reversi/main.ipynb

{
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    "# 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."
   ]
  },
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   "cell_type": "markdown",
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   "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"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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",
    "\n",
    "![Othello game board example](reversi_example.png)\n",
    "\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"
   ]
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   "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": [
    "%load_ext blackcellmagic\n",
    "%load_ext line_profiler\n",
    "%load_ext memory_profiler"
   ]
  },
  {
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pickle\n",
    "import abc\n",
    "import itertools\n",
    "import os.path\n",
    "import warnings\n",
    "from abc import ABC\n",
    "from enum import Enum\n",
    "from typing import Final\n",
    "from IPython.display import clear_output\n",
    "from pathlib import Path\n",
    "import glob\n",
    "import copy\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from ipywidgets import interact\n",
    "from scipy.ndimage import binary_dilation\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "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": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `os.makdir` not found.\n"
     ]
    }
   ],
   "source": [
    "?os.makdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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\n",
    "TRINING_RESULT_PATH: Final[Path] = Path(\"training_data\")\n",
    "if not os.path.exists(TRINING_RESULT_PATH):\n",
    "    os.mkdir(TRINING_RESULT_PATH)"
   ]
  },
  {
   "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",
    "\n",
    "![8-directions.png](8-directions.png \"Offset in 8 directions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1,  1],\n",
       "       [ 1, -1]])"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "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": 7,
     "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": 8,
   "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": 9,
   "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(\n",
    "    board: np.ndarray,\n",
    "    action: np.ndarray | None = None,\n",
    "    ax=None,\n",
    ") -> 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(\"#0f6b28\")\n",
    "    if action is not None:\n",
    "        ax.scatter(action[0], action[1], s=350 if plot_all else 200, c=\"red\")\n",
    "    for x_pos, y_pos in itertools.product(range(BOARD_SIZE), range(BOARD_SIZE)):\n",
    "        if board[x_pos, y_pos] == PLAYER:\n",
    "            color = \"white\"\n",
    "        elif board[x_pos, y_pos] == ENEMY:\n",
    "            color = \"black\"\n",
    "        else:\n",
    "            continue\n",
    "        ax.scatter(x_pos, y_pos, s=280 if plot_all else 140, 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",
    "    ax.set_xlabel(\n",
    "        f\"W{np.sum(board == ENEMY)} / {np.sum(board == 0)} / B{np.sum(board == PLAYER)}\"\n",
    "    )\n",
    "    if plot_all:\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "plot_othello_board(get_new_games(1)[0], action=np.array([3, 3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_othello_boards(boards: np.ndarray, actions: np.ndarray | None = None) -> 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",
    "    if actions is not None:\n",
    "        assert len(actions.shape) == 2\n",
    "        assert actions.shape[1] == 2\n",
    "        assert boards.shape[0] == actions.shape[0]\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",
    "            action = actions[game_index] if actions is not None else None\n",
    "            plot_othello_board(boards[game_index], action=action, ax=ax)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_duplicate_boards(\n",
    "    boards: np.ndarray, actions: np.ndarray | None\n",
    ") -> tuple[np.ndarray, np.ndarray | None]:\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",
    "    non_duplicates = ~np.all(boards == np.roll(boards, axis=0, shift=1), axis=(1, 2))\n",
    "    return (\n",
    "        boards[non_duplicates],\n",
    "        np.roll(actions, axis=0, shift=1)[non_duplicates]\n",
    "        if actions is not None\n",
    "        else None,\n",
    "    )"
   ]
  },
  {
   "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": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 1],\n",
       "        [1, 0, 1],\n",
       "        [1, 1, 1]]])"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.99 ms ± 557 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "985 ms ± 41.5 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": 13,
     "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": 14,
   "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "213 µs ± 7.62 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "38 µs ± 1.99 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
      "38 µs ± 1.92 µs 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": 17,
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.2 ms ± 2.16 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",
    "    action=np.array([2, 3]),\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": 19,
   "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": 20,
   "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": 21,
   "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",
    "        poss_turns = boards == 0  # checks where fields are empty.\n",
    "        poss_turns &= binary_dilation(boards == -1, SURROUNDING)\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",
    "                    np.array(\n",
    "                        list(\n",
    "                            _recursive_steps(boards[game, :, :], direction, position)\n",
    "                            for direction in DIRECTIONS\n",
    "                        )\n",
    "                    )\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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.18 s ± 36.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1.08 s ± 32.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
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/IRXRTwBMivlC50gfd00tULgppMhVV7VetLO5OboHNjdL9fWKTJumcFNId00tSG5QAAAAS/D+CQCA6LGUQpf25GXqzqkF0s6dilx6qbR/f+sfhMOdP+DE9/fvb73/rl26c2qB9uRlehMYAADAMN4/AQAQPZZSOK0XRufojmkFat6zWy3njpVmz5bKyzu/c3m5NHu2Ws4dq+Y9u/WNaYV6YXSOh2kBAADM4/0TAADRiemaUkhPz4/O1aaze2l+WbUmLlum5t8sU2D8BVJJiZSTI9XVSTt2qLmiXAFXWlfYS3dPGc6/8AEAgLTF+ycAAM6MpRSisicvU9d+cZjGHz6uWdtqdNm+bRqzpVyZEakpQ9reP6h15+XpmbF5XJQTAABAvH8CAOBMWEohJhX5WarIH9T2teO6ch3HYCIAAAC78f4JAIDOcU0pdAtvqAAAAGLD+ycAAFo5ruu6Xg6sq6tTbm5u6/A+3p+o5R5tkVxJjuT0NnOiGBnIYFMG0/MlyW1skSTV1tYqJ8fcxV1N95NkyfHgZ5IMZGifwYKOop/IYMt8MliWgX6SZMmxIAMZLJlvTYYo+8nor++dCGlmuOH5ZCCDbRlMz7eM8dfChuNhOoPp+WQgg6WMvw42HAsymJ9PBrsyWML462DDsSADGWyZb0uGMzC6lOJMKTKQwXwG0/MlO4uSf+kzl8H0fDKQoUMGyzqKfkrvDKbnk8GyDPSTJEuOBRnIYMl8azJE2U/mllK9fcqZXez52Lqlu+Q2tsjp7TcynwxksC2D6fmSVLtkp3Q0bGR2pwz1k2TH8TCdwfR8MpDhVFZ1FP2U9hlMzyeDXRnop1Y2HAsykMGW+bZkiLafuNA5AAAAAAAAPMdSCgAAAAAAAJ5jKQUAAAAAAADPGb3QOVJX6ZCxGjVwuPpm9VHD8UbtPLhHW/ZtS5v5AOwVPnxc4SNNUnNECmTI1y9TvvwsTzPQUQA6Y0M32JABgH14/wRTWEohalmBoG6Y8DndMekWjS86t8OfV1S+ryfeelovbHpFx5tDPW4+AHu5LRE176xTaMsRRf7a8b//jLOCCpb2U2BUjhx/ck4SpqMAdMaGbrAhAwD78P4JNmAphaiMPPscvTjnKRX1L1TEjXR6n/MGl2j+rId0//Q5+vz8r+nDwx/1mPkA7BWuaVLjyo/l1nf9sbORv4Z0rOyAjr/7V/W5bqh8eZkJzUBHAeiMDd1gQwYA9uH9E2zBNaVwRiPPPkdv3LdchXkD5TiOfBm+Tu/ny/DJcRwV5g3Um99doRH5w3rEfAD2Ctc0qeH5vXIbun5DdTK3oUUNz+9VuKYpYRnoKACdsaEbbMgAwD68f4JNurWU+tnPfibHcfTtb387QXFgm6xAUC/OeUrZwT7y+6I7sc7v8ys72Ecv3bVIWYFgSs9H6qKfej63JaLGlR9LTRHJjfZBkppaH+e2dP4vcrGgoxAP+qnns6EbbMiA1ERH9Wy8f4Jt4l5Kvfvuu/r1r3+t0tLSROaBZW6Y8DkV9S+MuixO8Pv8GjpgsL540fSUno/URD+lh+adda2nnEf7huoEV3LrW9S8s67bGegoxIp+Sg82dIMNGZB66Kiej/dPsE1cS6mGhgbNmjVLTz75pPr165foTLDIHZNu6fL3e88kHAnrjkm3pPR8pB76KX2Ethwx+niJjkJs6Kf0YUM32JABqYWOSg+8f4Jt4lpKzZkzR9dee60++9nPJjoPLFI6ZKzGF53b5e/3nokvw6cLho5T6ZCxKTkfqYl+Sg/hw8c7/ZSYWET+GlL48PG4H09HIVb0U3qwoRtsyIDUQ0f1fLx/go1i/vS95cuX67333tO7774b1f1DoZBCoU9+8Ovqun+6H7wxauDwhDxP8cDh2rJvW8rNR+qhn9JH+EhiLrQZrmmSLz8rrsfSUYgF/ZQ+bOgGGzIgtcTSUfRT6uL9E2wU05lSlZWVuvfee/XMM88oKyu6H8J58+YpNze37VZUVBRXUHivb1afhDxPdpzPY3o+Ugv9lGaau3+RTUmtF/mMEx2FaNFP6cWGbrAhA1JHrB1FP6Uw3j/BQjEtpTZt2qRDhw7poosukt/vl9/v11tvvaXHHntMfr9f4XC4w2Pmzp2r2tratltlZWXCwiO5Go43JuR56uN8HtPzkVropzQT6NaHx34iM/7noaMQLfopvdjQDTZkQOqItaPopxTG+ydYKKZf37vyyiu1devWdt+77bbbNGbMGN1///3y+Tr+XmgwGFQwyEc2pqKdB/ck5nkOfJiS85Fa6Kf04uuXmZjnyYv/eegoRIt+Si82dIMNGZA6Yu0o+il18f4JNoppKZWdna3zzjuv3ff69OmjAQMGdPg+Ut+WfdtUUfm+zhtcEteF6MKRsLbu266t+7en5HykFvopvfjys5RxVrBbF+vMOCsY9/UQJDoK0aOf0osN3WBDBqQOOip98P4JNkrQ+XvoqZ5462llOPH9mPgyfHriradTej4AewVLu/dx1d19vERHAeicDd1gQwYA9uH9E2zT7aXUmjVr9OijjyYgCmz0wqZXVPn3KrWEW2J6XEu4RR//bb9+996qlJ6P1EY/9WyBUTlysv2SE+MDHcnJ9iswKqfbGegoxIt+6tls6AYbMiB10VE9F++fYBvOlMJpHW8O6QsLvq76UGPUpdESblF9qFGfn/81HW+O/9RQG+YDsJfjz1Cf64a2Xmwz2jdWjqTM1sc5/u7/TyAdBaAzNnSDDRkA2If3T7ANSymc0e5De3XlIzerquagpNbf4+3Mie9X1RzUlY/crA8Pf9Qj5gOwly8vU31vPEdO3+gukej09avvjed06wKdp6KjAHTGhm6wIQMA+/D+CTaJ6ULnSF8fHv5In/rptfriRdN1x6RbdMHQcR3us3Xfdj3x1tP63XurEr69Nj0fgL18eZnK/j8j1LyzTqEtRzq9eGfGWUEFS/u1nrKegH/hOxUdBaAzNnSDDRkA2If3T7AFSylE7XhzSM+uf0nPrn9JpUPGqnjgcGVn9VH98UbtOrhHW/Zt69HzAdjL8Wcoc2yeMsfmKXz4uMI1TVJTRMrMkC8vs1ufEhMtOgpAZ2zoBhsyALAP759gA5ZSiMuWfduMFoTp+QDs5cvP8uRN1OnQUQA6Y0M32JABgH14/wRTuKYUAAAAAAAAPMdSCgAAAAAAAJ5jKQUAAAAAAADPOa7rul4OrKurU25ubuvwPt5f0so92iK5khzJ6W3mklpkIINNGUzPlyS3sUWSVFtbq5ycHCMZJPP9JFlyPPiZJAMZ2mewoKPoJzLYMp8MlmWgnyRZcizIQAZL5luTIcp+Mnqh8xMhzQw3PJ8MZLAtg+n5ljH+WthwPExnMD2fDGSwlPHXwYZjQQbz88lgVwZLGH8dbDgWZCCDLfNtyXAGRpdSnClFBjKYz2B6vmRnUfIvfeYymJ5PBjJ0yGBZR9FP6Z3B9HwyWJaBfpJkybEgAxksmW9Nhij7ydxSqrdPObOLPR9bt3SX3MYWOb39RuaTgQy2ZTA9X5Jql+yUjoaNzO6UoX6S7DgepjOYnk8GMpzKqo6in9I+g+n5ZLArA/3UyoZjQQYy2DLflgzR9hMXOgcAoIdyvL1sJAAAABATo7++BwAAEmf84eOata1Gl1cdU8mRkDIjUlOGtKNfUGsLe+mZsXmqyM8yHRMAAACQxFIKcSodMlajBg5X36w+ajjeqJ0H92jLvm2ezQ8fPq7wkSapOSIFMuTrlykf/48WAJnvJxMZhtc0aX5ZtSZWH1OzIwXGXyBNGyNlZyuzvl7nb9+uMRXlumNrjd4u6KW7phZoT15m0vIA6JwN/cR7KACdsaGfbMgA77GUQtSyAkHdMOFzumPSLRpfdG6HP6+ofF9PvPW0Xtj0io43hxI+322JqHlnnUJbjijy147Pn3FWUMHSfgqMypHj5zdTgXRiup9MZrjxg1otKDsgX2ZQmj1bgXvukS68sMP9Aps3S489pktXLNe65Xt059QCvTDazMeHA+nEhn7iPRSAztjQTzZkgFn8rw6iMvLsc7ThB/9P82c9pPMGl3R6n/MGl2j+rIe04Qf/TyPyhyV0frimSfXPfqhjZQc6fTMlSZG/hnSs7IDqn/1Q4ZqmhM4HYC/T/WQyw40f1OqJ16sVGFEs//vbpMWLpdLSzu9cWiotXiz/+9sUGD5ST75epRs/qE1IDgCds6GfeA8FoDM29JMNGWAeSymc0cizz9Eb9y1XYd5AOY4jX4av0/v5MnxyHEeFeQP15ndXJKw0wjVNanh+r9yG6D5S0m1oUcPze3lTBaQB0/1kMsOImiYtKDsgjRqljHXrpMGDJceRfJ3Pl8/X+ueDBytj/XqpuFgLyg5oOF0JJIUN/cR7KACdsaGfbMgAO7CUwmllBYJ6cc5Tyg72kd8X3W97+n1+ZQf76KW7FikrEOzWfLclosaVH0tNESnaD5FyJTW1Ps5tiXRrPgB7me4n0xkeL6tWRmZQGa+9JmVnS4FAdA8MBKTsbGW8/rp8mUHNL6uOOwOAztnQT7yHAtAZG/rJhgywR0xLqR/96EdyHKfdbcyYMcnKBgvcMOFzKupfGHVZnOD3+TV0wGB98aLp3ZrfvLNObn1L9G+mTnAlt75FzTvrujUfqYN+Sj+m+8lkhvGHjmti9TEFbv6yNGxY9AupEwIB6Zxz5P/SzZpYfUzjDx+PKweiR0elFxv6ifdQiBb9lF5s6CcbMsAeMZ8pNW7cOFVXV7fd/vznPycjFyxxx6RbFHHj+5eycCSsOybd0q35oS1HjD4eqYV+Si+m+8lkhlnba9TsSLrnHikS59kM4bB0991qdqRZ22riew7EhI5KHzb0E++hEAv6KX3Y0E82ZIA9Yv70Pb/fr0GDBiUjCyxTOmRsp5+AEC1fhk8XDB2n0iFj4/ooz/Dh411ekDNakb+GFD58nI86ThP0U/ow3U+mM1xedUyB8Rd0+il70QfwSRMmKDD+Al3Gxy17go5KDzb0E++hECv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      "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",
    "_turn_result = single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "plot_othello_boards(_turn_result[0][:8], _turn_result[1][:8])\n",
    "del _turn_result"
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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69PyRFXVatLJck8uPqN6TMieeIk0fJ+XlKau6Widv3apxGzdo3uYKvTI4R9dNG6ydhVkpzQSgLdv95EKGyIFaRQ7WSfVRKTNDgT5ZCrBYDlhnuxtcyUBHAe5xoRtcyEA/+Y9FKcQsOzOkWZPO17wpczSx+MQ2399Y+pbuWHWPHln3pGrrwz1q/mVvV2rxyn0KZIWkuXOVecMN0qmntrld5vr10u9/rzMffECrH9ipa6cN1iMn5Cc1C4C2bPeTCxlMQ1T126sU3nRQ0Q/abj+jf0ihkj7KHJMvL8i79wG/2O4GVzLQUYB7XOgGFzLQT3axKIWYHD9whFbM/6OK+xYpaqLt3mbCkLFaNPsm/XDGfF2y6Bq9e+C9HjH/srcrdcez5dKYMcr429+k4cOlaPsZVFIi3XWXgj/9qTKmT9edz+6QJ6OHTyhIShYAbdnuJxcyRCrqVPP4bpnqhg5vE/0grCMr96n29Q+Ue9EwBTiSE0g5293gSgY6CnCPC93gQgb6yT6W+dCp4weO0HM3PqCiwkHyPE+BjEC7twtkBOR5nooKB+n57z+oUQOGd/v5oyrqtHjlvsYFqdWrpSFDJM+TAu1nUCDQ+P0hQ5SxZo00erQWr9ynkRV1Xc4CoC3b/eRChkhFnQ49vEvmUMcvployhxp06OFditBLQErZ7gZXMtBRgHtc6AYXMtBPbohrUepnP/uZPM9rdRk3blyqssEB2ZkhrZj/R+WFchUMxHZgXTAQVF4oV49dt1TZmaFuPf+2leXKyAo1HiGVlydlZsZ2x8xMKS9PGc8+q0BWSItWlncpBzpHP6Uf2/3gQgbTEFXN47uluqgU6weBGkl1jfczDR0c9Ymko6PSi+1ucCUDHdU90E/pxYVucCED/eSOuI+UOumkk1ReXt58efnll1ORC46YNel8FfctirksmgQDQQ3rN0SXnjaj286f+H6tJpcfUeYVX258y16sC1JNMjOlESMU/NIVmlx+RBMP1CacBbGhn9KL7X5yIUP99qrGw81jfTHVxEimukH126u6NB/xoaPSh+1ucCUDHdV90E/pw4VucCED/eSOuBelgsGgjjvuuOZL//79U5ELjpg3ZU6H7+/tTCQa0bwpc7rt/NlbK1TvSbrhho7PIdVpiIh0/fWq96TZWyoSzoLY0E/pxXY/uZAhvOmg1fsjPnRU+rDdDa5koKO6D/opfbjQDS5koJ/cEfei1Pbt21VUVKRRo0Zp9uzZ2r17dypywQElQ8drYvGJHb6/tzOBjIBOGXaSSoaO75bzzyo7osyJpzR+yl5H55DqNERAmjRJmRNP0afLjyS2DcSMfkoftvvBhQyRA7XtfkJMPKIfhBXhKE7f0FHpwXY3uJKBjupe6Kf04EI3uJCBfnJLXItSZ555ppYvX66nn35aS5Ys0c6dO/XZz35W1dXVHd4nHA6rqqqq1QXdw5hBI5OyndEJbsf2/LEHw1Ky3k8/dqzGfZSajzBFI/opvdjuBxcyRA4m5ySbnKzTH/F2FP3UfdnuBlcy0FHdB/2UPlzoBhcy0E9uietNnDNmfPLezZKSEp155pkaPny4HnroIV1zzTXt3mfhwoX6+c9/3rWUsKJ3dm5StpOX4HZszveMUVZUjSc3T4b8fGVFG7drPC8520Qr9FN6sd1PTmSoT9IJNus4Uacf4u0o+qn7st4NjmSgo7oP+il9uNANLmSgn9wS99v3WiosLNQJJ5ygHTt2dHibBQsWqLKysvlSWlralZHw0aHamqRspzrB7dicbzxPdRmSjnGUTVyqqlSXIRakfEQ/9Wy2+8mJDJld+hX+iawkbQdx6ayj6Kfuy3o3OJKBjuq+6Keey4VucCED/eSWLj2Khw4d0jvvvKPBgwd3eJtQKKT8/PxWF3QP2/fvTM529r3bLedv6xOStm5NSgZt3aqtfbv+0aWIHf3Us9nuBxcyBPpkJWV+oDA520F8Ouso+qn7st0NrmSgo7ov+qnncqEbXMhAP7klrkWpG2+8UatWrdKuXbv097//XV/84hcVCAT0la98JVX5YNGmPVu0sfQtRaKRhO4fiUa0Yfc/tHlvYgs7tue/WpSj+o0bpPXrGz9FL6EQEWndOtVv2qjVg3MS2wZiQj+lF9v94EKGwIBsZfTv2mJ3Rv+QAgOyu7QNxIaOSh+2u8GVDHRU90E/pQ8XusGFDPSTW+JalNqzZ4++8pWvaOzYsfrSl76kfv36afXq1RowYECq8sGyO1bdowwvsQPqAhkB3bHqnm47/97xhco0kn7/eykjwYMKAwHpttuUaRq3h9Shn9KP7X5yIUOopI/V+yN2dFR6sd0NrmSgo7oH+im9uNANLmSgn9wR10/CAw88oLKyMoXDYe3Zs0cPPPCAjj/++FRlgwMeWfekSj8qU0OkIa77NUQatPvDvXr0jae67fyNA7L1yuAcNTz4gPTee1J9fXwbqK+Xdu1Sw0MP6pXBOdrISnpK0U/px3Y/uZAhc0y+vLygFO/p6jzJywsqcwxvufALHZVebHeDKxnoqO6BfkovLnSDCxnoJ3dwZi4cU219WF9c/HVVh2tiLo2GSIOqwzW6ZNE1qq0Pd+v5100brEhdWNFzz2086XmsC1P19VJ1taLTpytSF9Z10zo+rxGAxNjuBxcyeMEM5V40rPFEm7G+qPIkZTXezwvyMgBIBdvd4EoGOgpwjwvd4EIG+skdPJLo1Dvv79I5t1yhsor9ktTh+3+bri+r2K9zbrlC7x54r9vP31mYpWunDZa2b1f0zDOlvXs/HtbBe6Cbrt+7t/H2O3bo2mmDtZOT4AEpYbufXMgQKMxS78tGyOsdjOn2Xu+gel82gpNzAilmuxtcyUBHAe5xoRtcyEA/uSG2Rx9p790D7+mMX12gS0+boXlT5uiUYSe1uc3mPVt1x6p79OgbTyVl9dqV+Y+ckC9PRotXvqPAieMV/NIV0vXXS5Mmtb3xhg3Sbbep4aEHFakL69rpRXrkBA7tBFLJdj+5kCFQmKW8r45S/fYqhTcdVPSDttvP6B9SqKRP4+Hq/HUP8IXtbnAlAx0FuMeFbnAhA/1kH4tSiFltfVj3rXlM9615TCVDx2v0oJHKy85VdW2NduzfqU17tvTY+Q+fUKB1A3O0aGW5Jt99t+r/dLcyJ54ijR0r5edLVVXStm2q37hBmUZaXZSj66eO5AgpwCe2+8mFDF4wQ1njC5U1vlCRA7WKVNRJdVEpK0OBwiw+IQawxHY3uJKBjgLc40I3uJCBfrKLRSkkZNOeLb4UhEvzdxZm6YJLh2vigVrN3lKhT+/ZonGbNigrKtVlSFv7hrR6QqHuHV/ISc0Bi2z3kwsZAgOyeQEFOMh2N7iSgY4C3ONCN7iQgX7yH4tSQJw2DsjWxgHHNX/tGSPjxfuxDQAAAAAApDfeEAl0EQtSAAAAAADEj0UpAAAAAAAA+M4zxhg/B1ZVVamgoKBxeK7/7x40hxskI8mTvF523r1IBjK4lMH2fEkyNQ2SpMrKSuXn2/u0Qtv9JDmyP/iZJAMZWmdwoKPoJzK4Mp8MjmWgnyQ5si/IQAZH5juTIcZ+snpOqaaQdoZbnk8GMriWwfZ8x1h/LFzYH7Yz2J5PBjI4yvrj4MK+IIP9+WRwK4MjrD8OLuwLMpDBlfmuZOiE1UUpjpQiAxnsZ7A9X3KzKPlLn70MtueTgQxtMjjWUfRTemewPZ8MjmWgnyQ5si/IQAZH5juTIcZ+srco1Sug/LmjfR9btXyHTE2DvF5BK/PJQAbXMtieL0mVy7ZLhyNWZrfLUj9JbuwP2xlszycDGY7mVEfRT2mfwfZ8MriVgX5q5MK+IAMZXJnvSoZY+4kTnQMAAAAAAMB3LEoBAICU8fz9PBUAABLG7yzAf1bPKQUAAHqWiQdqNXtLhc4qO6KxB8PKikp1GdK2PiG9WpSje8cXauOAbNsxAQDgdxbgABaluqGSoeM1ZtBI9c7O1aHaGm3fv1Ob9mzxNUPkQK0iB+uk+qiUmaFAnywFKGwg7dFP6WtkRZ0WrSzX5PIjqvekN430iKRqSXlRadyHYV31UVjzNlfolcE5um7aYO0szLIdG2mEfkJL7Iv05trvLBd+Hl3IgEbpti9YlOomsjNDmjXpfM2bMkcTi09s8/2NpW/pjlX36JF1T6q2PpySDKYhqvrtVQpvOqjoB21nZPQPKVTSR5lj8uUFeWcokC7oJ1z2dqUWr9ynuojRMkm/N9KGdm53ipFukHRZ+RGtfmCnrp02WI+ckO9vWKQV+gktsS8gufM7y4WfRxcyoFE67wsWpbqB4weO0Ir5f1Rx3yJFTbTd20wYMlaLZt+kH86Yr0sWXaN3D7yX1AyRijrVPL5bprrjj3WMfhDWkZX7VPv6B8q9aJgC/AUc6PHoJ1z2dqXueLZcb0s6T9J76viElZskXS3p55KeiRjd+WyZPBk9fEKBT2mRTugntMS+gOTO7ywXfh5dyIBG6b4vetYSWw90/MAReu7GB1RUOEie5ymQEWj3doGMgDzPU1HhID3//Qc1asDwpGWIVNTp0MO7ZA51/CRpyRxq0KGHdylSUZe0DADcQz9hVEWdFq/cp7clfUbSno+vb/9//z+5fo+ksyRtl7R45T6NZH8gyegntMS+gOTO7ywXfh5dyIBG7IsEFqX27t2rOXPmqF+/fsrJydHJJ5+stWvXpiJb2svODGnF/D8qL5SrYCC2g9qCgaDyQrl67Lqlys4MdTmDaYiq5vHdUl1UivXDKIykusb7mYaOah5IPvrJP/QTJOm2leUKR4zOk1QlKRLj/SIf3/4LkuoiRotWlqcqolPoKH/QT2iJfRGbdOgnF35nufDz6EIGNGJfNIprUergwYOaPHmyMjMz9dRTT+mtt97Sb3/7W/Xp0ydV+dLarEnnq7hvUcwvqJoEA0EN6zdEl542o8sZ6rdXNR5GGO+noxrJVDeofntVlzMAsaCf/EU/YeL7tZpcfkSPqPHtD7G+uG8S+fh+j0iaXH5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pwxmTWrgd2/MlFZeWRCXCsShtB42jn+KMA/1gOwP9FDvopzhDP0mio2JJpB1FP8UwB7rBhQz0k1sieqXUhg0bdOjQIZ1zzjm13wuFQnr99dc1e/ZslZWVye+v+77MYDCoYJCPS4xF/s5J0dlORsu2Y3u+JOUd3BWVDHkHPorKdtA4+im+uNAPtjPQT7GDfoovtrvBlQx0VOyItKPop9jlQje4kIF+cktEi1KXXnqptmzZUud7t9xyi4YNG6Z77rmn3hMqxDZ/92QldAu26kJ0Cd2C8ncP71+FXZsvSZv3bdOmvVt1Rp+hLboQXqgqpC37tmvLJ9tbnAHhoZ/iiwv9YDsD/RQ76Kf4YrsbXMlAR8UOOip+uNANLmSgn9wS0WveUlNTdcYZZ9S5paSkqGvXrjrjjDPaKiMsCua27pM3Yv3xkjRn9QIl+Fr28lB/gl9zVi9odQY0j36KPy70g+0M9FNsoJ/ij+1ucCUDHRUb6Kj44kI3uJCBfnJHlN7QifYqcXCafKkByRfhA32SLzWgxMFpMT1fkhZveEl7j+xXZagyosdVhiq157NP9Nd3l7U6A4D6XOgH2xnoJ8BNtrvBlQx0FOAeF7rBhQz0kztavSi1atUqPfTQQ1GIAhf5AglKuaZf9YXkwi0Nn6Sk6sf5Aq07xWzPl6TSijJd//B3dKysJOzSqgxV6lhZia6bPU2lFS1/aSpah35q31zoB9sZ6KfYRT+1b7a7wZUMdFTsoqPaLxe6wYUM9JM7eKUUmuXPSFKnGwfI1ym8S5D5OgXU6cYBrbr4nEvzJenDQ7t16YM3aX/BQUnV7yNuSM339xcc1KUP3qSPDn8ctQwA6nOhH2xnoJ8AN9nuBlcy0FGAe1zoBhcy0E9uiOhC54hf/owkpX5jkCryilS2+WiDF6ZL6BZUMLdz9csxo7B67dJ8Sfro8Me64BdX6YZzJmj6mMk6q9/p9e6zZd92zVm9QH99dxmr54BHXOgH2xnoJ8BNtrvBlQx0FOAeF7rBhQz0k30sSiFsvkCCknIylJSTodDhUoUKyqXyKikpQf6MpFZ9AkIszJeqX+a5cN1zWrjuOeX2zVF2z4FKTU7RsdIS7Ty4S5v3bWvzDADqc6EfbGegnwA32e4GVzLQUYB7XOgGFzLQT3axKIUW8XdP9qQgXJ0vVX+UKAUFuMeFfrCdgX4C3GS7G1zJQEcB7nGhG1zIQD95j2tKAQAAAAAAwHMsSgEAAAAAAMBzLEoBAAAAAADAcyxKAQAAAAAAwHM+Y4zxcmBRUZHS09Orh6d4f511c7xSMpJ8kq+jneu8k4EMLmWwPV+STEmlJKmwsFBpaWlWMkj2+0ly5HhwTpKBDHUzONBR9BMZXJlPBscy0E+SHDkWZCCDI/OdyRBmP1n99L2akHaGW55PBjK4lsH2fMdY3xcuHA/bGWzPJwMZHGV9P7hwLMhgfz4Z3MrgCOv7wYVjQQYyuDLflQzNsLooxSulyEAG+xlsz5fcLEr+pc9eBtvzyUCGehkc6yj6Kb4z2J5PBscy0E+SHDkWZCCDI/OdyRBmP9lblOroV9rUbM/HFs3fKVNSKV/HgJX5ZCCDaxlsz5ekwnl50vGQldkNstRPkhvHw3YG2/PJQIZTOdVR9FPcZ7A9nwxuZaCfqrlwLMhABlfmu5Ih3H7iQucAAAAAAADwHItSAAAAAAAA8ByLUgAAAAAAAPCc1QudI3bl9s3R4J4D1Sk5RcWlJco7uEub922Lm/mSFDpcqtDRcqmiSkpMkL9zkvzdkz3NAKA++sH+fAANo5+qubAfANTlQje4kIF+8h6LUghbcmJQE8+9UtPHTNaIrOH1fr5p71bNWb1Aize8pNKKsnY3X5JMZZUq8opUtvmoqj6tPyOhW1DB3M5KHJwmX4AXIgJeoR/szwfQMPqpmgv7AUBdLnSDCxnoJ7tYlEJYTusxQEtmPKasLpmqMlUN3ueMPkM1e9IDumfCDF03e5o+Ovxxu5kvSaGCcpUs3SNzrPGPtqz6tEwnVhxQ6fpPlXJNP/kzkqKaAUB99IP9+QAaRj9Vc2E/AKjLhW5wIQP9ZB//VIpmndZjgJb/YJEyM3rK5/PJn+Bv8H7+BL98Pp8yM3rqtR8+pUHd+7eL+VJ1YRY/u1umuPHCPJkprlTxs7sVKiiPWgYA9dEP9ucDaBj9VM2F/QCgLhe6wYUM9JMbIlqU+ulPfyqfz1fnNmzYsLbKBgckJwa1ZMZjSg2mKOAP74V1AX9AqcEUPXfHXCUnBmN6vlT9ktKSpXuk8irJhPsgSeXVjzOVDa+4I7rop/hDP9ifj/DRUfGFfqrmwn5A8+in+OJCN7iQgX5yR8SvlDr99NOVn59fe3vjjTfaIhccMfHcK5XVJTPsP9QaAX9A/br20Q3nTIjp+ZJUkVdU/ZLScAuzhpHMsUpV5BW1OgPCQz/FF/rB/nxEho6KH/RTNRf2A8JDP8UPF7rBhQz0kzsiXpQKBALq1atX7a1bt25tkQuOmD5mcqPvrW1OqCqk6WMmx/R8SSrbfNTq4xE++im+0A/2H4/I0FHxg36q5sJ+QHjop/jhQje4kIF+ckfEi1J5eXnKzMzUoEGDNGnSJO3Zs6ctcsEBuX1zNCJreKPvrW2OP8Gvs/qdrty+OTE5X6r+WNKGPgUiElWflil0uLRV20B46Kf4QT/Yn4/I0VHxgX6q5sJ+QPjop/jgQje4kIF+cktEi1IjR47U/Pnz9fLLL+uRRx7Rrl279OUvf1nHjh1r9DFlZWUqKiqqc0NsGNxzYFS2k93C7dieL0mho9G7EDHaFv0UX+gH+/MRmUg7in6KXfRTNRf2A8JDP8UPF7rBhQz0k1siegPlhAlfvG8yNzdXI0eOVP/+/fX0009r2rRpDT5m1qxZ+tnPfta6lLCiU3JKVLaT2sLt2J4vSaqI0kWAy7mYcFujn+IL/eDAfEQk0o6in2IX/VTNif2AsNBPccSBbnAhA/3klojfvneyjIwMDRkyRDt37mz0PjNnzlRhYWHtbe/eva0ZCQ8Vl5ZEZTvHWrgd2/MlSYmt+hP5QlKUtoOw0U/tG/3gwHy0SnMdRT/FLvqpmhP7AS1CP7VjDnSDCxnoJ7e06owoLi7Whx9+qN69ezd6n2AwqLS0tDo3xIa8g7uis50DH8XkfEnyd06KSgZ/RnS2g/DRT+0b/WB/PlqnuY6in2IX/VTNhf2AlqGf2i8XusGFDPSTWyJalPrBD36g1atXa/fu3Xrrrbd0/fXXy+/36+abb26rfLBo875t2rR3q0JVoRY9PlQV0sY9/9SWT7bH5HxJ8ndPVkK3YIsfL0kJ3YLyd09u1TbQPPopvtAP9ucjMnRU/KCfqrmwHxAe+il+uNANLmSgn9wS0aLUvn37dPPNN2vo0KH6+te/rq5du2rt2rXq3r17W+WDZXNWL1CCr2UvqPMn+DVn9YKYni9JwdzOVh+P8NBP8Yd+sP94hI+Oii/0UzUX9gOaRz/FFxe6wYUM9JM7IrrQ+aJFi9oqBxy1eMNLumfCDGVm9FTAH/7pUhmq1P6Cg/rru8tier4kJQ5OU+n6T2WKKyUTwQN9kq9TQImDeUmzF+in+EM/2J+P8NFR8YV+qubCfkDz6Kf44kI3uJCBfnIHVzdFk0orynT9w9/RsbISVYYqw3pMZahSx8pKdN3saSqtKIvp+ZLkCyQo5Zp+1RfT84X7IElJ1Y/zBfgzA9oC/WB/PoCG0U/VXNgPAOpyoRtcyEA/uYNno2jWh4d269IHb9L+goOS1Oh7b2u+v7/goC598CZ9dPjjdjFfqr6QXqcbB8jXKbxVdF+ngDrdOIALCANtjH6wPx9Aw+inai7sBwB1udANLmSgn9wQ0dv3EL8+OvyxLvjFVbrhnAmaPmayzup3er37bNm3XXNWL9Bf310W9ZVj2/Ol6uJM/cYgVeQVqWzzUVV9Wn9GQreggrmdlTg4jVcgAB6hH+zPB9Aw+qmaC/sBQF0udIMLGegn+1iUQthKK8q0cN1zWrjuOeX2zVF2z4FKTU7RsdIS7Ty4S5v3bWvX86Xql5om5WQoKSdDocOlChWUS+VVUlKC/BlJfIoVYAn9YH8+gIbRT9Vc2A8A6nKhG1zIQD/ZxaIUWmTzvm1W/zhtz5eqP86U/8gD3EM/2J8PoGH0UzUX9gOAulzoBhcy0E/e4/X7AAAAAAAA8ByLUgAAAAAAAPAci1IAAAAAAADwnM8YY7wcWFRUpPT09OrhKd5f0socr5SMJJ/k62jnklpkIINLGWzPlyRTUilJKiwsVFpampUMkv1+khw5HpyTZCBD3QwOdBT9RAZX5pPBsQz0kyRHjgUZyODIfGcyhNlPVi90XhPSznDL88lABtcy2J7vGOv7woXjYTuD7flkIIOjrO8HF44FGezPJ4NbGRxhfT+4cCzIQAZX5ruSoRlWF6V4pRQZyGA/g+35kptFyb/02ctgez4ZyFAvg2MdRT/Fdwbb88ngWAb6SZIjx4IMZHBkvjMZwuwne4tSHf1Km5rt+dii+TtlSirl6xiwMp8MZHAtg+35klQ4L086HrIyu0GW+kly43jYzmB7PhnIcCqnOop+ivsMtueTwa0M9FM1F44FGcjgynxXMoTbT1zoHAAAAAAAAJ5jUQoAgHbK5+1nmQAAAAARsXpNKQAAED0jDpdq0rYCjdp/QkOPlimpSipPkHZ0DmpNZgc9mZOhTd2TbccEAAAAJLEoFbHQ4VKFjpZLFVVSYoL8nZPk9/gJvgsZcvvmaHDPgeqUnKLi0hLlHdylzfu2eZoBbpwLcIcL54MLGeKxnwYWlGv2inyNzj+hCp/0vpEWSzomKbVKGvZZmW45UqbpWwr0Zu8OumNcb+3KSGrTTC6cC3CHC+eDCxnisZ8a4sJ+cOF8gDtsnw+257uSwQX0k/dYlAqDqaxSRV6RyjYfVdWnZfV+ntAtqGBuZyUOTpMv0DbviHQhQ3JiUBPPvVLTx0zWiKzh9X6+ae9WzVm9QIs3vKTSivoZER0unAtwhwvngwsZ4rmfbvygUA+vOKDykNE8Sb8z0sYG7neWke6SdGP+Ca1dtEu3j+utxUPSoprFhXMB7nDhfHAhQzz308lc2A8unA9wh+3zwfZ8VzK4gH6yi0WpZoQKylWydI/MscY/zrDq0zKdWHFApes/Vco1/eSP8r8+u5DhtB4DtGTGY8rqkqkqU9Xgfc7oM1SzJz2geybM0HWzp+mjwx9HNQPcOBfgDhfOBxcyxHM/3fhBoea8mq8PJI2X9LEav1jkZknflvQzSa+EjB59db98Mnp2SHpUsrhwLsAdLpwPLmSI5346mQv7wYXzAe6wfT7Ynu9KBhfQT/a1ryW2KAsVlKv42d0yxY2fHCczxZUqfna3QgXl7SrDaT0GaPkPFikzo6d8Pp/8Cf4G7+dP8Mvn8ykzo6de++FTGtS9f9QywI1zAe5w4XxwIUM899OggnI9vOKAPpB0oaR9n3+/4adTX3x/n6RRkvIkPbzigAZG4Xi4cC7AHS6cDy5kiOd+OpkL+8GF8wHusH0+2J7vSgYX0E9uiHhR6pNPPtHkyZPVtWtXdejQQWeeeabeeeedtshmlamsUsnSPVJ5lRTuhxcZSeXVjzOVjf1nQWxlSE4MasmMx5QaTFHAH94L6wL+gFKDKXrujrlKTgy2OgPcOBdiAf3U1INEP6l99dPvV+SrLGQ0XlKRpFCYjwt9fv+vSCoPGc1ekd+qHC6cC7EiHjrKhfPBhQzx3k81XNgPLpwPsSAe+kmyfz7Ynu9KBhfQT+6IaFHq6NGjGj16tBITE7Vs2TJt3bpVv/nNb9S5c+e2ymdNRV5R9cvnIv00bSOZY5WqyCtqFxkmnnulsrpkhv2HWiPgD6hf1z664ZwJrc4AN84F19FPYaCfJLWPfhpxqFSj809osarfshfuglSN0OePWyxpdP4JjThc2uIsLpwLsSBeOsqF88GFDPHcTydzYT+4cD64Ll76SbJ/Ptie70oGF9BP7ohoUeq//uu/lJWVpXnz5umCCy7QwIEDdcUVV+i0005rq3zWlG0+avXxrmSYPmZyo++tbU6oKqTpYya3OgPcOBdcRz9593hXMsRzP03aXqAKn/Q7tfx9+AmqfnyFT5q0raDFWVw4F2JBvHSUC+eDCxniuZ9O5sJ+cOF8cF289JNk/3yw/XhXMriAfnJHRM9lly5dqvPOO09f+9rX1KNHD5199tl69NFH2yqbNaHDpQ1e8T4SVZ+WKdSKf3l2IUNu3xyNyBre6Htrm+NP8Ousfqcrt29OizPAjXMhFtBP4aOfYr+fRu0/ofc//5S9lr5wu0rSe5LeN9KX8k+0aBsunAuxIh46yoXzwYUM8d5PNVzYDy6cD7EgHvpJsn8+2J7vSgYX0E9uiWhR6qOPPtIjjzyiwYMH65VXXtFtt92mu+66S0888USjjykrK1NRUVGdm+tCR6N3EbtYzjC458CoZMiO0nbilQvnQiygnyLcDv0kKXb7aejRMm2P0rZ2SBp2pGVPilw4F2JFpB1FP8Vuhnjvpxou7AcXzodYEA/9JNk/H2zPdyWDC+gnt0T0Bsqqqiqdd955+uUvfylJOvvss/X+++/rj3/8o6ZMmdLgY2bNmqWf/exnrU/qpYooXTCsvBXbcSBDp+SUqERIjdJ24pYD50IsoJ8iRD9Jis1+8hmjpCrpWJS2VyQpqap6u8bni+zBDpwLsSLSjqKfYjdDPPfTyZzYDw6cD7EgLvpJsn8+2J7vSgYH0E9uieiVUr1799bw4cPrfC8nJ0d79uxp9DEzZ85UYWFh7W3v3r0tS+qlxJZeoeMUSa3YjgMZiktLohLhWJS2E7ccOBdiAf0UIfpJUmz2k/H5VJ4gpUZpe2mSyhMU+YKU5MS5ECsi7Sj6KXYzxHM/ncyJ/eDA+RAL4qKfJPvng+35rmRwAP3kloheKTV69Gjt2LGjzvc++OAD9e/fv9HHBINBBYOx9bG2/s5J0dlORsu340KGvIO7opIh78BHUdlOvHLhXIgF9FOE26GfqrcTo/20o3NQwz5r3XUIagyTtL1Ly/4OXDgXYkWkHUU/xW6GeO+nGi7sBxfOh1gQD/0k2T8fbM93JYML6Ce3RLSsdvfdd2vt2rX65S9/qZ07d2rhwoWaM2eOZsyY0Vb5rPB3T1ZCt9YVbUK3oPzdk2M6w+Z927Rp71aFqiL9sPFqoaqQNu75p7Z8Eq0rn8QnF86FWEA/hY9+iv1+WpPZQWf4pLPUuk/fO1vS6T5pbe8OLdqGC+dCrIiHjnLhfHAhQ7z3Uw0X9oML50MsiId+kuyfD7bnu5LBBfSTWyJ6Lnv++edryZIl+stf/qIzzjhDP//5z/XQQw9p0qRJbZXPmmBuZ6uPdyXDnNULlOBr2X/y+BP8mrN6QaszwI1zwXX0k3ePdyVDPPfTkzkZSjTSXWrdp+/dJSnRVG+vpVw4F2JBvHSUC+eDCxniuZ9O5sJ+cOF8cF289JNk/3yw/XhXMriAfnJHxEfhq1/9qrZs2aLS0lJt27ZNt956a1vksi5xcJp8qQEp0kts+CRfakCJg9PaRYbFG17S3iP7VRmqjOhxlaFK7fnsE/313WWtzgA3zoVYQD81g36S1D76aVP3ZL3Zu4NulNRfUqQfaOz//HETJb3Zu4M2teJf2Vw4F2JFPHSUC+eDCxniuZ9O5sJ+cOF8iAXx0E+S/fPB9nxXMriAfnJH7F8Vq434AglKuaZf9YXDwj1JfJKSqh/nC7R+17qQobSiTNc//B0dKysJ+w+2MlSpY2Ulum72NJVWROeaJ/HOhXMB7nDhfHAhQ7z30x3jeivJ79PLqr5YebgLU/7P7/+KpCS/T3eM692qHC6cC3CHC+eDCxnivZ9quLAfXDgf4A7b54Pt+a5kcAH95I728Vu0EX9GkjrdOEC+TuFdD97XKaBONw6I6sXGXMjw4aHduvTBm7S/4KAkNfre25rv7y84qEsfvEkfHf44ahngxrkAd7hwPriQIZ77aVdGkm4f11tDJK2R1Pfz7zf2f+w13+/7+f0HS7p9XG/tisLxcOFcgDtcOB9cyBDP/XQyF/aDC+cD3GH7fLA935UMLqCf3BDRp+/FI39GklK/MUgVeUUq23xUVZ/WXxFN6BZUMLdz9cvv2mC10oUMHx3+WBf84irdcM4ETR8zWWf1O73efbbs2645qxfor+8uazf/wucaF84FuMOF88GFDPHcT4uHpMkno4dXHNCWkNFiSb+T9F4D9x2h6mtITVT1K6RuHddbi4dE72XfLpwLcIcL54MLGeK5n07mwn5w4XyAO2yfD7bnu5LBBfSTfSxKhcEXSFBSToaScjIUOlyqUEG5VF4lJSXIn5HkyRXvXchQWlGmheue08J1zym3b46yew5UanKKjpWWaOfBXdq8b1ubZ4Ab5wLc4cL54EKGeO6nZ4eka0OPDpq9Il9T809okk9630g7JBWp+q16QyWd4au+qPkbmR1059jovELqVC6cC3CHC+eDCxniuZ9O5sJ+cOF8gDtsnw+257uSwQX0k10sSkXI3z3Z+gnhQobN+7bFzZMol7lwLsAdLpwPLmSIx37alZGkq27orxGHSzVpW4G+lH9CNxwpU1KVVJ4gbe8S1LzeHfRkTkarLmoeCRfOBbjDhfPBhQzx2E8NcWE/uHA+wB22zwfb813J4AL6yXssSgEA0E5s6p6sTd171X7tM0bGF+lHugAAAADeaF9vRgQAALVYkAIAAIDLWJQCAAAAAACA53zGGOPlwKKiIqWnp1cPT/H+3YPmeKVkJPkkX0c7714kAxlcymB7viSZkkpJUmFhodLSovdpYJGy3U+SI8eDc5IMZKibwYGOop/I4Mp8MjiWgX6S5MixIAMZHJnvTIYw+8nqNaVqQtoZbnk+GcjgWgbb8x1jfV+4cDxsZ7A9nwxkcJT1/eDCsSCD/flkcCuDI6zvBxeOBRnI4Mp8VzI0w+qiFK+UIgMZ7GewPV9ysyj5lz57GWzPJwMZ6mVwrKPop/jOYHs+GRzLQD9JcuRYkIEMjsx3JkOY/WRvUaqjX2lTsz0fWzR/p0xJpXwdA1bmk4EMrmWwPV+SCuflScdDVmY3yFI/SW4cD9sZbM8nAxlO5VRH0U9xn8H2fDK4lYF+qubCsSADGVyZ70qGcPuJC50DAAAAAADAcyxKoVV83l4nHwAAAO0EzyMBAFavKYXYM+JwqSZtK9Co/Sc09GiZkqqk8gRpR+eg1mR20JM5GdrUPdl2TAAAADiG55EAgFOxKIWwDCwo1+wV+Rqdf0IVPilxxFnS5cOk1FQlHTumM7dv17BNGzV9S4He7N1Bd4zrrV0ZSbZjA4hDuX1zNLjnQHVKTlFxaYnyDu7S5n3bbMcCgLjtJ55HAu4LHS5V6Gi5VFElJSbI3zlJ/jhcJI7XnraJRSk068YPCvXwigPyJwWlqVOVeNdd0tln17tf4nvvSb/7nUY+tUhrF+3S7eN6a/GQNAuJAcSb5MSgJp57paaPmawRWcPr/XzT3q2as3qBFm94SaUVZRYSAohX8d5PPI8E3GUqq1SRV6SyzUdV9Wn9/knoFlQwt7MSB6fJF2i/V/6J9562rf2eWYiKGz8o1JxX85U4KFuBrdukxx+XcnMbvnNurvT44wps3abEgafp0Vf368YPCr0NDCDunNZjgN6+70XNnvSAzugztMH7nNFnqGZPekBv3/eiBnXv73FCAPEq3vuJ55GAu0IF5Tq28COdWHGgwQUpSar6tEwnVhzQsYUfKVRQ7nFCb8R7T7uARSk0alBBuR5ecUAaPFgJa9dKffpIPp/k9zf8AL+/+ud9+ihh3TopO1sPrzigge20wADYd1qPAVr+g0XKzOgpn88nf0LD/eRP8Mvn8ykzo6de++FTPKEA0ObivZ94Hgm4K1RQruJnd8sUV4Z1f1NcqeJnd7e7hal472lXRLQoNWDAAPl8vnq3GTNmtFU+WPT7FflKSAoq4e9/l1JTpcTE8B6YmCilpirh1VflTwpq9or8tg0KfI6Oii/JiUEtmfGYUoMpCvjDezd6wB9QajBFz90xV8mJwTZOCHyBfoov9BPPI2MJ/RRfTGWVSpbukcqrpHA/ANNIKq9+nKmsast4nqGn3RHRotT69euVn59fe3v11VclSV/72tfaJBzsGXGoVKPzTyjxpn+R+vcP/4lEjcREacAABb5+k0bnn9CIw6VtExQ4CR0VXyaee6WyumSG/USiRsAfUL+ufXTDORPaKBlQH/0UX+K9n3geGVvop/hSkVckc6wy/AWpGkYyxypVkVfUJrm8Fu897ZKIFqW6d++uXr161d5eeOEFnXbaaRozZkxb5YMlk7YXqMIn6a67pKoWroaHQtKdd6rCJ03aVhDNeECD6Kj4Mn3MZFWZlvVTqCqk6WMmRzkR0Dj6Kb7Eez/xPDK20E/xpWzzUauPd0W897RLWnxNqfLyci1YsEDf/va35fP5opkJDhi1/0T1x/WefXbj7/1vjt8vnXuuEkecpS/ln4hqPqA5dFT7lts3RyOyhjf63v/m+BP8Oqvf6crtmxPlZEDz6Kf2jX7ieWQso5/at9Dh0kYvah6uqk/LFIrxVy/S025p8aLUc889p4KCAk2dOrXJ+5WVlamoqKjODe4berRMGjYsShsbqmFH+OhMeCucjqKfYtfgngOjsp3sKG0HiAT91L7RTzyPjGX0U/sWOhqdC5XH+gXP6Wm3tHhRau7cuZowYYIyMzObvN+sWbOUnp5ee8vKymrpSHjEZ4ySqlR9UcpoSEtTUlX1dgGvhNNR9FPs6pScEpXtpEZpO0Ak6Kf2Ld77ieeRsY1+aucqonSR8vLYvth5vPe0a1q0KPXxxx9r+fLl+s53vtPsfWfOnKnCwsLa2969e1syEh4yPp/KEyQdOxadDRYVqTyheruAF8LtKPopdhWXlkRlO8eitB0gXPRT+xfv/cTzyNhFP8WBxBa/JqWupChtx5J472nXRHap+c/NmzdPPXr00FVXXdXsfYPBoIJBPi4x1uzoHNSZ27dHZ2Pbt2t7F84BeCfcjqKfYlfewV3R2c6Bj6KyHSBc9FP7Rz/xPDJW0U/tn79zUnS2kxGd7dhCT7sl4iXOqqoqzZs3T1OmTFEg0KI1LcSANZkdVLFpo/Tee9WfftISoZC0YYMqNm/S2t4dopoPaAwdFR8279umTXu3KlTVsn4KVYW0cc8/teWTKP1HExAG+ik+0E88j4xF9FN88HdPVkK31i0oJnQLyt89OUqJ7KCn3RLxotTy5cu1Z88effvb326LPHDEkzkZSjSSfvc7KaGFL8/0+6Xf/16Jpnp7gBfoqPgxZ/UCJfha1k/+BL/mrF4Q5URA0+in+BHv/cTzyNhDP8WPYG5nq493Rbz3tEsiPgpXXHGFjDEaMmRIW+SBIzZ1T9abvTuo8qlF0scfSxUVkW2gokLavVuVTz+lN3t30KYYX01H7KCj4sfiDS9p75H9qgxVRvS4ylCl9nz2if767rI2SgY0jH6KH/HeTzyPjD30U/xIHJwmX2pAivQybT7JlxpQ4uC0NsnltXjvaZfE9hXK0KbuGNdbofIyVV1xRfXFKsN9QlFRIR07pqrLL1eovEx3jOvdtkEBxKXSijJd//B3dKysJOwnFJWhSh0rK9F1s6eptIKPGAfQNugnnkcCrvIFEpRyTb/qi5WHuzDlk5RU/ThfoH0sIdDT7mgfZxTaxK6MJN0+rreUl6eqkSOlTz6p/kFj1wao+f4nn1Tff+dO3T6ut3bF+IXwALjrw0O7demDN2l/wUFJavTaADXf319wUJc+eJM+OvyxZxkBxKd47yeeRwLu8mckqdONA+TrFN71w3ydAup044CYv8D5qeK9p13BohSatHhImqZf3lsVuz5U5fAcaepUaePGhu+8caM0daoqh+eoYteHuvXyTC0e0j5e3gnAXR8d/lgX/OIq3f5/M7VlX8MXnNyyb7tu/7+ZuuAXV/FEAoBn4r2feB4JuMufkaTUbwxSh3G9Gr34eUK3oDqM66XUbwxqdwtSNeK9p13ARyugWc8OSdeGHh00e0W+Rj/xhCr+/IQSR5wlDR0qpaVJRUXSjh2q2LRRiUZam9lBd44dyL9sAfBMaUWZFq57TgvXPafcvjnK7jlQqckpOlZaop0Hd2nzvm22IwKIU/HeTzyPBNzlCyQoKSdDSTkZCh0uVaigXCqvkpIS5M9IivlP2QtXvPe0bSxKISy7MpJ01Q39NeJwqSZtK9CX9m3TsM0blVQllSdI27sEtfaMDD2Zk8HFKAFYtXnfNp48AHBSvPYTzyMB9/m7J8fNIlRT4rWnbWJRChHZ1D1Zm7r3qv3aZ4yML9KPbgAAAEC84XkkAOBUXFMKrcITCQAAALQEzyMBACxKAQAAAAAAwHM+Y4zxcmBRUZHS09Orh6d4/+5Bc7xSMpJ8kq+jnXcvkoEMLmWwPV+STEmlJKmwsFBpafY+acd2P0mOHA/OSTKQoW4GBzqKfiKDK/PJ4FgG+kmSI8eCDGRwZL4zGcLsJ6vXlKoJaWe45flkIINrGWzPd4z1feHC8bCdwfZ8MpDBUdb3gwvHggz255PBrQyOsL4fXDgWZCCDK/NdydAMq4tSvFKKDGSwn8H2fMnNouRf+uxlsD2fDGSol8GxjqKf4juD7flkcCwD/STJkWNBBjI4Mt+ZDGH2k71FqY5+pU3N9nxs0fydMiWV8nUMWJlPBjK4lsH2fEkqnJcnHQ9Zmd0gS/0kuXE8bGewPZ8MZDiVUx1FP8V9BtvzyeBWBvqpmgvHggxkcGW+KxnC7ScudA4AAAAAAADPsSgFAAAAAADk8/Zz0AC715QCAAAAAAB2jDhcqknbCjRq/wkNPVqmpCqpPEHa0TmoNZkd9GROhjZ1T7YdE+0Yi1IAAAAAAMSRgQXlmr0iX6PzT6jCJ71vpMWSjklKrZKGfVamW46UafqWAr3Zu4PuGNdbuzKSbMdGO8SiFAAAAAAAceLGDwr18IoDKg8ZzZP0OyNtbOB+ZxnpLkk35p/Q2kW7dPu43lo8JM3bsGj3uKYUAAAAAABx4MYPCjXn1XztChmdKenbkjY3ct/Nn//8TEm7Q0aPvrpfN35Q6FVUxAkWpQAAAAAAaOcGFZTr4RUH9IGkCyXt+/z7VY3cv+b7+ySNkpQn6eEVBzSwoLxtgyKuRLQoFQqF9J//+Z8aOHCgOnTooNNOO00///nPZbhCPwDL6CcALqOjALiKfoofv1+Rr7KQ0XhJRZJCYT4u9Pn9vyKpPGQ0e0V+W0VEHIromlL/9V//pUceeURPPPGETj/9dL3zzju65ZZblJ6errvuuqutMgJAs+gnAC6jowC4in6KDyMOlWp0/gnNk/RxCx4f+vxxiyVNzT+hEYdL+VQ+REVEi1JvvfWWrr32Wl111VWSpAEDBugvf/mL3n777TYJBwDhop8AuIyOAuAq+ik+TNpeoApf9UXNE9T4W/aakiDpd5Im+aRJ2wq0qXuv6IZEXIro7XsXXnihXnvtNX3wwQeSpE2bNumNN97QhAkT2iQcAISLfgLgMjoKgKvop/gwav8Jvf/5p+y1ZEFKnz/uPUnvG+lL+Seilg3xLaJXSt17770qKirSsGHD5Pf7FQqF9MADD2jSpEmNPqasrExlZWW1XxcVFbU8LQA0gn4C4LJIO4p+AuAV+ik+DD1apsVR2tYOSTccKWv2fkA4Inql1NNPP60nn3xSCxcu1LvvvqsnnnhCv/71r/XEE080+phZs2YpPT299paVldXq0ABwKvoJgMsi7Sj6CYBX6Kf2z2eMkqqkY1HaXpGkpKrq7QKtFdGi1A9/+EPde++9+pd/+RedeeaZ+uY3v6m7775bs2bNavQxM2fOVGFhYe1t7969rQ4NAKeinwC4LNKOop8AeIV+av+Mz6fyBCk1SttLk1SeUL1doLUievve8ePHlZBQdx3L7/erqqrxd6UGg0EFg8GWpQOAMNFPAFwWaUfRTwC8Qj/Fhx2dgxr2WXTecjdM0vYunAOIjogWpa6++mo98MAD6tevn04//XS99957+p//+R99+9vfbqt8ABAW+gmAy+goAK6in+LDmswOuuVImc4y0ma1/NP3Rkg63SfN690hugERtyJalPr973+v//zP/9Ttt9+uQ4cOKTMzU//6r/+q//f//l9b5QOAsNBPAFxGRwFwFf0UH57MydD0LQW6S1JLlxurJN0lKdFUbw+IhogWpVJTU/XQQw/poYceaqM4ANAy9BMAl9FRAFxFP8WHTd2T9WbvDrox/4R+JmmfpFAEj/dL6itpoqQ3e3fQpu7JbRETcSiiC50DAAAAAIDYc8e43kry+/Syqi9W7g/zcf7P7/+KpCS/T3eM691WERGHWJQCAAAAAKCd25WRpNvH9dYQSWtU/conqfFFgZrv9/38/oMl3T6ut3ZlJLVtUMQVFqUAAAAAAIgDi4ekafrlvTXA79MWSfNUffHyhoz4/OdbJA3w+3Tr5ZlaPCTNq6iIExFdUwoAAAAAAMSuZ4eka0OPDpq9Il9T809okk9630g7JBWp+q16QyWd4au+qPkbmR1051heIYW2waIUAAAAAABxZFdGkq66ob9GHC7VpG0F+lL+Cd1wpExJVVJ5grS9S1DzenfQkzkZXNQcbYpFKQAAAAAA4tCm7sna1L1X7dc+Y2R8PouJEG+4phQAAAAAAGBBCp5jUQoAAAAAAACe8xljjJcDCwsLlZGRUf1FR7+Xo6sdD33xv23MJwMZXMtge/5JGQoKCpSenm4ngxzoJ8mp42Etg+35ZCBDIxlsdhT9RAZn5pPByQz0kzvHggxksD7fsQzN9ZPn15Q6duzYF1+cvKNssD2fDGRwLYPl+ceOHbO6KOVUP5HBjflkIMNJbHYU/UQGJ+eTwZkM9NNJyEAGlzLYnu9Ahub6yfNXSlVVVWn//v1KTU2VrwXvVy0qKlJWVpb27t2rtLS0NkhIhljJYHs+GaKXwRijY8eOKTMzUwkJ9t5VTD+RoT1lsD2/PWVwoaNa20+S/eNhez4ZyOBaBvrpC7aPhQsZbM8nAxminSHcfvL8lVIJCQnq27dvq7eTlpZm7eCQwa0MtueTIToZbL5Cqgb9RIb2mMH2/PaSwXZHRaufJPvHw/Z8MpDBtQz00xdsHwsXMtieTwYyRDNDOP3Ehc4BAAAAAADgORalAAAAAAAA4LmYW5QKBoP6yU9+omAwSIY4z2B7PhncyuACF/YDGcjgynwyuMf2vrA9nwxkcC2D7fkucWFf2M5gez4ZyGArg+cXOgcAAAAAAABi7pVSAAAAAAAAiH0sSgEAAAAAAMBzLEoBAAAAAADAcyxKAQAAAAAAwHMxtSi1Zs0a+f1+XXXVVZ7Pnjp1qnw+X+2ta9euGj9+vDZv3ux5lgMHDujOO+/UoEGDFAwGlZWVpauvvlqvvfZam88+eT8kJiaqZ8+euvzyy/X444+rqqqqzeefmuHk2/jx4z2Z31yOnTt3ejL/wIED+t73vqfs7GwlJyerZ8+eGj16tB555BEdP368zedPnTpV1113Xb3vr1q1Sj6fTwUFBW2ewTV0FP10ag5bHWW7nyS7HUU/1Uc/0U+n5qCfeA7lCvqJfjo1B/0UX/0UU4tSc+fO1Z133qnXX39d+/fv93z++PHjlZ+fr/z8fL322msKBAL66le/6mmG3bt369xzz9WKFSv04IMPasuWLXr55Zc1duxYzZgxw5MMNfth9+7dWrZsmcaOHavvfe97+upXv6rKykpPM5x8+8tf/uLJ7OZyDBw4sM3nfvTRRzr77LP197//Xb/85S/13nvvac2aNfrRj36kF154QcuXL2/zDKgv3juKfqqfw2ZH2eoniY5yEf1EP52ag36in1xBP9FPp+agn+KrnwK2A4SruLhYTz31lN555x0dOHBA8+fP13/8x394miEYDKpXr16SpF69eunee+/Vl7/8ZR0+fFjdu3f3JMPtt98un8+nt99+WykpKbXfP/300/Xtb3/bkwwn74c+ffronHPO0Ze+9CVdeumlmj9/vr7zne94msEmWzluv/12BQIBvfPOO3XOg0GDBunaa6+VMcbzTPGOjqKfGsthi80MdJRb6Cf6qbEcttBPqEE/0U+N5bCFfvJezLxS6umnn9awYcM0dOhQTZ48WY8//rjVg1JcXKwFCxYoOztbXbt29WTmkSNH9PLLL2vGjBl1TtIaGRkZnuRoyLhx4zRixAj99a9/tZYhXnz22Wf6+9//3uh5IEk+n8/jVIj3jqKfUIOOcg/9RD+hGv3kHvqJfkK1eO6nmFmUmjt3riZPniyp+iV1hYWFWr16tacZXnjhBXXq1EmdOnVSamqqli5dqqeeekoJCd7sxp07d8oYo2HDhnkyL1LDhg3T7t27PZl18rGouf3yl7/0ZHZTOb72ta+1+cya82Do0KF1vt+tW7faHPfcc0+b55AaPg4TJkzwZLZr4r2j6Ke6XOgoG/0kudNR9NMX6Cf66WT0k/1+kuioGvQT/XQy+ik++ykm3r63Y8cOvf3221qyZIkkKRAI6KabbtLcuXN1ySWXeJZj7NixeuSRRyRJR48e1R/+8AdNmDBBb7/9tvr379/m811/uZ4xxrPV25OPRY0uXbp4MrupHI2tanvh7bffVlVVlSZNmqSysjJPZjZ0HNatW1f75CJe0FH006lc6CiX+knyvqPop2r0E/10KvqpPp5D2UE/0U+nop/qi4d+iolFqblz56qyslKZmZm13zPGKBgMavbs2UpPT/ckR0pKirKzs2u/fuyxx5Senq5HH31Uv/jFL9p8/uDBg+Xz+bR9+/Y2n9US27Zt8+wicKceC1ts5MjOzpbP59OOHTvqfH/QoEGSpA4dOniWpaHff9++fZ7NdwUdRT+dyoWOspXBlY6in6rRT/TTqegn+/0k0VES/STRT6ein+Kzn5x/+15lZaX+/Oc/6ze/+Y02btxYe9u0aZMyMzOtfOJaDZ/Pp4SEBJ04ccKTeV26dNFXvvIVPfzwwyopKan3c5sfH7tixQpt2bJFEydOtJYhXnTt2lWXX365Zs+e3eB5AG/RUdXoJ9Sgo9xBP1Wjn1CDfnIH/VSNfkKNeO4n518p9cILL+jo0aOaNm1avdXyiRMnau7cufrud7/rSZaysjIdOHBAUvVLO2fPnq3i4mJdffXVnsyXpIcfflijR4/WBRdcoPvvv1+5ubmqrKzUq6++qkceeUTbtm1r8ww1+yEUCungwYN6+eWXNWvWLH31q1/Vt771rTaff3KGkwUCAXXr1s2T+bb94Q9/0OjRo3Xeeefppz/9qXJzc5WQkKD169dr+/btOvfcc21HjBt01Bfop/o5TkZH0VFeo5++QD/Vz3Ey+ol+8hr99AX6qX6Ok9FPcdBPxnFf/epXzZVXXtngz9atW2ckmU2bNrV5jilTphhJtbfU1FRz/vnnm2effbbNZ59q//79ZsaMGaZ///4mKSnJ9OnTx1xzzTVm5cqVbT775P0QCARM9+7dzWWXXWYef/xxEwqF2nz+qRlOvg0dOtST+SfnuPbaaz2debL9+/ebO+64wwwcONAkJiaaTp06mQsuuMA8+OCDpqSkpM3nN/b7r1y50kgyR48ebfMMLqCj6or3fjo1h62Ost1PxtjtKPqpGv1UF/1EP9XgOZR99FNd9BP9VCMe+8lnjONXVwMAAAAAAEC74/w1pQAAAAAAAND+sCgFAAAAAAAAz7EoBQAAAAAAAM+xKAUAAAAAAADPsSgFAAAAAAAAz7EoBQAAAAAAAM+xKAUAAAAAAADPsSgFAAAAAAAAz7EoBQAAAAAAAM+xKNUO/fGPf1RqaqoqKytrv1dcXKzExERdcsklde67atUq+Xw+ffjhh7XfW7NmjcaNG6eUlBSlpaXp4osv1okTJ5qde8stt+i+++5r8Gc//elP5fP56tyGDRtW+/MjR47ozjvv1NChQ9WhQwf169dPd911lwoLC8P6nceOHavHHnuswZ9dcskldeb27NlTX/va1/Txxx/Xu+/8+fOVm5ur5ORk9ejRQzNmzAhrPoDwtKafDhw4oG9+85vq1auXUlJSdM4552jx4sVhzW1NP0nSnDlzdMkllygtLU0+n08FBQVh/85NzZ46dWqduV27dtX48eO1efPmOvcbMGBAvYy/+tWvws4AIDyx+BxKkkpLSzVjxgx17dpVnTp10sSJE3Xw4MGwfudoPIdav369Lr30UmVkZKhz5876yle+ok2bNoU1H0DDWtNH4TxvOXLkiCZNmqS0tDRlZGRo2rRpKi4uDivbwIEDtXz58ibvY4zRhAkT5PP59Nxzz9X52WuvvaYLL7xQqamp6tWrl+655546v2dLZ5/8fMnv9yszM1PTpk3T0aNHa++ze/fuep3q8/m0du3asOYj+liUaofGjh2r4uJivfPOO7Xf+8c//qFevXpp3bp1Ki0trf3+ypUr1a9fP5122mmSqp9MjR8/XldccYXefvttrV+/XnfccYcSEpo+VUKhkF544QVdc801jd7n9NNPV35+fu3tjTfeqP3Z/v37tX//fv3617/W+++/r/nz5+vll1/WtGnTmv19jxw5ojfffFNXX311o/e59dZblZ+fr/379+v555/X3r17NXny5Dr3+Z//+R/9+Mc/1r333qt//vOfWr58ub7yla80Ox9A+FrTT9/61re0Y8cOLV26VFu2bNENN9ygr3/963rvvfeanNnafpKk48ePa/z48fqP//iPiH7fcGaPHz++du5rr72mQCCgr371q/Xud//999fJeOedd0aUBUDzYvE5lCTdfffd+tvf/qZnnnlGq1ev1v79+3XDDTc0+/tG4zlUcXGxxo8fr379+mndunV64403lJqaqq985SuqqKhoNgOAhrWmj8J53jJp0iT985//1KuvvqoXXnhBr7/+uqZPn95srs2bN+vo0aMaM2ZMk/d76KGH5PP56n1/06ZNuvLKKzV+/Hi99957euqpp7R06VLde++9UZld83xpz549evLJJ/X666/rrrvuqne/5cuX1+nVc889t9n5aCMG7VLv3r3NrFmzar/+0Y9+ZGbMmGFycnLMypUra79/8cUXmylTptR+PXLkSHPfffdFPO/11183vXv3NlVVVQ3+/Cc/+YkZMWJERNt8+umnTVJSkqmoqGjyfn/+85/NyJEjG/35mDFjzPe+97063/u///s/07Fjx9qvjxw5Yjp06GCWL18eUUYAkWtpP6WkpJg///nPdbbVpUsX8+ijjzY5L5r9tHLlSiPJHD16NKz7Nzd7ypQp5tprr63zvX/84x9Gkjl06FDt9/r3729++9vfhjUTQOvE2nOogoICk5iYaJ555pna723bts1IMmvWrGlydjSeQ61fv95IMnv27Kn93ubNm40kk5eX1+R8AE1raR/VaOx5y9atW40ks379+trvLVu2zPh8PvPJJ580men+++83N910U5P3ee+990yfPn1Mfn6+kWSWLFlS+7OZM2ea8847r879ly5dapKTk01RUVGrZjf0fOnnP/+5GT58eO3Xu3btMpLMe++91+QseIdXSrVTY8eO1cqVK2u/XrlypS655BKNGTOm9vsnTpzQunXrNHbsWEnSoUOHtG7dOvXo0UMXXnihevbsqTFjxtT717iGLF26VFdffXWDq+E18vLylJmZqUGDBmnSpEnas2dPk9ssLCxUWlqaAoFAs7OvvfbaZjPWOHLkiJ5++mmNHDmy9nuvvvqqqqqq9MknnygnJ0d9+/bV17/+de3duzfs7QIIT0v6SZIuvPBCPfXUUzpy5Iiqqqq0aNEilZaW1nsJ+6naop/CFc7skxUXF2vBggXKzs5W165d6/zsV7/6lbp27aqzzz5bDz74YNgvcwcQmVh7DrVhwwZVVFTosssuq/3esGHD1K9fP61Zs6bZ2a19DjV06FB17dpVc+fOVXl5uU6cOKG5c+cqJydHAwYMCHvbAOpr6XOm5qxZs0YZGRk677z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      "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",
    "_unique_bords, _unique_actions = drop_duplicate_boards(\n",
    "    simulation_results[0].reshape(-1, 8, 8), simulation_results[1].reshape(-1, 2)\n",
    ")\n",
    "plot_othello_boards(_unique_bords, actions=_unique_actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 8, 8)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(simulation_results[0], (-1, 8, 8)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 2)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulation_results[1].reshape(-1, 2).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 340.06 MiB, increment: 0.29 MiB\n",
      "10.3 s ± 473 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%memit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n",
    "%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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70, 10000, 8, 8), (70, 10000, 2))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "_board_history.shape, _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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000, 8, 8)"
      ]
     },
     "execution_count": 28,
     "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": "markdown",
   "metadata": {},
   "source": [
    "The action space size can be drawn into a histogram by turn and a curve over the mean action space size.\n",
    "This can be used to analyse in which area of the game that cant be solved abolutely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f676f497a974475bb7a51fd6fb3921a3",
       "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": [
    "count_poss_turns = np.sum(_poss_turns, axis=(2, 3))\n",
    "mean_possibilitie_count = np.mean(count_poss_turns, axis=1)\n",
    "std_possibilitie_count = np.std(count_poss_turns, axis=1)\n",
    "cum_prod = count_poss_turns\n",
    "\n",
    "\n",
    "@interact(turn=(0, 69))\n",
    "def poss_turn_count(turn):\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(15, 8))\n",
    "    ax1, ax2, ax3, ax4 = axes.flatten()\n",
    "    _mean_possibilitie_count = mean_possibilitie_count.copy()\n",
    "    _std_possibilitie_count = std_possibilitie_count.copy()\n",
    "    _mean_possibilitie_count[_mean_possibilitie_count <= 1] = 1\n",
    "    _std_possibilitie_count[_std_possibilitie_count <= 1] = 1\n",
    "    np.cumprod(_mean_possibilitie_count[::-1], axis=0)[::-1]\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis\\nThe total size is estimated to be around {np.prod(_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",
    "\n",
    "    ax4.plot(\n",
    "        range(70),\n",
    "        np.cumprod((_mean_possibilitie_count)[::-1], axis=0)[::-1],\n",
    "        # yerr=np.cumprod(_std_possibilitie_count[::-1], axis=0)[::-1],\n",
    "    )\n",
    "    ax4.scatter(\n",
    "        turn,\n",
    "        np.cumprod(_mean_possibilitie_count[::-1], axis=0)[::-1][turn],\n",
    "        marker=\"x\",\n",
    "    )\n",
    "    ax4.set_yscale(\"log\", base=10)\n",
    "    ax4.set_xlabel(\"Turn\")\n",
    "    ax4.set_ylabel(\"Mean remaining total action space size\")\n",
    "    fig.delaxes(ax3)\n",
    "    fig.tight_layout()\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": 30,
   "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": 30,
     "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": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000, 8, 8)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_poss_turns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d48165bf2db47f491a1fdfea7445b27",
       "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_poss_turn = np.mean(_poss_turns, axis=1)\n",
    "del _poss_turns\n",
    "\n",
    "\n",
    "@interact(turn=(0, 69))\n",
    "def turn_distribution_heatmap(turn):\n",
    "    turn_possibility_on_field = mean_poss_turn[turn]\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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 10000)\n"
     ]
    }
   ],
   "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",
    "print(np.max(np.abs(calculate_direct_score(_board_history))))\n",
    "assert len(calculate_direct_score(_board_history).shape) == 2\n",
    "assert calculate_direct_score(_board_history).shape[0] == SIMULATE_TURNS\n",
    "print(np.mincalculate_direct_score(_board_history).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1381da4a05b24c60b58f0a8ff2d8d7be",
       "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, 6))\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis / total size estimat {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": 35,
   "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",
    "print(np.max(np.abs(calculate_final_evaluation_for_history(_board_history))))\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 distribution\")\n",
    "plt.hist((_final_eval * 64), density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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_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(\"Win distribtuion\")\n",
    "plt.bar(\n",
    "    [\"black\", \"draw\", \"white\"],\n",
    "    pd.Series(calculate_who_won(_board_history)).value_counts().sort_index() / 10000,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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 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.xlabel(\"Turn\")\n",
    "plt.ylabel(\"Factor of skipped turns\")\n",
    "plt.yscale(\"log\", base=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000)"
      ]
     },
     "execution_count": 38,
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09677184,  0.0037773 ,  0.12190913,  0.03519891,  0.16118614,\n",
       "        0.00617017,  0.12490022, -0.03918723,  0.14632847, -0.01240192,\n",
       "        0.1016851 ,  0.00991888,  0.1295861 , -0.03332988,  0.07552515,\n",
       "       -0.10090606,  0.14730492, -0.08930635,  0.08367957, -0.09071304,\n",
       "        0.1600462 ,  0.08287025,  0.22077531, -0.07559336,  0.1789458 ,\n",
       "        0.02836975,  0.23077469,  0.01503086,  0.13597608, -0.18159241,\n",
       "       -0.03167801, -0.23491001,  0.05792499, -0.04478127,  0.06121092,\n",
       "       -0.04067385,  0.37884519,  0.04386898,  0.17202373, -0.05840784,\n",
       "        0.0441777 , -0.14009038,  0.02019953, -0.09193809,  0.15851489,\n",
       "        0.08095611,  0.45275764,  0.13625955,  0.36563693, -0.05076633,\n",
       "        0.28810459, -0.22580677, -0.16507096, -0.5579012 , -0.033314  ,\n",
       "       -0.15883   ,  0.23115   , -0.45325   , -0.37125   , -0.58125   ,\n",
       "       -0.21875   , -0.21875   , -0.21875   , -0.21875   , -0.21875   ,\n",
       "       -0.21875   , -0.21875   , -0.21875   , -0.21875   , -0.21875   ])"
      ]
     },
     "execution_count": 39,
     "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, 0, -1):\n",
    "        values = gama_table[turn] * combined_score[turn]\n",
    "        combined_score[turn - 1] += values\n",
    "\n",
    "    return combined_score\n",
    "\n",
    "\n",
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.8, who_won_fraction=0, final_score_fraction=1\n",
    ")[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.53249554e-06, -1.91561943e-06, -2.39452428e-06, -2.99315535e-06,\n",
       "       -3.74144419e-06, -4.67680524e-06, -5.84600655e-06, -7.30750819e-06,\n",
       "       -9.13438523e-06, -1.14179815e-05, -1.42724769e-05, -1.78405962e-05,\n",
       "       -2.23007452e-05, -2.78759315e-05, -3.48449144e-05, -4.35561430e-05,\n",
       "       -5.44451787e-05, -6.80564734e-05, -8.50705917e-05, -1.06338240e-04,\n",
       "       -1.32922800e-04, -1.66153499e-04, -2.07691874e-04, -2.59614843e-04,\n",
       "       -3.24518554e-04, -4.05648192e-04, -5.07060240e-04, -6.33825300e-04,\n",
       "       -7.92281625e-04, -9.90352031e-04, -1.23794004e-03, -1.54742505e-03,\n",
       "       -1.93428131e-03, -2.41785164e-03, -3.02231455e-03, -3.77789319e-03,\n",
       "       -4.72236648e-03, -5.90295810e-03, -7.37869763e-03, -9.22337204e-03,\n",
       "       -1.15292150e-02, -1.44115188e-02, -1.80143985e-02, -2.25179981e-02,\n",
       "       -2.81474977e-02, -3.51843721e-02, -4.39804651e-02, -5.49755814e-02,\n",
       "       -6.87194767e-02, -8.58993459e-02, -1.07374182e-01, -1.34217728e-01,\n",
       "       -1.67772160e-01, -2.09715200e-01, -2.62144000e-01, -3.27680000e-01,\n",
       "       -4.09600000e-01, -5.12000000e-01, -6.40000000e-01, -8.00000000e-01,\n",
       "       -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00,\n",
       "       -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00,\n",
       "       -1.00000000e+00, -1.00000000e+00])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.8, who_won_fraction=1, final_score_fraction=0\n",
    ")[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.09670969,   0.12088712,   3.9011089 ,   1.12638612,\n",
       "         5.15798265,   0.19747831,   3.99684789,  -1.25394014,\n",
       "         4.68257483,  -0.39678147,   3.25402317,   0.31752896,\n",
       "         4.1469112 ,  -1.066361  ,   2.41704875,  -3.22868907,\n",
       "         4.71413867,  -2.85732667,   2.67834167,  -2.90207292,\n",
       "         5.12240885,   2.65301107,   7.06626383,  -2.41717021,\n",
       "         5.72853724,   0.91067155,   7.38833944,   0.4854243 ,\n",
       "         4.35678037,  -5.80402453,  -1.00503067,  -7.50628834,\n",
       "         1.86713958,  -1.41607552,   1.9799056 ,  -1.27511801,\n",
       "        12.15610249,   1.44512812,   5.55641015,  -1.80448732,\n",
       "         1.49439085,  -4.38201144,   0.77248571,  -2.78439287,\n",
       "         5.26950892,   2.83688614,  14.79610768,   4.7451346 ,\n",
       "        12.18141825,  -1.02322719,   9.97096602,  -6.28629248,\n",
       "        -4.1078656 , -16.384832  ,   0.76896   ,  -2.7888    ,\n",
       "        10.264     , -10.92      ,  -7.4       , -13.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.8, who_won_fraction=0, final_score_fraction=0\n",
    ")[:, 0] * 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def weights_init_normal(m):\n",
    "    \"\"\"Takes in a module and initializes all linear layers with weight\n",
    "       values taken from a normal distribution.\n",
    "    Source: https://stackoverflow.com/a/55546528/11003343\n",
    "    \"\"\"\n",
    "\n",
    "    classname = m.__class__.__name__\n",
    "    # for every Linear layer in a model\n",
    "    if classname.find(\"Linear\") != -1:\n",
    "        y = m.in_features\n",
    "        # m.weight.data shoud be taken from a normal distribution\n",
    "        m.weight.data.normal_(0.0, 1 / np.sqrt(y))\n",
    "        # m.bias.data should be 0\n",
    "        m.bias.data.fill_(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]], grad_fn=<TanhBackward0>)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BATCH_SIZE = 1000\n",
    "\n",
    "\n",
    "class DQLNet(nn.Module):\n",
    "    def __init__(self, load_from: str | None = None):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(8 * 8 * 2, 128 * 2)\n",
    "        # self.nb1 = nn.BatchNorm1d([128 * 2])\n",
    "        self.fc2 = nn.Linear(128 * 2, 128 * 3)\n",
    "        # self.nb2 = nn.BatchNorm1d([128 * 3])\n",
    "        self.fc3 = nn.Linear(128 * 3, 128 * 2)\n",
    "        self.fc4 = nn.Linear(128 * 2, 1)\n",
    "        if not load_from:\n",
    "            self.apply(weights_init_normal)\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",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        # x = self.nb1(x)\n",
    "        # x = self.dropout1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        # x = self.nb2(x)\n",
    "        x = self.fc3(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc4(x)\n",
    "        x = torch.tanh(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "DQLNet().forward(np.zeros((5, 2, 8, 8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SymmetryMode(Enum):\n",
    "    MULTIPLY = \"MULTIPLY\"\n",
    "    BREAK_SEQUENCE = \"BREAK_SEQUENCE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70, 100, 8, 8), (70, 100, 2))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_board_history, _action_history = simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n",
    "_board_history.shape, _action_history.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "472 ms ± 24.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "peak memory: 382.54 MiB, increment: 6.84 MiB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(70, 100, 2, 8, 8)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def action_to_q_learning_format(\n",
    "    board_history: np.ndarray, action_history: np.ndarray\n",
    ") -> np.ndarray:\n",
    "    q_learning_format = np.zeros(\n",
    "        (SIMULATE_TURNS, board_history.shape[1], 2, 8, 8), dtype=float\n",
    "    )\n",
    "    q_learning_format[:, :, 1, :, :] = -1\n",
    "    q_learning_format[:, :, 1, action_history[:, :, 0], action_history[:, :, 0]] = 1\n",
    "    return q_learning_format\n",
    "\n",
    "\n",
    "%timeit action_to_q_learning_format(_board_history, _action_history)\n",
    "%memit action_to_q_learning_format(_board_history, _action_history)\n",
    "action_to_q_learning_format(_board_history, _action_history).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "493 ms ± 27.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "peak memory: 378.35 MiB, increment: 6.84 MiB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2, 2, 2, 70, 100, 2, 8, 8)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_symetry_action(\n",
    "    board_history: np.ndarray, action_history: np.ndarray\n",
    ") -> np.ndarray:\n",
    "    board_history = board_history.copy()\n",
    "    board_history[::2] *= -1\n",
    "    q_learning_format = np.zeros(\n",
    "        (2, 2, 2, SIMULATE_TURNS, board_history.shape[1], 2, 8, 8)\n",
    "    )\n",
    "    q_learning_format[0, 0, 0, :, :, :, :, :] = action_to_q_learning_format(\n",
    "        board_history, action_history\n",
    "    )\n",
    "    q_learning_format[1, 0, 0, :, :, :, :, :] = np.transpose(\n",
    "        q_learning_format[0, 0, 0, :, :, :, :, :], [0, 1, 2, 4, 3]\n",
    "    )\n",
    "    q_learning_format[:, 1, 0, :, :, :, :, :] = q_learning_format[\n",
    "        :, 0, 0, :, :, :, ::-1, :\n",
    "    ]\n",
    "    q_learning_format[:, :, 1, :, :, :, :, :] = q_learning_format[\n",
    "        :, :, 0, :, :, :, :, ::-1\n",
    "    ]\n",
    "    return q_learning_format\n",
    "\n",
    "\n",
    "%timeit build_symetry_action(_board_history, _action_history)\n",
    "%memit build_symetry_action(_board_history, _action_history)\n",
    "build_symetry_action(_board_history, _action_history).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def live_history(training_history: pd.DataFrame, trainable, max_epochs: int):\n",
    "    clear_output(wait=True)\n",
    "    # plt.ylim(0, 100)\n",
    "    _ = training_history[[c for c in training_history.columns if c[0] != \"base\"]].plot(\n",
    "        secondary_y=[c for c in training_history.columns if c[1] == \"final_score\"]\n",
    "    )\n",
    "    plt.xlim(0, max_epochs)\n",
    "\n",
    "    plt.title(\"title\")\n",
    "    plt.xlabel(\"axis x\")\n",
    "    plt.ylabel(\"axis y\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLPolicy(GamePolicy):\n",
    "    def __init__(\n",
    "        self,\n",
    "        epsilon: float,\n",
    "        neural_network: DQLNet,\n",
    "        symmetry_mode: SymmetryMode,\n",
    "        gamma: float = 0.8,\n",
    "        who_won_fraction: float = 0,\n",
    "        final_score_fraction: float = 0,\n",
    "        optimizer: torch.optim.Optimizer | None = None,\n",
    "        loss: nn.modules.loss._Loss | None = None,\n",
    "    ):\n",
    "        super().__init__(epsilon)\n",
    "        assert 0 <= gamma <= 1\n",
    "        self.gamma: str = gamma\n",
    "        del gamma\n",
    "        self.symmetry_mode: SymmetryMode = symmetry_mode\n",
    "        del symmetry_mode\n",
    "        self.neural_network: DQLNet = neural_network\n",
    "        del neural_network\n",
    "        self.who_won_fraction: final = who_won_fraction\n",
    "        del who_won_fraction\n",
    "        self.final_score_fraction: final = final_score_fraction\n",
    "        del final_score_fraction\n",
    "\n",
    "        if optimizer is None:\n",
    "            self.optimizer = torch.optim.Adam(self.neural_network.parameters(), lr=5e-3)\n",
    "        else:\n",
    "            self.optimizer = optimizer\n",
    "        if loss is None:\n",
    "            self.loss = nn.MSELoss()\n",
    "        else:\n",
    "            self.loss = loss\n",
    "        self.training_results: list[dict[tuple[str, str], float]] = []\n",
    "\n",
    "    @property\n",
    "    def policy_name(self) -> str:\n",
    "        symmetry_name = {SymmetryMode.MULTIPLY: \"M\", SymmetryMode.BREAK_SEQUENCE: \"B\"}\n",
    "        g = f\"{self.gamma:.1f}\".replace(\".\", \"\")\n",
    "        ww = f\"{self.who_won_fraction:.1f}\".replace(\".\", \"\")\n",
    "        fsf = f\"{self.final_score_fraction:.1f}\".replace(\".\", \"\")\n",
    "        return f\"QL-{symmetry_name[self.symmetry_mode]}-G{g}-WW{ww}-FSF{fsf}-{ql_policy.neural_network.__class__.__name__}-{self.loss.__class__.__name__}\"\n",
    "\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        results = np.zeros_like(boards, dtype=float)\n",
    "        results = torch.from_numpy(results).float()\n",
    "        q_learning_boards = np.zeros((boards.shape[0], 2, 8, 8))\n",
    "        q_learning_boards[:, 0, :, :] = boards\n",
    "        poss_turns = boards == 0  # checks where fields are empty.\n",
    "        poss_turns &= binary_dilation(boards == -1, SURROUNDING)\n",
    "        turn_possible = np.any(poss_turns, axis=0)\n",
    "        for action_x, action_y in itertools.product(range(8), range(8)):\n",
    "            if not turn_possible[action_x, action_y]:\n",
    "                continue\n",
    "            _q_learning_board = q_learning_boards[\n",
    "                poss_turns[:, action_x, action_y]\n",
    "            ].copy()\n",
    "            _q_learning_board[:, 1, action_x, action_y] = 1\n",
    "            ql_result = self.neural_network.forward(_q_learning_board)\n",
    "            results[poss_turns[:, action_x, action_y], action_x, action_y] = (\n",
    "                ql_result.reshape(-1) + 0.1\n",
    "            )\n",
    "        return results.cpu().detach().numpy()\n",
    "\n",
    "    def generate_trainings_data(self, generate_data_size: int) -> np.ndarray:\n",
    "        train_boards, train_actions = simulate_game(generate_data_size, [self] * 2)\n",
    "        action_possible = ~np.all(train_actions[:, :] == -1, axis=(2))\n",
    "        q_leaning_formated_action = build_symetry_action(train_boards, train_actions)\n",
    "        q_rewords = calculate_q_reword(\n",
    "            board_history=train_boards,\n",
    "            who_won_fraction=self.who_won_fraction,\n",
    "            final_score_fraction=self.final_score_fraction,\n",
    "        )\n",
    "        if self.symmetry_mode == SymmetryMode.MULTIPLY:\n",
    "            q_rewords = np.array([q_rewords] * 8)\n",
    "            action_possible = np.array([action_possible] * 8).reshape(-1)\n",
    "\n",
    "        elif self.symmetry_mode == SymmetryMode.BREAK_SEQUENCE:\n",
    "            axis1 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            axis2 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            axis3 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            q_leaning_formated_action = q_leaning_formated_action[\n",
    "                axis1, axis2, axis3, range(SIMULATE_TURNS)\n",
    "            ]\n",
    "            action_possible = action_possible.reshape(-1)\n",
    "\n",
    "        return (\n",
    "            torch.from_numpy(\n",
    "                q_leaning_formated_action.reshape(-1, 2, BOARD_SIZE, BOARD_SIZE)[\n",
    "                    action_possible\n",
    "                ]\n",
    "            ).float(),\n",
    "            torch.from_numpy(q_rewords.reshape(-1, 1)[action_possible]).float(),\n",
    "        )\n",
    "\n",
    "    def train_batch(self, nr_of_games: int):\n",
    "        x_train, y_train = self.generate_trainings_data(nr_of_games)\n",
    "        y_pred = self.neural_network.forward(x_train)\n",
    "        loss_score = self.loss(y_pred, y_train)\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        loss_score.backward()\n",
    "        # Update the parameters\n",
    "        self.optimizer.step()\n",
    "        # generate trainings data\n",
    "\n",
    "    def evaluate_model(self, compare_models: list[GamePolicy], nr_of_games: int):\n",
    "        result_dict: dict[tuple[str, str], float] = {}\n",
    "        eval_copy = copy.copy(ql_policy)\n",
    "        eval_copy._epsilon = 1\n",
    "        for model in compare_models:\n",
    "            boards_white, _ = simulate_game(nr_of_games, (eval_copy, model))\n",
    "            boards_black, _ = simulate_game(nr_of_games, (model, eval_copy))\n",
    "            win_eval_white = evaluate_who_won(boards_white[-1])\n",
    "            win_eval_black = evaluate_who_won(boards_black[-1])\n",
    "            result_dict[(model.policy_name, \"final_score\")] = np.mean(\n",
    "                final_boards_evaluation(boards_white[-1])\n",
    "                + final_boards_evaluation(boards_black[-1]) * -1\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"white_win\")] = (\n",
    "                np.sum(win_eval_white == 1) / nr_of_games\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"white_lose\")] = (\n",
    "                np.sum(win_eval_white == -1) / nr_of_games\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"black_win\")] = (\n",
    "                np.sum(win_eval_black == 1) / nr_of_games\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"black_lose\")] = (\n",
    "                np.sum(win_eval_black == -1) / nr_of_games\n",
    "            )\n",
    "            result_dict[(\"base\", \"base\")] = nr_of_games\n",
    "        return result_dict\n",
    "\n",
    "    def save(self):\n",
    "        filename: str = f\"{self.policy_name}-{len(self.training_results)}\"\n",
    "        with open(TRINING_RESULT_PATH / Path(f\"{filename}.pickle\"), \"wb\") as f:\n",
    "            pickle.dump(self.training_results, f)\n",
    "        torch.save(\n",
    "            self.neural_network.state_dict(),\n",
    "            TRINING_RESULT_PATH / Path(f\"{filename}.torch\"),\n",
    "        )\n",
    "\n",
    "    def load(self):\n",
    "        pickle_files = glob.glob(f\"{TRINING_RESULT_PATH}/{self.policy_name}-*.pickle\")\n",
    "        torch_files = glob.glob(f\"{TRINING_RESULT_PATH}/{self.policy_name}-*.torch\")\n",
    "\n",
    "        assert len(pickle_files) == len(torch_files)\n",
    "        if not pickle_files:\n",
    "            return\n",
    "\n",
    "        pickle_dict = {\n",
    "            int(file.split(\"-\")[-1].split(\".\")[0]): file for file in pickle_files\n",
    "        }\n",
    "        torch_dict = {\n",
    "            int(file.split(\"-\")[-1].split(\".\")[0]): file for file in torch_files\n",
    "        }\n",
    "        pickle_file = pickle_dict[max(pickle_dict.keys())]\n",
    "        torch_file = torch_dict[max(torch_dict.keys())]\n",
    "\n",
    "        with open(pickle_file, \"rb\") as f:\n",
    "            self.training_results = pickle.load(f)\n",
    "\n",
    "        self.neural_network.load_state_dict(torch.load(Path(torch_file)))\n",
    "\n",
    "    def train(\n",
    "        self,\n",
    "        epochs: int,\n",
    "        batches: int,\n",
    "        batch_size: int,\n",
    "        eval_batch_size: int,\n",
    "        compare_with: list[GamePolicy],\n",
    "        save_every_epoch: bool = True,\n",
    "        live_plot: bool = True,\n",
    "    ) -> pd.DataFrame:\n",
    "        max_epochs = epochs + len(self.training_results)\n",
    "        assert epochs > 0\n",
    "        for epoch in tqdm(range(epochs)):\n",
    "            for batch in tqdm(range(batches)):\n",
    "                self.train_batch(batch_size)\n",
    "            self.training_results.append(\n",
    "                self.evaluate_model(compare_with, eval_batch_size)\n",
    "            )\n",
    "            if save_every_epoch:\n",
    "                self.save()\n",
    "            if live_plot:\n",
    "                live_history(self.history, self, max_epochs)\n",
    "        return self.history\n",
    "\n",
    "    @property\n",
    "    def history(self) -> pd.DataFrame:\n",
    "        pandas_result = pd.DataFrame(self.training_results)\n",
    "        pandas_result.columns = pd.MultiIndex.from_tuples(pandas_result.columns)\n",
    "        return pandas_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.8 s ± 1.61 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "peak memory: 664.29 MiB, increment: 279.84 MiB\n",
      "27.9 s ± 302 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "peak memory: 384.48 MiB, increment: 0.00 MiB\n"
     ]
    }
   ],
   "source": [
    "ql_policy = QLPolicy(\n",
    "    0.95,\n",
    "    neural_network=DQLNet(),\n",
    "    symmetry_mode=SymmetryMode.MULTIPLY,\n",
    "    gamma=0.8,\n",
    "    who_won_fraction=0,\n",
    "    final_score_fraction=0,\n",
    ")\n",
    "_batch_size = 100\n",
    "%timeit ql_policy.train_batch(_batch_size)\n",
    "%memit ql_policy.train_batch(_batch_size)\n",
    "%timeit ql_policy.evaluate_model([RandomPolicy(0)], _batch_size)\n",
    "%memit ql_policy.evaluate_model([RandomPolicy(0)], _batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'QL-M-G08-WW10-FSF00-DQLNet-MSELoss'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql_policy = QLPolicy(\n",
    "    0.95,\n",
    "    neural_network=DQLNet(),\n",
    "    symmetry_mode=SymmetryMode.MULTIPLY,\n",
    "    gamma=0.8,\n",
    "    who_won_fraction=1,\n",
    "    final_score_fraction=0,\n",
    ")\n",
    "ql_policy.policy_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gen = ql_policy.generate_trainings_data(10)\n",
    "gen[0][4, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ql_policy.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8c055d3efec4253af97679ddaad11ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39545244041249cba2f5876dfeded265",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[75], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mql_policy\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtrain\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m200\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m10\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m1000\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m100\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m[\u001B[49m\u001B[43mRandomPolicy\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mGreedyPolicy\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n",
      "Cell \u001B[1;32mIn[71], line 180\u001B[0m, in \u001B[0;36mQLPolicy.train\u001B[1;34m(self, epochs, batches, batch_size, eval_batch_size, compare_with, save_every_epoch, live_plot)\u001B[0m\n\u001B[0;32m    178\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m epoch \u001B[38;5;129;01min\u001B[39;00m tqdm(\u001B[38;5;28mrange\u001B[39m(epochs)):\n\u001B[0;32m    179\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m batch \u001B[38;5;129;01min\u001B[39;00m tqdm(\u001B[38;5;28mrange\u001B[39m(batches)):\n\u001B[1;32m--> 180\u001B[0m         \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtrain_batch\u001B[49m\u001B[43m(\u001B[49m\u001B[43mbatch_size\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    181\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtraining_results\u001B[38;5;241m.\u001B[39mappend(\n\u001B[0;32m    182\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mevaluate_model(compare_with, eval_batch_size)\n\u001B[0;32m    183\u001B[0m     )\n\u001B[0;32m    184\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m save_every_epoch:\n",
      "Cell \u001B[1;32mIn[71], line 97\u001B[0m, in \u001B[0;36mQLPolicy.train_batch\u001B[1;34m(self, nr_of_games)\u001B[0m\n\u001B[0;32m     96\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtrain_batch\u001B[39m(\u001B[38;5;28mself\u001B[39m, nr_of_games: \u001B[38;5;28mint\u001B[39m):\n\u001B[1;32m---> 97\u001B[0m     x_train, y_train \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgenerate_trainings_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnr_of_games\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     98\u001B[0m     y_pred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mneural_network\u001B[38;5;241m.\u001B[39mforward(x_train)\n\u001B[0;32m     99\u001B[0m     loss_score \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mloss(y_pred, y_train)\n",
      "Cell \u001B[1;32mIn[71], line 66\u001B[0m, in \u001B[0;36mQLPolicy.generate_trainings_data\u001B[1;34m(self, generate_data_size)\u001B[0m\n\u001B[0;32m     65\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mgenerate_trainings_data\u001B[39m(\u001B[38;5;28mself\u001B[39m, generate_data_size: \u001B[38;5;28mint\u001B[39m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m np\u001B[38;5;241m.\u001B[39mndarray:\n\u001B[1;32m---> 66\u001B[0m     train_boards, train_actions \u001B[38;5;241m=\u001B[39m \u001B[43msimulate_game\u001B[49m\u001B[43m(\u001B[49m\u001B[43mgenerate_data_size\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m     67\u001B[0m     action_possible \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m~\u001B[39mnp\u001B[38;5;241m.\u001B[39mall(train_actions[:, :] \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m, axis\u001B[38;5;241m=\u001B[39m(\u001B[38;5;241m2\u001B[39m))\n\u001B[0;32m     68\u001B[0m     q_leaning_formated_action \u001B[38;5;241m=\u001B[39m build_symetry_action(train_boards, train_actions)\n",
      "Cell \u001B[1;32mIn[23], line 25\u001B[0m, in \u001B[0;36msimulate_game\u001B[1;34m(nr_of_games, policies, tqdm_on)\u001B[0m\n\u001B[0;32m     23\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m policy_index \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[0;32m     24\u001B[0m     current_boards \u001B[38;5;241m=\u001B[39m current_boards \u001B[38;5;241m*\u001B[39m \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m\n\u001B[1;32m---> 25\u001B[0m current_boards, action_taken \u001B[38;5;241m=\u001B[39m \u001B[43msingle_turn\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcurrent_boards\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpolicy\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     26\u001B[0m action_history_stack[turn_index, :] \u001B[38;5;241m=\u001B[39m action_taken\n\u001B[0;32m     28\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m policy_index \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n",
      "Cell \u001B[1;32mIn[22], line 25\u001B[0m, in \u001B[0;36msingle_turn\u001B[1;34m(current_boards, policy)\u001B[0m\n\u001B[0;32m     19\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m VERIFY_POLICY:\n\u001B[0;32m     20\u001B[0m     \u001B[38;5;28;01massert\u001B[39;00m np\u001B[38;5;241m.\u001B[39mall(moves_possible(current_boards, policy_results)), (\n\u001B[0;32m     21\u001B[0m         current_boards[(moves_possible(current_boards, policy_results) \u001B[38;5;241m==\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m)],\n\u001B[0;32m     22\u001B[0m         policy_results[(moves_possible(current_boards, policy_results) \u001B[38;5;241m==\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m)],\n\u001B[0;32m     23\u001B[0m         np\u001B[38;5;241m.\u001B[39mwhere(moves_possible(current_boards, policy_results) \u001B[38;5;241m==\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m),\n\u001B[0;32m     24\u001B[0m     )\n\u001B[1;32m---> 25\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mdo_moves\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcurrent_boards\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpolicy_results\u001B[49m\u001B[43m)\u001B[49m, policy_results\n",
      "Cell \u001B[1;32mIn[18], line 74\u001B[0m, in \u001B[0;36mdo_moves\u001B[1;34m(boards, moves)\u001B[0m\n\u001B[0;32m     72\u001B[0m boards \u001B[38;5;241m=\u001B[39m boards\u001B[38;5;241m.\u001B[39mcopy()\n\u001B[0;32m     73\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m game \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(boards\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m]):\n\u001B[1;32m---> 74\u001B[0m     \u001B[43m_do_move\u001B[49m\u001B[43m(\u001B[49m\u001B[43mboards\u001B[49m\u001B[43m[\u001B[49m\u001B[43mgame\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmoves\u001B[49m\u001B[43m[\u001B[49m\u001B[43mgame\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     75\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m boards\n",
      "Cell \u001B[1;32mIn[18], line 64\u001B[0m, in \u001B[0;36mdo_moves.<locals>._do_move\u001B[1;34m(_board, move)\u001B[0m\n\u001B[0;32m     62\u001B[0m action \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[0;32m     63\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m direction \u001B[38;5;129;01min\u001B[39;00m DIRECTIONS:\n\u001B[1;32m---> 64\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[43m_do_directional_move\u001B[49m\u001B[43m(\u001B[49m\u001B[43m_board\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmove\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdirection\u001B[49m\u001B[43m)\u001B[49m:\n\u001B[0;32m     65\u001B[0m         action \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[0;32m     66\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m action:\n",
      "Cell \u001B[1;32mIn[18], line 38\u001B[0m, in \u001B[0;36mdo_moves.<locals>._do_directional_move\u001B[1;34m(board, rec_move, rev_direction, step_one)\u001B[0m\n\u001B[0;32m     36\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m step_one\n\u001B[0;32m     37\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m next_field \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m---> 38\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[43m_do_directional_move\u001B[49m\u001B[43m(\u001B[49m\u001B[43mboard\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrec_position\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrev_direction\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstep_one\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m:\n\u001B[0;32m     39\u001B[0m         board[\u001B[38;5;28mtuple\u001B[39m(rec_position\u001B[38;5;241m.\u001B[39mtolist())] \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[0;32m     40\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m\n",
      "Cell \u001B[1;32mIn[18], line 30\u001B[0m, in \u001B[0;36mdo_moves.<locals>._do_directional_move\u001B[1;34m(board, rec_move, rev_direction, step_one)\u001B[0m\n\u001B[0;32m     15\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Changes the color of enemy stones in one direction.\u001B[39;00m\n\u001B[0;32m     16\u001B[0m \n\u001B[0;32m     17\u001B[0m \u001B[38;5;124;03mThis function works recursive. The argument step_one should always be used in its default value.\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     27\u001B[0m \u001B[38;5;124;03m    All changes are made on the view of the numpy array and therefore not included in the return value.\u001B[39;00m\n\u001B[0;32m     28\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m     29\u001B[0m rec_position \u001B[38;5;241m=\u001B[39m rec_move \u001B[38;5;241m+\u001B[39m rev_direction\n\u001B[1;32m---> 30\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43many\u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrec_position\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m>\u001B[39;49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m8\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m|\u001B[39;49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43mrec_position\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m<\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m:\n\u001B[0;32m     31\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[0;32m     32\u001B[0m next_field \u001B[38;5;241m=\u001B[39m board[\u001B[38;5;28mtuple\u001B[39m(rec_position\u001B[38;5;241m.\u001B[39mtolist())]\n",
      "File \u001B[1;32m<__array_function__ internals>:180\u001B[0m, in \u001B[0;36many\u001B[1;34m(*args, **kwargs)\u001B[0m\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "ql_policy.train(200, 10, 1000, 100, [RandomPolicy(0), GreedyPolicy(0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df2 = train(ql_winner_onyl, 5, 5, 1000, 50, [RandomPolicy(0), GreedyPolicy(0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = train(ql_winner_onyl, 5, 5, 1000, 50, [RandomPolicy(0), GreedyPolicy(0)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a model"
   ]
  },
  {
   "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": [
    "import sys\n",
    "\n",
    "\n",
    "def sizeof_fmt(num, suffix=\"B\"):\n",
    "    \"\"\"by Fred Cirera,  https://stackoverflow.com/a/1094933/1870254, modified\"\"\"\n",
    "    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n",
    "        if abs(num) < 1024.0:\n",
    "            return \"%3.1f %s%s\" % (num, unit, suffix)\n",
    "        num /= 1024.0\n",
    "    return \"%.1f %s%s\" % (num, \"Yi\", suffix)\n",
    "\n",
    "\n",
    "for name, size in sorted(\n",
    "    ((name, sys.getsizeof(value)) for name, value in list(locals().items())),\n",
    "    key=lambda x: -x[1],\n",
    ")[:20]:\n",
    "    print(\"{:>30}: {:>8}\".format(name, sizeof_fmt(size)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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