandomPolicy(1)\n",
    "assert rnd_policy.policy_name == \"random\"\n",
    "assert rnd_policy.epsilon == 0\n",
    "\n",
    "rnd_policy_result = rnd_policy.get_policy(get_new_games(10))\n",
    "assert np.any((5 >= rnd_policy_result) & (rnd_policy_result >= 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GreedyPolicy(GamePolicy):\n",
    "    \"\"\"\n",
    "    A policy playing always one of the strongest turns.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float = 1):\n",
    "        _ = epsilon\n",
    "        super().__init__(1)\n",
    "\n",
    "    @property\n",
    "    def policy_name(self) -> str:\n",
    "        return \"greedy_policy\"\n",
    "\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        policies = np.random.rand(*boards.shape)\n",
    "        for game, idx, idy in itertools.product(\n",
    "            range(boards.shape[0]), range(BOARD_SIZE), range(BOARD_SIZE)\n",
    "        ):\n",
    "\n",
    "            if _poss_turns[game, idx, idy]:\n",
    "                position = idx, idy\n",
    "                policies[game, idx, idy] += np.sum(\n",
    "                    _recursive_steps(boards[game, :, :], direction, position)\n",
    "                    for direction in DIRECTIONS\n",
    "                )\n",
    "        return policies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting the game simulation together\n",
    "Now it's time to bring all together for a proper simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Playing a single turn\n",
    "\n",
    "The next function needed is used to request a policy, verify that the turn is legit and place a stone and turn enemy stones if possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.02 s ± 31.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1.01 s ± 35 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def single_turn(\n",
    "    current_boards: np, policy: GamePolicy\n",
    ") -> tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"Execute a single turn on a board.\n",
    "\n",
    "    Places a new stone on the board. Turns captured enemy stones.\n",
    "\n",
    "    Args:\n",
    "        current_boards: The current board before the game.\n",
    "        policy: The game policy to be used.\n",
    "\n",
    "    Returns:\n",
    "        The new game board and the policy vector containing the index of the action used.\n",
    "    \"\"\"\n",
    "    policy_results = policy.get_policy(current_boards)\n",
    "\n",
    "    # if the constant VERIFY_POLICY is set to true the policy is verified. Should be good though.\n",
    "    # todo deactivate the policy verification after some testing.\n",
    "    if VERIFY_POLICY:\n",
    "        assert np.all(moves_possible(current_boards, policy_results)), (\n",
    "            current_boards[(moves_possible(current_boards, policy_results) == False)],\n",
    "            policy_results[(moves_possible(current_boards, policy_results) == False)],\n",
    "            np.where(moves_possible(current_boards, policy_results) == False),\n",
    "        )\n",
    "    return do_moves(current_boards, policy_results), policy_results\n",
    "\n",
    "\n",
    "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "VERIFY_POLICY = False  # type: ignore\n",
    "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "VERIFY_POLICY = True  # type: ignore\n",
    "plot_othello_boards(\n",
    "    single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))[0][:8]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulate a stack of games\n",
    "This function will simulate a stack of games and return an array of policies and histories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x4800 with 61 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def simulate_game(\n",
    "    nr_of_games: int,\n",
    "    policies: tuple[GamePolicy, GamePolicy],\n",
    "    tqdm_on: bool = False,\n",
    ") -> tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"Simulates a stack of games.\n",
    "\n",
    "    Args:\n",
    "        nr_of_games: The number of games that should be simulated.\n",
    "        policies: The policies that should be used to simulate the game.\n",
    "        tqdm_on: Switches tqdm on.\n",
    "\n",
    "    Returns:\n",
    "        A stack of board histories and actions.\n",
    "    \"\"\"\n",
    "    board_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 8, 8), dtype=np.int8)\n",
    "    action_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 2), dtype=np.int8)\n",
    "    current_boards = get_new_games(nr_of_games)\n",
    "    for turn_index in tqdm(range(SIMULATE_TURNS)) if tqdm_on else range(SIMULATE_TURNS):\n",
    "        policy_index = turn_index % 2\n",
    "        policy = policies[policy_index]\n",
    "        board_history_stack[turn_index, :, :, :] = current_boards\n",
    "        if policy_index == 0:\n",
    "            current_boards = current_boards * -1\n",
    "        current_boards, action_taken = single_turn(current_boards, policy)\n",
    "        action_history_stack[turn_index, :] = action_taken\n",
    "\n",
    "        if policy_index == 0:\n",
    "            current_boards = current_boards * -1\n",
    "\n",
    "    return board_history_stack, action_history_stack\n",
    "\n",
    "\n",
    "simulation_results = simulate_game(1, (RandomPolicy(1), RandomPolicy(1)))\n",
    "plot_othello_boards(\n",
    "    drop_duplicate_boards(np.reshape(simulation_results[0], (-1, 8, 8)))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.5 s ± 737 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical examination of the natural action space and result\n",
    "As for many project some evaluation of the project is in order.\n",
    "\n",
    "1. What is the expected distribution of scores\n",
    "2. What is the expected distribution of possible actions\n",
    "\n",
    "    a. over time\n",
    "    \n",
    "    b. ober space\n",
    "\n",
    "The easiest and robustest way to analyse this is when analyzing randomly played games."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this pupose we played a sample of 10k games and saved them for later analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 10000, 8, 8)\n",
      "(70, 10000, 2)\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(\"rnd_history.npy\") and not os.path.exists(\"rnd_action.npy\"):\n",
    "    rnds = RandomPolicy(1), RandomPolicy(1)\n",
    "    simulation_results = simulate_game(10_000, rnds, tqdm_on=True)\n",
    "    _board_history, _action_history = simulation_results\n",
    "    np.save(\"rnd_history.npy\", np.astpye.astype(np.int8))\n",
    "    np.save(\"rnd_action.npy\", _action_history.astype(np.int8))\n",
    "else:\n",
    "    _board_history = np.load(\"rnd_history.npy\")\n",
    "    _action_history = np.load(\"rnd_action.npy\")\n",
    "print(_board_history.shape)\n",
    "print(_action_history.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For those 10k games the possible actions where evaluated and saved for each and every turn in the game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000, 8, 8)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists(\"turn_possible.npy\"):\n",
    "    __board_history = _board_history.copy()\n",
    "    __board_history[1::2] = __board_history[1::2] * -1\n",
    "\n",
    "    _poss_turns = get_possible_turns(\n",
    "        __board_history.reshape((-1, 8, 8)), tqdm_on=True\n",
    "    ).reshape((SIMULATE_TURNS, -1, 8, 8))\n",
    "    np.save(\"turn_possible.npy\", _poss_turns)\n",
    "    del __board_history\n",
    "_poss_turns = np.load(\"turn_possible.npy\")\n",
    "_poss_turns.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those possible turms then where counted for all games in the history stack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_poss_turns = np.sum(_poss_turns, axis=(2, 3))\n",
    "count_poss_turns.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the po"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b098bb4da154488b8b4c22722833e8c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=34, description='turn', max=69), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_possibilitie_count = np.mean(count_poss_turns, axis=1)\n",
    "std_possibilitie_count = np.std(count_poss_turns, axis=1)\n",
    "\n",
    "\n",
    "@interact(turn=(0, 69))\n",
    "def poss_turn_count(turn):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 7))\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis\\nThe total size is estimated to be around {np.prod(np.extract(mean_possibilitie_count, mean_possibilitie_count)):.4g}\"\n",
    "    )\n",
    "    ax1.hist(count_poss_turns[turn], density=True)\n",
    "    ax1.set_title(f\"Histogram of the action space size for turn {turn}\")\n",
    "    ax1.set_xlabel(\"Action space size\")\n",
    "    ax1.set_ylabel(\"Action space size probability\")\n",
    "    ax2.set_title(f\"Mean size of the action space per turn\")\n",
    "    ax2.set_xlabel(\"Turn\")\n",
    "    ax2.set_ylabel(\"Average possible moves\")\n",
    "\n",
    "    ax2.errorbar(\n",
    "        range(70),\n",
    "        mean_possibilitie_count,\n",
    "        yerr=std_possibilitie_count,\n",
    "        label=\"Mean action space size with error bars\",\n",
    "    )\n",
    "    ax2.scatter(turn, mean_possibilitie_count[turn], marker=\"x\")\n",
    "    ax2.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is interesting to see that the action space for the first player (white) is much smaller than for the second palyer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>Total mean actionspace</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>5.687159e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black</th>\n",
       "      <td>3.753117e+20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total mean actionspace\n",
       "white            5.687159e+18\n",
       "black            3.753117e+20"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white = mean_possibilitie_count[::2]\n",
    "black = mean_possibilitie_count[1::2]\n",
    "df = pd.DataFrame(\n",
    "    [\n",
    "        {\n",
    "            \"white\": np.prod(np.extract(white, white)),\n",
    "            \"black\": np.prod(np.extract(black, black)),\n",
    "        }\n",
    "    ],\n",
    "    index=[\"Total mean actionspace\"],\n",
    ").T\n",
    "del white, black\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7002a64f4eb740c7bcbb4810783e70fa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=34, description='turn', max=69), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(turn=(0, 69))\n",
    "def turn_distribution_heatmap(turn):\n",
    "    turn_possibility_on_field = np.mean(_poss_turns[turn], axis=0)\n",
    "\n",
    "    uniform_data = np.random.rand(10, 12)\n",
    "    sns.heatmap(\n",
    "        turn_possibility_on_field,\n",
    "        linewidth=0.5,\n",
    "        square=True,\n",
    "        annot=True,\n",
    "        xticklabels=\"ABCDEFGH\",\n",
    "        yticklabels=list(range(1, 9)),\n",
    "    )\n",
    "    plt.title(f\"Headmap of where stones can be placed on turn {turn}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 10000)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.046875,  0.046875,  0.046875, ...,  0.046875,  0.046875,\n",
       "         0.046875],\n",
       "       [-0.046875, -0.046875, -0.046875, ..., -0.046875, -0.046875,\n",
       "        -0.046875],\n",
       "       [ 0.046875,  0.046875,  0.046875, ...,  0.078125,  0.046875,\n",
       "         0.046875],\n",
       "       ...,\n",
       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
       "         0.      ],\n",
       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
       "         0.      ],\n",
       "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
       "         0.      ]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calculate_direct_score(board_history: np.ndarray) -> np.ndarray:\n",
    "    boards_evaluated = np.reshape(\n",
    "        evaluate_boards(np.reshape(board_history, (-1, 8, 8))), (SIMULATE_TURNS, -1)\n",
    "    )\n",
    "    direct_score = boards_evaluated - np.roll(boards_evaluated, shift=-1, axis=0)\n",
    "    direct_score[-1] = 0\n",
    "    return direct_score / 64\n",
    "\n",
    "\n",
    "assert len(calculate_direct_score(_board_history).shape) == 2\n",
    "assert calculate_direct_score(_board_history).shape[0] == SIMULATE_TURNS\n",
    "print(calculate_direct_score(_board_history).shape)\n",
    "calculate_direct_score(_board_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75ffc8765b074cd0b4495ac6075bb6b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=29, description='turn', max=59), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_history = calculate_direct_score(_board_history) * 64\n",
    "score_history[1::2] = score_history[1::2] * -1\n",
    "\n",
    "\n",
    "@interact(turn=(0, 59))\n",
    "def hist_direct_score(turn):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 7))\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis\\nThe total size is estimated to be around {np.prod(np.extract(mean_possibilitie_count, mean_possibilitie_count)):.4g}\"\n",
    "    )\n",
    "\n",
    "    ax1.set_title(\n",
    "        f\"Histogram of scores on turn {turn} by {'white' if turn % 2 == 0 else 'black'}\"\n",
    "    )\n",
    "\n",
    "    ax1.hist(score_history[turn], density=True)\n",
    "    ax1.set_xlabel(\"Points made\")\n",
    "    ax1.set_ylabel(\"Score probability\")\n",
    "    ax2.set_title(f\"Points scored at turn\")\n",
    "    ax2.set_xlabel(\"Turn\")\n",
    "    ax2.set_ylabel(\"Average points scored\")\n",
    "\n",
    "    ax2.errorbar(\n",
    "        range(60),\n",
    "        np.mean(score_history, axis=1)[:60],\n",
    "        yerr=np.std(score_history, axis=1)[:60],\n",
    "        label=\"Mean socre at turn\",\n",
    "    )\n",
    "    ax2.scatter(turn, np.mean(score_history, axis=1)[turn], marker=\"x\", color=\"red\")\n",
    "    ax2.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_final_evaluation_for_history(board_history: np.ndarray) -> np.ndarray:\n",
    "    final_evaluation = final_boards_evaluation(board_history[-1])\n",
    "    return final_evaluation / 64\n",
    "\n",
    "\n",
    "assert len(calculate_final_evaluation_for_history(_board_history).shape) == 1\n",
    "_final_eval = calculate_final_evaluation_for_history(_board_history)\n",
    "plt.title(\"Histogram over the score distribtuion\")\n",
    "plt.hist((_final_eval * 64), density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lypRSYzLGlJoXb5Q3X5XVvHlzDRgwQC+//LK+++67UvcfOXLkgtso+frl+eefV9euXRUUFORuz8zM1MaNGyv1Fc3FuPXWW3Xu3Dm99NJL7raioqJKPe+OHz9eqi0qKkqS3K+Ni32cz/f3v/9dJ0+edN9esmSJvvvuO/frqGSfZX0lWXIOR8l5OtKPY33llVcuuN/Kvo5Rt3FkBFZ16tRJAwYMUHR0tJo2baqNGzdqyZIlGjt2rLtPdHS0JOnRRx9VfHy8fH19NWTIEN1+++26/vrrNXHiRO3du1fdunXT6tWrtWzZMv3hD39wv0FGR0fr7rvvVnp6uo4dO+a+tPfrr7+WVLl/yQcGBqpfv36aPn26fvjhB4WHh2v16tXas2fPJXhUSuvWrZsSExP1yiuv6MSJE+rfv782bNigV199VYMHD9b111/v0f/WW29V48aNNX78ePn6+uruu+/2uL9NmzZ65plnlJKSor1792rw4MFq3Lix9uzZo7feeksPPvigxo8fX6Vay5svb2RkZKhPnz7q0qWLRo8erdatWys3N1dZWVk6ePCgPvvsswrXb9u2rcLCwrRjxw6PE0b79eunJ554QpIueRi5/fbbdd1112nChAnau3evOnXqpKVLl5b5YX6+qVOn6v3339fAgQPVqlUrHT58WH/961915ZVXun/3pk2bNgoODtbs2bPVuHFjNWzYULGxsWWe71QZTZs2VZ8+fZSUlKTc3Fylp6erbdu2Gj16tLtPdHS0Fi1apOTkZPXs2VONGjXS7bffrmuvvVa9evVSSkqKjh8/rqZNm2rhwoU6d+5cpR6nyryOUcdZuIIHdUTJ5Xyffvppmff379//gpf2PvPMMyYmJsYEBwebBg0amA4dOphnn33WnD171t3n3Llz5pFHHjHNmjUzDofD4xLIkydPmj/+8Y+mZcuWpn79+qZdu3ZmxowZpri42GO/BQUFZsyYMaZp06amUaNGZvDgwWbHjh1GkseltiWXWJZ1SeXBgwfNnXfeaYKDg01QUJC59957zaFDh8q9PPj8bSQmJpqGDRtW6nEqyw8//GCmTJlirr76alO/fn0TERFhUlJSPC5//bkHHnjASDIul6vcbf7rX/8yffr0MQ0bNjQNGzY0HTp0MGPGjDE7duzwur4S5c1XyaW9M2bMKLXO+Y+hMcbs3r3bDB8+3ISFhZn69eub8PBwc9ttt5klS5ZUqo57773XSDKLFi1yt509e9YEBAQYPz8/c+bMGY/+5V3aW9al5+dfHlueY8eOmWHDhpnAwEATFBRkhg0bZjZv3nzBS3szMzPNoEGDTMuWLY2fn59p2bKlGTp0qPn66689tr9s2TLTqVMnU69ePY9tVjRn5V3a+8Ybb5iUlBTTvHlz06BBAzNw4ED3peAlTp06Ze6//34THBxsJHlcort7927jcrmM0+k0oaGh5sknnzRr1qy54KW9xlT+dSzJjBkzptSYzn9fQe3jMIazfvDLtGXLFnXv3l2vvfaaHnjgAdvlAMAvFueM4BehrD9Clp6eLh8fnwv+8ikA4NLinBH8IkyfPl3Z2dm6/vrrVa9ePa1cuVIrV67Ugw8+qIiICNvlAcAvGl/T4BdhzZo1mjJlirZt26ZTp07pqquu0rBhwzRx4kTVq0cmBwCbCCMAAMAqzhkBAABWEUYAAIBVteLL8uLiYh06dEiNGzeu9p9ABgAAl4YxRidPnlTLli3l41P+8Y9aEUYOHTrEFQ8AANRSBw4c0JVXXlnu/bUijJT88aQDBw4oMDDQcjUAAKAy8vPzFRER4fFHEMtSK8JIyVczgYGBhBEAAGqZC51iwQmsAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwqp7tAgAAlRc5YbntElAH7Z020Or+OTICAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAqiqFkYyMDEVGRsrf31+xsbHasGFDuX0HDBggh8NRahk4cGCViwYAAHWH12Fk0aJFSk5O1qRJk7Rp0yZ169ZN8fHxOnz4cJn9ly5dqu+++869fPnll/L19dW999570cUDAIDaz+swMmvWLI0ePVpJSUnq1KmTZs+erYCAAM2bN6/M/k2bNlVYWJh7WbNmjQICAggjAABAkpdh5OzZs8rOzpbL5fppAz4+crlcysrKqtQ25s6dqyFDhqhhw4bl9iksLFR+fr7HAgAA6iavwsjRo0dVVFSk0NBQj/bQ0FDl5ORccP0NGzboyy+/1KhRoyrsl5aWpqCgIPcSERHhTZkAAKAWqdGraebOnasuXbooJiamwn4pKSnKy8tzLwcOHKihCgEAQE2r503nkJAQ+fr6Kjc316M9NzdXYWFhFa5bUFCghQsXaurUqRfcj9PplNPp9KY0AABQS3l1ZMTPz0/R0dHKzMx0txUXFyszM1NxcXEVrvvmm2+qsLBQv/nNb6pWKQAAqJO8OjIiScnJyUpMTFSPHj0UExOj9PR0FRQUKCkpSZI0fPhwhYeHKy0tzWO9uXPnavDgwbriiiuqp3IAAFAneB1GEhISdOTIEaWmpionJ0dRUVFatWqV+6TW/fv3y8fH84DLjh07tH79eq1evbp6qgYAAHWGwxhjbBdxIfn5+QoKClJeXp4CAwNtlwMA1kROWG67BNRBe6ddml9Fr+znN3+bBgAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABY5fWlvXUNZ6bjUrlUZ6cDQF3DkREAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWFWlMJKRkaHIyEj5+/srNjZWGzZsqLD/iRMnNGbMGLVo0UJOp1PXXHONVqxYUaWCAQBA3VLP2xUWLVqk5ORkzZ49W7GxsUpPT1d8fLx27Nih5s2bl+p/9uxZ3XTTTWrevLmWLFmi8PBw7du3T8HBwdVRPwAAqOW8DiOzZs3S6NGjlZSUJEmaPXu2li9frnnz5mnChAml+s+bN0/Hjx/XRx99pPr160uSIiMjL65qAABQZ3j1Nc3Zs2eVnZ0tl8v10wZ8fORyuZSVlVXmOu+8847i4uI0ZswYhYaGqnPnznruuedUVFRU7n4KCwuVn5/vsQAAgLrJqzBy9OhRFRUVKTQ01KM9NDRUOTk5Za7zzTffaMmSJSoqKtKKFSv01FNP6c9//rOeeeaZcveTlpamoKAg9xIREeFNmQAAoBa55FfTFBcXq3nz5nrllVcUHR2thIQETZw4UbNnzy53nZSUFOXl5bmXAwcOXOoyAQCAJV6dMxISEiJfX1/l5uZ6tOfm5iosLKzMdVq0aKH69evL19fX3daxY0fl5OTo7Nmz8vPzK7WO0+mU0+n0pjQAAFBLeXVkxM/PT9HR0crMzHS3FRcXKzMzU3FxcWWuc91112nXrl0qLi52t3399ddq0aJFmUEEAAD8snj9NU1ycrLmzJmjV199Vdu3b9dDDz2kgoIC99U1w4cPV0pKirv/Qw89pOPHj+uxxx7T119/reXLl+u5557TmDFjqm8UAACg1vL60t6EhAQdOXJEqampysnJUVRUlFatWuU+qXX//v3y8fkp40REROjdd9/VH//4R3Xt2lXh4eF67LHH9MQTT1TfKAAAQK3ldRiRpLFjx2rs2LFl3rdu3bpSbXFxcfr444+rsisAAFDH8bdpAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWFWlMJKRkaHIyEj5+/srNjZWGzZsKLfvggUL5HA4PBZ/f/8qFwwAAOoWr8PIokWLlJycrEmTJmnTpk3q1q2b4uPjdfjw4XLXCQwM1Hfffede9u3bd1FFAwCAusPrMDJr1iyNHj1aSUlJ6tSpk2bPnq2AgADNmzev3HUcDofCwsLcS2ho6EUVDQAA6g6vwsjZs2eVnZ0tl8v10wZ8fORyuZSVlVXueqdOnVKrVq0UERGhQYMGaevWrRXup7CwUPn5+R4LAACom7wKI0ePHlVRUVGpIxuhoaHKyckpc5327dtr3rx5WrZsmV577TUVFxerd+/eOnjwYLn7SUtLU1BQkHuJiIjwpkwAAFCLXPKraeLi4jR8+HBFRUWpf//+Wrp0qZo1a6aXX3653HVSUlKUl5fnXg4cOHCpywQAAJbU86ZzSEiIfH19lZub69Gem5ursLCwSm2jfv366t69u3bt2lVuH6fTKafT6U1pAACglvLqyIifn5+io6OVmZnpbisuLlZmZqbi4uIqtY2ioiJ98cUXatGihXeVAgCAOsmrIyOSlJycrMTERPXo0UMxMTFKT09XQUGBkpKSJEnDhw9XeHi40tLSJElTp05Vr1691LZtW504cUIzZszQvn37NGrUqOodCQAAqJW8DiMJCQk6cuSIUlNTlZOTo6ioKK1atcp9Uuv+/fvl4/PTAZfvv/9eo0ePVk5Ojpo0aaLo6Gh99NFH6tSpU/WNAgAA1FoOY4yxXcSF5OfnKygoSHl5eQoMDKzWbUdOWF6t2wNK7J020HYJqIN4z8KlcKneryr7+c3fpgEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGBVlcJIRkaGIiMj5e/vr9jYWG3YsKFS6y1cuFAOh0ODBw+uym4BAEAd5HUYWbRokZKTkzVp0iRt2rRJ3bp1U3x8vA4fPlzhenv37tX48ePVt2/fKhcLAADqHq/DyKxZszR69GglJSWpU6dOmj17tgICAjRv3rxy1ykqKtIDDzygKVOmqHXr1hdVMAAAqFu8CiNnz55Vdna2XC7XTxvw8ZHL5VJWVla5602dOlXNmzfXyJEjK7WfwsJC5efneywAAKBu8iqMHD16VEVFRQoNDfVoDw0NVU5OTpnrrF+/XnPnztWcOXMqvZ+0tDQFBQW5l4iICG/KBAAAtcglvZrm5MmTGjZsmObMmaOQkJBKr5eSkqK8vDz3cuDAgUtYJQAAsKmeN51DQkLk6+ur3Nxcj/bc3FyFhYWV6r97927t3btXt99+u7utuLj4xx3Xq6cdO3aoTZs2pdZzOp1yOp3elAYAAGopr46M+Pn5KTo6WpmZme624uJiZWZmKi4urlT/Dh066IsvvtCWLVvcyx133KHrr79eW7Zs4esXAADg3ZERSUpOTlZiYqJ69OihmJgYpaenq6CgQElJSZKk4cOHKzw8XGlpafL391fnzp091g8ODpakUu0AAOCXyeswkpCQoCNHjig1NVU5OTmKiorSqlWr3Ce17t+/Xz4+/LArAACoHK/DiCSNHTtWY8eOLfO+devWVbjuggULqrJLAABQR3EIAwAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYFWVwkhGRoYiIyPl7++v2NhYbdiwody+S5cuVY8ePRQcHKyGDRsqKipK//jHP6pcMAAAqFu8DiOLFi1ScnKyJk2apE2bNqlbt26Kj4/X4cOHy+zftGlTTZw4UVlZWfr888+VlJSkpKQkvfvuuxddPAAAqP28DiOzZs3S6NGjlZSUpE6dOmn27NkKCAjQvHnzyuw/YMAA3XnnnerYsaPatGmjxx57TF27dtX69evL3UdhYaHy8/M9FgAAUDd5FUbOnj2r7OxsuVyunzbg4yOXy6WsrKwLrm+MUWZmpnbs2KF+/fqV2y8tLU1BQUHuJSIiwpsyAQBALeJVGDl69KiKiooUGhrq0R4aGqqcnJxy18vLy1OjRo3k5+engQMH6sUXX9RNN91Ubv+UlBTl5eW5lwMHDnhTJgAAqEXq1cROGjdurC1btujUqVPKzMxUcnKyWrdurQEDBpTZ3+l0yul01kRpAADAMq/CSEhIiHx9fZWbm+vRnpubq7CwsHLX8/HxUdu2bSVJUVFR2r59u9LS0soNIwAA4JfDq69p/Pz8FB0drczMTHdbcXGxMjMzFRcXV+ntFBcXq7Cw0JtdAwCAOsrrr2mSk5OVmJioHj16KCYmRunp6SooKFBSUpIkafjw4QoPD1daWpqkH09G7dGjh9q0aaPCwkKtWLFC//jHP/TSSy9V70gAAECt5HUYSUhI0JEjR5SamqqcnBxFRUVp1apV7pNa9+/fLx+fnw64FBQU6OGHH9bBgwfVoEEDdejQQa+99poSEhKqbxQAAKDWchhjjO0iLiQ/P19BQUHKy8tTYGBgtW47csLyat0eUGLvtIG2S0AdxHsWLoVL9X5V2c9v/jYNAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAq6oURjIyMhQZGSl/f3/FxsZqw4YN5fadM2eO+vbtqyZNmqhJkyZyuVwV9gcAAL8sXoeRRYsWKTk5WZMmTdKmTZvUrVs3xcfH6/Dhw2X2X7dunYYOHaq1a9cqKytLERER+vWvf61vv/32oosHAAC1n9dhZNasWRo9erSSkpLUqVMnzZ49WwEBAZo3b16Z/f/5z3/q4YcfVlRUlDp06KC//e1vKi4uVmZm5kUXDwAAaj+vwsjZs2eVnZ0tl8v10wZ8fORyuZSVlVWpbZw+fVo//PCDmjZtWm6fwsJC5efneywAAKBu8iqMHD16VEVFRQoNDfVoDw0NVU5OTqW28cQTT6hly5YegeZ8aWlpCgoKci8RERHelAkAAGqRGr2aZtq0aVq4cKHeeust+fv7l9svJSVFeXl57uXAgQM1WCUAAKhJ9bzpHBISIl9fX+Xm5nq05+bmKiwsrMJ1Z86cqWnTpum9995T165dK+zrdDrldDq9KQ0AANRSXh0Z8fPzU3R0tMfJpyUno8bFxZW73vTp0/X0009r1apV6tGjR9WrBQAAdY5XR0YkKTk5WYmJierRo4diYmKUnp6ugoICJSUlSZKGDx+u8PBwpaWlSZKef/55paam6vXXX1dkZKT73JJGjRqpUaNG1TgUAABQG3kdRhISEnTkyBGlpqYqJydHUVFRWrVqlfuk1v3798vH56cDLi+99JLOnj2re+65x2M7kyZN0uTJky+uegAAUOt5HUYkaezYsRo7dmyZ961bt87j9t69e6uyCwAA8AvB36YBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgVZXCSEZGhiIjI+Xv76/Y2Fht2LCh3L5bt27V3XffrcjISDkcDqWnp1e1VgAAUAd5HUYWLVqk5ORkTZo0SZs2bVK3bt0UHx+vw4cPl9n/9OnTat26taZNm6awsLCLLhgAANQtXoeRWbNmafTo0UpKSlKnTp00e/ZsBQQEaN68eWX279mzp2bMmKEhQ4bI6XRedMEAAKBu8SqMnD17VtnZ2XK5XD9twMdHLpdLWVlZ1VZUYWGh8vPzPRYAAFA3eRVGjh49qqKiIoWGhnq0h4aGKicnp9qKSktLU1BQkHuJiIiotm0DAIDLy2V5NU1KSory8vLcy4EDB2yXBAAALpF63nQOCQmRr6+vcnNzPdpzc3Or9eRUp9PJ+SUAAPxCeHVkxM/PT9HR0crMzHS3FRcXKzMzU3FxcdVeHAAAqPu8OjIiScnJyUpMTFSPHj0UExOj9PR0FRQUKCkpSZI0fPhwhYeHKy0tTdKPJ71u27bN/f/ffvuttmzZokaNGqlt27bVOBQAAFAbeR1GEhISdOTIEaWmpionJ0dRUVFatWqV+6TW/fv3y8fnpwMuhw4dUvfu3d23Z86cqZkzZ6p///5at27dxY8AAADUal6HEUkaO3asxo4dW+Z95weMyMhIGWOqshsAAPALcFleTQMAAH45CCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAq6r0C6wALixywnLbJQBArcCREQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYFWVwkhGRoYiIyPl7++v2NhYbdiwocL+b775pjp06CB/f3916dJFK1asqFKxAACg7vE6jCxatEjJycmaNGmSNm3apG7duik+Pl6HDx8us/9HH32koUOHauTIkdq8ebMGDx6swYMH68svv7zo4gEAQO3nMMYYb1aIjY1Vz5499Ze//EWSVFxcrIiICD3yyCOaMGFCqf4JCQkqKCjQ//7v/7rbevXqpaioKM2ePbtS+8zPz1dQUJDy8vIUGBjoTbkXFDlhebVuDwCA2mbvtIGXZLuV/fyu581Gz549q+zsbKWkpLjbfHx85HK5lJWVVeY6WVlZSk5O9miLj4/X22+/Xe5+CgsLVVhY6L6dl5cn6cdBVbfiwtPVvk0AAGqTS/H5+vPtXui4h1dh5OjRoyoqKlJoaKhHe2hoqL766qsy18nJySmzf05OTrn7SUtL05QpU0q1R0REeFMuAACohKD0S7v9kydPKigoqNz7vQojNSUlJcXjaEpxcbGOHz+uK664Qg6Ho9r2k5+fr4iICB04cKDav/65XNT1MTK+2q+uj5Hx1X51fYyXcnzGGJ08eVItW7assJ9XYSQkJES+vr7Kzc31aM/NzVVYWFiZ64SFhXnVX5KcTqecTqdHW3BwsDeleiUwMLBOPsF+rq6PkfHVfnV9jIyv9qvrY7xU46voiEgJr66m8fPzU3R0tDIzM91txcXFyszMVFxcXJnrxMXFefSXpDVr1pTbHwAA/LJ4/TVNcnKyEhMT1aNHD8XExCg9PV0FBQVKSkqSJA0fPlzh4eFKS0uTJD322GPq37+//vznP2vgwIFauHChNm7cqFdeeaV6RwIAAGolr8NIQkKCjhw5otTUVOXk5CgqKkqrVq1yn6S6f/9++fj8dMCld+/eev311/WnP/1JTz75pNq1a6e3335bnTt3rr5RVJHT6dSkSZNKfSVUl9T1MTK+2q+uj5Hx1X51fYyXw/i8/p0RAACA6sTfpgEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVtX5MPLss8+qd+/eCggIqPSvuBpjlJqaqhYtWqhBgwZyuVzauXOnR5/jx4/rgQceUGBgoIKDgzVy5EidOnXqEoygYt7WsXfvXjkcjjKXN998092vrPsXLlxYE0PyUJXHecCAAaVq//3vf+/RZ//+/Ro4cKACAgLUvHlzPf744zp37tylHEq5vB3j8ePH9cgjj6h9+/Zq0KCBrrrqKj366KPuPyhZwtYcZmRkKDIyUv7+/oqNjdWGDRsq7P/mm2+qQ4cO8vf3V5cuXbRixQqP+yvzeqxp3oxxzpw56tu3r5o0aaImTZrI5XKV6j9ixIhSc3XzzTdf6mGUy5vxLViwoFTt/v7+Hn0utzn0ZnxlvZ84HA4NHPjTX7m9nObv/fff1+23366WLVvK4XBU+EdpS6xbt06/+tWv5HQ61bZtWy1YsKBUH29f114zdVxqaqqZNWuWSU5ONkFBQZVaZ9q0aSYoKMi8/fbb5rPPPjN33HGHufrqq82ZM2fcfW6++WbTrVs38/HHH5sPPvjAtG3b1gwdOvQSjaJ83tZx7tw5891333ksU6ZMMY0aNTInT55095Nk5s+f79Hv5+OvKVV5nPv3729Gjx7tUXteXp77/nPnzpnOnTsbl8tlNm/ebFasWGFCQkJMSkrKpR5Ombwd4xdffGHuuusu884775hdu3aZzMxM065dO3P33Xd79LMxhwsXLjR+fn5m3rx5ZuvWrWb06NEmODjY5Obmltn/ww8/NL6+vmb69Olm27Zt5k9/+pOpX7+++eKLL9x9KvN6rEnejvH+++83GRkZZvPmzWb79u1mxIgRJigoyBw8eNDdJzEx0dx8880ec3X8+PGaGpIHb8c3f/58ExgY6FF7Tk6OR5/LaQ69Hd+xY8c8xvbll18aX19fM3/+fHefy2n+VqxYYSZOnGiWLl1qJJm33nqrwv7ffPONCQgIMMnJyWbbtm3mxRdfNL6+vmbVqlXuPt4+ZlVR58NIifnz51cqjBQXF5uwsDAzY8YMd9uJEyeM0+k0b7zxhjHGmG3bthlJ5tNPP3X3WblypXE4HObbb7+t9trLU111REVFmd/+9rcebZV5El9qVR1f//79zWOPPVbu/StWrDA+Pj4eb5gvvfSSCQwMNIWFhdVSe2VV1xwuXrzY+Pn5mR9++MHdZmMOY2JizJgxY9y3i4qKTMuWLU1aWlqZ/e+77z4zcOBAj7bY2Fjzu9/9zhhTuddjTfN2jOc7d+6cady4sXn11VfdbYmJiWbQoEHVXWqVeDu+C723Xm5zeLHz98ILL5jGjRubU6dOudsup/n7ucq8B/y///f/zLXXXuvRlpCQYOLj4923L/Yxq4w6/zWNt/bs2aOcnBy5XC53W1BQkGJjY5WVlSVJysrKUnBwsHr06OHu43K55OPjo08++aTGaq2OOrKzs7VlyxaNHDmy1H1jxoxRSEiIYmJiNG/ePJka/n28ixnfP//5T4WEhKhz585KSUnR6dOnPbbbpUsX968GS1J8fLzy8/O1devW6h9IBarruZSXl6fAwEDVq+f5o8o1OYdnz55Vdna2x2vHx8dHLpfL/do5X1ZWlkd/6ce5KOlfmddjTarKGM93+vRp/fDDD2ratKlH+7p169S8eXO1b99eDz30kI4dO1attVdGVcd36tQptWrVShERERo0aJDH6+hymsPqmL+5c+dqyJAhatiwoUf75TB/VXGh12B1PGaV4fXPwdd1OTk5kuTxQVVyu+S+nJwcNW/e3OP+evXqqWnTpu4+NaE66pg7d646duyo3r17e7RPnTpVN9xwgwICArR69Wo9/PDDOnXqlB599NFqq/9Cqjq++++/X61atVLLli31+eef64knntCOHTu0dOlS93bLmt+S+2pSdczh0aNH9fTTT+vBBx/0aK/pOTx69KiKiorKfGy/+uqrMtcpby5+/loraSuvT02qyhjP98QTT6hly5Yeb+4333yz7rrrLl199dXavXu3nnzySd1yyy3KysqSr69vtY6hIlUZX/v27TVv3jx17dpVeXl5mjlzpnr37q2tW7fqyiuvvKzm8GLnb8OGDfryyy81d+5cj/bLZf6qorzXYH5+vs6cOaPvv//+op/zlVErw8iECRP0/PPPV9hn+/bt6tChQw1VVL0qO76LdebMGb3++ut66qmnSt3387bu3buroKBAM2bMqJYPsks9vp9/KHfp0kUtWrTQjTfeqN27d6tNmzZV3q43amoO8/PzNXDgQHXq1EmTJ0/2uO9SziGqZtq0aVq4cKHWrVvncZLnkCFD3P/fpUsXde3aVW3atNG6det044032ii10uLi4jz+Cnvv3r3VsWNHvfzyy3r66actVlb95s6dqy5duigmJsajvTbP3+WiVoaRcePGacSIERX2ad26dZW2HRYWJknKzc1VixYt3O25ubmKiopy9zl8+LDHeufOndPx48fd61+Myo7vYutYsmSJTp8+reHDh1+wb2xsrJ5++mkVFhZe9B9TqqnxlYiNjZUk7dq1S23atFFYWFipM8Fzc3MlqVrmT6qZMZ48eVI333yzGjdurLfeekv169evsH91zmFZQkJC5Ovr634sS+Tm5pY7lrCwsAr7V+b1WJOqMsYSM2fO1LRp0/Tee++pa9euFfZt3bq1QkJCtGvXrhr9MLuY8ZWoX7++unfvrl27dkm6vObwYsZXUFCghQsXaurUqRfcj635q4ryXoOBgYFq0KCBfH19L/o5USnVdvbJZc7bE1hnzpzpbsvLyyvzBNaNGze6+7z77rvWTmCtah39+/cvdQVGeZ555hnTpEmTKtdaFdX1OK9fv95IMp999pkx5qcTWH9+JvjLL79sAgMDzX/+85/qG0AlVHWMeXl5plevXqZ///6moKCgUvuqiTmMiYkxY8eOdd8uKioy4eHhFZ7Aetttt3m0xcXFlTqBtaLXY03zdozGGPP888+bwMBAk5WVVal9HDhwwDgcDrNs2bKLrtdbVRnfz507d860b9/e/PGPfzTGXH5zWNXxzZ8/3zidTnP06NEL7sPm/P2cKnkCa+fOnT3ahg4dWuoE1ot5TlSq1mrb0mVq3759ZvPmze7LVzdv3mw2b97scRlr+/btzdKlS923p02bZoKDg82yZcvM559/bgYNGlTmpb3du3c3n3zyiVm/fr1p166dtUt7K6rj4MGDpn379uaTTz7xWG/nzp3G4XCYlStXltrmO++8Y+bMmWO++OILs3PnTvPXv/7VBAQEmNTU1Es+nvN5O75du3aZqVOnmo0bN5o9e/aYZcuWmdatW5t+/fq51ym5tPfXv/612bJli1m1apVp1qyZ1Ut7vRljXl6eiY2NNV26dDG7du3yuJzw3Llzxhh7c7hw4ULjdDrNggULzLZt28yDDz5ogoOD3VcuDRs2zEyYMMHd/8MPPzT16tUzM2fONNu3bzeTJk0q89LeC70ea5K3Y5w2bZrx8/MzS5Ys8ZirkvegkydPmvHjx5usrCyzZ88e895775lf/epXpl27djUejqsyvilTpph3333X7N6922RnZ5shQ4YYf39/s3XrVnefy2kOvR1fiT59+piEhIRS7Zfb/J08edL9OSfJzJo1y2zevNns27fPGGPMhAkTzLBhw9z9Sy7tffzxx8327dtNRkZGmZf2VvSYVYc6H0YSExONpFLL2rVr3X30//8eQ4ni4mLz1FNPmdDQUON0Os2NN95oduzY4bHdY8eOmaFDh5pGjRqZwMBAk5SU5BFwasqF6tizZ0+p8RpjTEpKiomIiDBFRUWltrly5UoTFRVlGjVqZBo2bGi6detmZs+eXWbfS83b8e3fv9/069fPNG3a1DidTtO2bVvz+OOPe/zOiDHG7N2719xyyy2mQYMGJiQkxIwbN87jstia5O0Y165dW+ZzWpLZs2ePMcbuHL744ovmqquuMn5+fiYmJsZ8/PHH7vv69+9vEhMTPfovXrzYXHPNNcbPz89ce+21Zvny5R73V+b1WNO8GWOrVq3KnKtJkyYZY4w5ffq0+fWvf22aNWtm6tevb1q1amVGjx5drW/03vJmfH/4wx/cfUNDQ82tt95qNm3a5LG9y20OvX2OfvXVV0aSWb16daltXW7zV977Q8mYEhMTTf/+/UutExUVZfz8/Ezr1q09Pg9LVPSYVQeHMTV8vSYAAMDP8DsjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArPr/ABe1qcGpyxpNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_who_won(board_history: np.ndarray) -> np.ndarray:\n",
    "    who_won = evaluate_who_won(board_history[-1])\n",
    "    return who_won\n",
    "\n",
    "\n",
    "plt.title(\"Histogram over the win distribtuion\")\n",
    "plt.hist(calculate_who_won(_board_history), density=True, bins=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def history_changed(board_history: np.ndarray) -> np.ndarray:\n",
    "    return ~np.all(\n",
    "        np.roll(board_history, shift=1, axis=0) == board_history, axis=(2, 3)\n",
    "    )\n",
    "\n",
    "\n",
    "plt.title(\"Share of turns skipped\")\n",
    "plt.plot(1 - np.mean(history_changed(_board_history), axis=1))\n",
    "# plt.yscale('log',base=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_gamma_table(board_history, gamma_value: float):\n",
    "    unchanged = history_changed(board_history)\n",
    "    gamma_values = np.ones_like(unchanged, dtype=float)\n",
    "    gamma_values[unchanged] = gamma_value\n",
    "    return gamma_values\n",
    "\n",
    "\n",
    "get_gamma_table(_board_history, 0.8).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06513542, 0.02282552, 0.08712565, 0.05031332, 0.1214854 ,\n",
       "       0.06314092, 0.14037841, 0.0759323 , 0.16290323, 0.08841437,\n",
       "       0.18861413, 0.10899313, 0.19318856, 0.10964757, 0.20731361,\n",
       "       0.13148691, 0.22510605, 0.13292382, 0.23539045, 0.12585808,\n",
       "       0.23848829, 0.15176572, 0.26641954, 0.17761089, 0.28060736,\n",
       "       0.16112694, 0.31286326, 0.17623532, 0.28714693, 0.16549503,\n",
       "       0.33439531, 0.19199073, 0.29858216, 0.20875418, 0.33700629,\n",
       "       0.1993395 , 0.36539974, 0.24731134, 0.36773293, 0.24404735,\n",
       "       0.42837075, 0.28564118, 0.41564522, 0.28406984, 0.41368105,\n",
       "       0.2341238 , 0.39596409, 0.26595149, 0.39103311, 0.38067557,\n",
       "       0.47846166, 0.30745852, 0.4819794 , 0.38419044, 0.5611122 ,\n",
       "       0.524575  , 0.546425  , 0.466925  , 0.601625  , 0.570625  ,\n",
       "       0.570625  , 0.35625   , 0.36875   , 0.36875   , 0.38125   ,\n",
       "       0.38125   , 0.38125   , 0.38125   , 0.38125   , 0.39715854])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calculate_q_reword(\n",
    "    board_history: np.ndarray,\n",
    "    who_won_fraction: float = 0.2,\n",
    "    final_score_fraction=0.2,\n",
    "    gamma=0.8,\n",
    ") -> np.ndarray:\n",
    "    assert who_won_fraction + final_score_fraction <= 1\n",
    "    assert final_score_fraction >= 0\n",
    "    assert who_won_fraction >= 0\n",
    "\n",
    "    gama_table = get_gamma_table(board_history, gamma)\n",
    "    combined_score = np.zeros_like(gama_table)\n",
    "    combined_score += calculate_direct_score(board_history) * (\n",
    "        1 - who_won_fraction + final_score_fraction\n",
    "    )\n",
    "    combined_score[-1] += (\n",
    "        calculate_final_evaluation_for_history(board_history) * final_score_fraction\n",
    "    )\n",
    "    combined_score[-1] += calculate_who_won(board_history) * who_won_fraction\n",
    "    for turn in range(SIMULATE_TURNS - 1, -1, -1):\n",
    "        values = gama_table[turn] * combined_score[turn]\n",
    "        combined_score[turn - 1] += values\n",
    "\n",
    "    return combined_score\n",
    "\n",
    "\n",
    "np.max(calculate_q_reword(_board_history, gamma=0.8), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'rewords' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[152], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mrewords\u001B[49m\n\u001B[0;32m      2\u001B[0m evaluate_boards(boards)\u001B[38;5;241m.\u001B[39mshape\n",
      "\u001B[1;31mNameError\u001B[0m: name 'rewords' is not defined"
     ]
    }
   ],
   "source": [
    "rewords\n",
    "evaluate_boards(boards).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 1000\n",
    "\n",
    "\n",
    "class DQLNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(BATCH_SIZE, 64)\n",
    "        self.fc2 = nn.Linear(BATCH_SIZE, 64)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if isinstance(x, np.ndarray):\n",
    "            x = torch.from_numpy(x).float()\n",
    "        x = torch.flatten(x, 1)\n",
    "        print(x)\n",
    "        x = self.fc1(x)\n",
    "        print(x)\n",
    "        x = F.relu(x)\n",
    "        print(x)\n",
    "        # x = self.dropout1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        # x = self.dropout2(x)\n",
    "        x = torch.reshape(x, (BATCH_SIZE, 8, 8))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DQLNet().fc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ones = np.ones((1000, 8, 8), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DQLNet().forward(ones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
    "torch.flatten(t)\n",
    "torch.tensor([1, 2, 3, 4, 5, 6, 7, 8])\n",
    "torch.flatten(t, start_dim=1)\n",
    "torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DQLearningWinner(GamePolicy):\n",
    "\n",
    "    # network =\n",
    "\n",
    "    @property\n",
    "    def policy_name(self):\n",
    "        return \"DQL-Winner\"\n",
    "\n",
    "    def _internal_policy(boards) -> np.ndarray:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DQLearningWinner(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_simple_rewords()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sources\n",
    "\n",
    "* Game rules and example board images [https://en.wikipedia.org/wiki/Reversi](https://en.wikipedia.org/wiki/Reversi)\n",
    "* Game rules and example game images [https://de.wikipedia.org/wiki/Othello_(Spiel)](https://de.wikipedia.org/wiki/Othello_(Spiel))\n",
    "* Game strategy examples [https://de.wikipedia.org/wiki/Computer-Othello](https://de.wikipedia.org/wiki/Computer-Othello)\n",
    "* Image for 8 directions [https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281](https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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