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",
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   "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",
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   "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."
   ]
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  {
   "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 an 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."
   ]
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  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import abc\n",
    "import itertools\n",
    "import os.path\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",
    "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"
   ]
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   "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",
    "TRAINING_RESULT_PATH: Final[Path] = Path(\"training_data\")\n",
    "if not os.path.exists(TRAINING_RESULT_PATH):\n",
    "    os.mkdir(TRAINING_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"
   ]
  },
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   "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": {
      "text/plain": "<Figure size 300x300 with 1 Axes>",
      "image/png": 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\n"
     },
     "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": [
      "9.56 ms ± 332 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "969 ms ± 25.9 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": [
      "210 µs ± 10.8 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "34.5 µs ± 1.39 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
      "35.8 µs ± 274 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def final_boards_evaluation(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Evaluates the board at the end of the game.\n",
    "\n",
    "    All unused fields are added to the score of the player that has more stones with his color up.\n",
    "    This score only applies to the end of the game.\n",
    "    Normally the score is represented by the number of stones each player has.\n",
    "    In this case the score was combined by building the difference.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of game bords ot the end of the game.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    score1, score2 = np.sum(boards == 1, axis=(1, 2)), np.sum(boards == -1, axis=(1, 2))\n",
    "    player_1_won = score1 > score2\n",
    "    player_2_won = score1 < score2\n",
    "    score1_final = 64 - score2[player_1_won]\n",
    "    score2_final = 64 - score1[player_2_won]\n",
    "    score1[player_1_won] = score1_final\n",
    "    score2[player_2_won] = score2_final\n",
    "    return score1 - score2\n",
    "\n",
    "\n",
    "def evaluate_boards(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Counts the stones each player has on the board.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    return np.sum(boards, axis=(1, 2))\n",
    "\n",
    "\n",
    "def evaluate_who_won(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Checks who won or is winning a game.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        The information who won for both player. 1 meaning the player won, -1 means the opponent lost. 0 represents a patt.\n",
    "    \"\"\"\n",
    "    return np.sign(np.sum(boards, axis=(1, 2)))\n",
    "\n",
    "\n",
    "_boards = get_new_games(EXAMPLE_STACK_SIZE)\n",
    "%timeit final_boards_evaluation(_boards)\n",
    "%timeit evaluate_boards(_boards)\n",
    "%timeit evaluate_who_won(_boards)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute a chosen action\n",
    "\n",
    "After an evaluation what turns are possible there needs to be a function that executes a turn.\n",
    "This next sections does that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": [
      "104 ms ± 3.76 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 300x300 with 1 Axes>",
      "image/png": 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\n"
     },
     "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": [
      "1.78 s ± 420 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1.29 s ± 388 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 8 Axes>",
      "image/png": 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\n"
     },
     "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": {
      "text/plain": "<Figure size 1200x4800 with 61 Axes>",
      "image/png": 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     },
     "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": [
    {
     "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[26], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mget_ipython\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun_line_magic\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mmemit\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43msimulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m      2\u001B[0m get_ipython()\u001B[38;5;241m.\u001B[39mrun_line_magic(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtimeit\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124msimulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\reversi-SkjoUH1O-py3.10\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2369\u001B[0m, in \u001B[0;36mInteractiveShell.run_line_magic\u001B[1;34m(self, magic_name, line, _stack_depth)\u001B[0m\n\u001B[0;32m   2367\u001B[0m     kwargs[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlocal_ns\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_local_scope(stack_depth)\n\u001B[0;32m   2368\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mbuiltin_trap:\n\u001B[1;32m-> 2369\u001B[0m     result \u001B[38;5;241m=\u001B[39m fn(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   2371\u001B[0m \u001B[38;5;66;03m# The code below prevents the output from being displayed\u001B[39;00m\n\u001B[0;32m   2372\u001B[0m \u001B[38;5;66;03m# when using magics with decodator @output_can_be_silenced\u001B[39;00m\n\u001B[0;32m   2373\u001B[0m \u001B[38;5;66;03m# when the last Python token in the expression is a ';'.\u001B[39;00m\n\u001B[0;32m   2374\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mgetattr\u001B[39m(fn, magic\u001B[38;5;241m.\u001B[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001B[38;5;28;01mFalse\u001B[39;00m):\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\reversi-SkjoUH1O-py3.10\\lib\\site-packages\\memory_profiler.py:1113\u001B[0m, in \u001B[0;36mMemoryProfilerMagics.memit\u001B[1;34m(self, line, cell)\u001B[0m\n\u001B[0;32m   1111\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m counter \u001B[38;5;241m<\u001B[39m repeat:\n\u001B[0;32m   1112\u001B[0m     counter \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[1;32m-> 1113\u001B[0m     tmp \u001B[38;5;241m=\u001B[39m \u001B[43mmemory_usage\u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[43m_func_exec\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43mstmt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mshell\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43muser_ns\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1114\u001B[0m \u001B[43m                       \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minterval\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minterval\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1115\u001B[0m \u001B[43m                       \u001B[49m\u001B[43mmax_usage\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmax_iterations\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1116\u001B[0m \u001B[43m                       \u001B[49m\u001B[43minclude_children\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minclude_children\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1117\u001B[0m     mem_usage\u001B[38;5;241m.\u001B[39mappend(tmp)\n\u001B[0;32m   1119\u001B[0m result \u001B[38;5;241m=\u001B[39m MemitResult(mem_usage, baseline, repeat, timeout, interval,\n\u001B[0;32m   1120\u001B[0m                      include_children)\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\reversi-SkjoUH1O-py3.10\\lib\\site-packages\\memory_profiler.py:379\u001B[0m, in \u001B[0;36mmemory_usage\u001B[1;34m(proc, interval, timeout, timestamps, include_children, multiprocess, max_usage, retval, stream, backend, max_iterations)\u001B[0m\n\u001B[0;32m    376\u001B[0m \u001B[38;5;66;03m# When there is an exception in the \"proc\" - the (spawned) monitoring processes don't get killed.\u001B[39;00m\n\u001B[0;32m    377\u001B[0m \u001B[38;5;66;03m# Therefore, the whole process hangs indefinitely. Here, we are ensuring that the process gets killed!\u001B[39;00m\n\u001B[0;32m    378\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 379\u001B[0m     returned \u001B[38;5;241m=\u001B[39m f(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkw)\n\u001B[0;32m    380\u001B[0m     parent_conn\u001B[38;5;241m.\u001B[39msend(\u001B[38;5;241m0\u001B[39m)  \u001B[38;5;66;03m# finish timing\u001B[39;00m\n\u001B[0;32m    381\u001B[0m     ret \u001B[38;5;241m=\u001B[39m parent_conn\u001B[38;5;241m.\u001B[39mrecv()\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\reversi-SkjoUH1O-py3.10\\lib\\site-packages\\memory_profiler.py:889\u001B[0m, in \u001B[0;36m_func_exec\u001B[1;34m(stmt, ns)\u001B[0m\n\u001B[0;32m    886\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_func_exec\u001B[39m(stmt, ns):\n\u001B[0;32m    887\u001B[0m     \u001B[38;5;66;03m# helper for magic_memit, just a function proxy for the exec\u001B[39;00m\n\u001B[0;32m    888\u001B[0m     \u001B[38;5;66;03m# statement\u001B[39;00m\n\u001B[1;32m--> 889\u001B[0m     \u001B[43mexec\u001B[49m\u001B[43m(\u001B[49m\u001B[43mstmt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mns\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m<string>:1\u001B[0m\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 15\u001B[0m, in \u001B[0;36msingle_turn\u001B[1;34m(current_boards, policy)\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msingle_turn\u001B[39m(\n\u001B[0;32m      2\u001B[0m     current_boards: np, policy: GamePolicy\n\u001B[0;32m      3\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mtuple\u001B[39m[np\u001B[38;5;241m.\u001B[39mndarray, np\u001B[38;5;241m.\u001B[39mndarray]:\n\u001B[0;32m      4\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Execute a single turn on a board.\u001B[39;00m\n\u001B[0;32m      5\u001B[0m \n\u001B[0;32m      6\u001B[0m \u001B[38;5;124;03m    Places a new stone on the board. Turns captured enemy stones.\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     13\u001B[0m \u001B[38;5;124;03m        The new game board and the policy vector containing the index of the action used.\u001B[39;00m\n\u001B[0;32m     14\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m---> 15\u001B[0m     policy_results \u001B[38;5;241m=\u001B[39m \u001B[43mpolicy\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_policy\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcurrent_boards\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     17\u001B[0m     \u001B[38;5;66;03m# if the constant VERIFY_POLICY is set to true the policy is verified. Should be good though.\u001B[39;00m\n\u001B[0;32m     18\u001B[0m     \u001B[38;5;66;03m# todo deactivate the policy verification after some testing.\u001B[39;00m\n\u001B[0;32m     19\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m VERIFY_POLICY:\n",
      "Cell \u001B[1;32mIn[19], line 65\u001B[0m, in \u001B[0;36mGamePolicy.get_policy\u001B[1;34m(self, boards)\u001B[0m\n\u001B[0;32m     58\u001B[0m         policies \u001B[38;5;241m=\u001B[39m policies \u001B[38;5;241m*\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mepsilon \u001B[38;5;241m+\u001B[39m np\u001B[38;5;241m.\u001B[39mrandom\u001B[38;5;241m.\u001B[39mrand(\u001B[38;5;241m*\u001B[39mboards\u001B[38;5;241m.\u001B[39mshape) \u001B[38;5;241m*\u001B[39m (\n\u001B[0;32m     59\u001B[0m             \u001B[38;5;241m1\u001B[39m \u001B[38;5;241m-\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mepsilon\n\u001B[0;32m     60\u001B[0m         )\n\u001B[0;32m     62\u001B[0m \u001B[38;5;66;03m# todo talk to team about backpropagation of score and epsilon for greedy factor\u001B[39;00m\n\u001B[0;32m     63\u001B[0m \n\u001B[0;32m     64\u001B[0m \u001B[38;5;66;03m# todo possibly change this function to only validate the purpose turn and not all turns\u001B[39;00m\n\u001B[1;32m---> 65\u001B[0m possible_turns \u001B[38;5;241m=\u001B[39m \u001B[43mget_possible_turns\u001B[49m\u001B[43m(\u001B[49m\u001B[43mboards\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     66\u001B[0m policies[possible_turns \u001B[38;5;241m==\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1.0\u001B[39m\n\u001B[0;32m     67\u001B[0m max_indices \u001B[38;5;241m=\u001B[39m [\n\u001B[0;32m     68\u001B[0m     np\u001B[38;5;241m.\u001B[39munravel_index(policy\u001B[38;5;241m.\u001B[39margmax(), policy\u001B[38;5;241m.\u001B[39mshape) \u001B[38;5;28;01mfor\u001B[39;00m policy \u001B[38;5;129;01min\u001B[39;00m policies\n\u001B[0;32m     69\u001B[0m ]\n",
      "Cell \u001B[1;32mIn[13], line 48\u001B[0m, in \u001B[0;36mget_possible_turns\u001B[1;34m(boards, tqdm_on)\u001B[0m\n\u001B[0;32m     42\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m boards\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m1\u001B[39m:] \u001B[38;5;241m==\u001B[39m (\n\u001B[0;32m     43\u001B[0m     BOARD_SIZE,\n\u001B[0;32m     44\u001B[0m     BOARD_SIZE,\n\u001B[0;32m     45\u001B[0m ), \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe input dimensions do not fit.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m     47\u001B[0m poss_turns \u001B[38;5;241m=\u001B[39m boards \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m  \u001B[38;5;66;03m# checks where fields are empty.\u001B[39;00m\n\u001B[1;32m---> 48\u001B[0m poss_turns \u001B[38;5;241m&\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[43mbinary_dilation\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m     49\u001B[0m \u001B[43m    \u001B[49m\u001B[43mboards\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mSURROUNDING\u001B[49m\n\u001B[0;32m     50\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# checks where fields are next to an enemy filed an empty\u001B[39;00m\n\u001B[0;32m     51\u001B[0m iterate_over \u001B[38;5;241m=\u001B[39m itertools\u001B[38;5;241m.\u001B[39mproduct(\n\u001B[0;32m     52\u001B[0m     \u001B[38;5;28mrange\u001B[39m(boards\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m0\u001B[39m]), \u001B[38;5;28mrange\u001B[39m(BOARD_SIZE), \u001B[38;5;28mrange\u001B[39m(BOARD_SIZE)\n\u001B[0;32m     53\u001B[0m )\n\u001B[0;32m     54\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m tqdm_on:\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\reversi-SkjoUH1O-py3.10\\lib\\site-packages\\scipy\\ndimage\\_morphology.py:520\u001B[0m, in \u001B[0;36mbinary_dilation\u001B[1;34m(input, structure, iterations, mask, output, border_value, origin, brute_force)\u001B[0m\n\u001B[0;32m    517\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m structure\u001B[38;5;241m.\u001B[39mshape[ii] \u001B[38;5;241m&\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m    518\u001B[0m         origin[ii] \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[1;32m--> 520\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_binary_erosion\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstructure\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43miterations\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmask\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    521\u001B[0m \u001B[43m                       \u001B[49m\u001B[43moutput\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mborder_value\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morigin\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbrute_force\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\reversi-SkjoUH1O-py3.10\\lib\\site-packages\\scipy\\ndimage\\_morphology.py:254\u001B[0m, in \u001B[0;36m_binary_erosion\u001B[1;34m(input, structure, iterations, mask, output, border_value, origin, invert, brute_force)\u001B[0m\n\u001B[0;32m    252\u001B[0m     output \u001B[38;5;241m=\u001B[39m _ni_support\u001B[38;5;241m.\u001B[39m_get_output(output\u001B[38;5;241m.\u001B[39mdtype, \u001B[38;5;28minput\u001B[39m)\n\u001B[0;32m    253\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m iterations \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m--> 254\u001B[0m     \u001B[43m_nd_image\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbinary_erosion\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstructure\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmask\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moutput\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    255\u001B[0m \u001B[43m                             \u001B[49m\u001B[43mborder_value\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morigin\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minvert\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcit\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m    256\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m cit \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m brute_force:\n\u001B[0;32m    257\u001B[0m     changed, coordinate_list \u001B[38;5;241m=\u001B[39m _nd_image\u001B[38;5;241m.\u001B[39mbinary_erosion(\n\u001B[0;32m    258\u001B[0m         \u001B[38;5;28minput\u001B[39m, structure, mask, output,\n\u001B[0;32m    259\u001B[0m         border_value, origin, invert, cit, \u001B[38;5;241m1\u001B[39m)\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "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 purpose we played a sample of 10k games and saved them for later analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"rnd_history.npy\") and not os.path.exists(\"rnd_action.npy\"):\n",
    "    simulation_results = simulate_game(\n",
    "        10_000, (RandomPolicy(1), RandomPolicy(1)), tqdm_on=True\n",
    "    )\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": null,
   "metadata": {},
   "outputs": [],
   "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 absolutely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_poss_turns = np.sum(_poss_turns, axis=(2, 3))\n",
    "mean_possibility_count = np.mean(count_poss_turns, axis=1)\n",
    "std_possibility_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_possibility_count = mean_possibility_count.copy()\n",
    "    _std_possibility_count = std_possibility_count.copy()\n",
    "    _mean_possibility_count[_mean_possibility_count <= 1] = 1\n",
    "    _std_possibility_count[_std_possibility_count <= 1] = 1\n",
    "    np.cumprod(_mean_possibility_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_possibility_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_possibility_count,\n",
    "        yerr=std_possibility_count,\n",
    "        label=\"Mean action space size with error bars\",\n",
    "    )\n",
    "    ax2.scatter(turn, mean_possibility_count[turn], marker=\"x\")\n",
    "    ax2.legend()\n",
    "\n",
    "    ax4.plot(\n",
    "        range(70),\n",
    "        np.cumprod(_mean_possibility_count[::-1], axis=0)[::-1],\n",
    "        # yerr=np.cumprod(_std_possibility_count[::-1], axis=0)[::-1],\n",
    "    )\n",
    "    ax4.scatter(\n",
    "        turn,\n",
    "        np.cumprod(_mean_possibility_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 player."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "white = mean_possibility_count[::2]\n",
    "black = mean_possibility_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 action-space\"],\n",
    ").T\n",
    "del white, black\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "_poss_turns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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 estimate {np.prod(np.extract(mean_possibility_count, mean_possibility_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 score 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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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 distribution\")\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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "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 should 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": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SymmetryMode(Enum):\n",
    "    MULTIPLY = \"MULTIPLY\"\n",
    "    BREAK_SEQUENCE = \"BREAK_SEQUENCE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "_board_history, _action_history = simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n",
    "_board_history.shape, _action_history.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "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": null,
   "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": null,
   "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: float = 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: float = who_won_fraction\n",
    "        del who_won_fraction\n",
    "        self.final_score_fraction: float = 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, self))\n",
    "        action_possible = ~np.all(train_actions[:, :] == -1, axis=2)\n",
    "        q_leaning_formatted_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_formatted_action = q_leaning_formatted_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_formatted_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(self)\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\")] = float(\n",
    "                np.mean(\n",
    "                    final_boards_evaluation(boards_white[-1])\n",
    "                    + final_boards_evaluation(boards_black[-1]) * -1\n",
    "                )\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(TRAINING_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",
    "            TRAINING_RESULT_PATH / Path(f\"{filename}.torch\"),\n",
    "        )\n",
    "\n",
    "    def load(self):\n",
    "        pickle_files = glob.glob(f\"{TRAINING_RESULT_PATH}/{self.policy_name}-*.pickle\")\n",
    "        torch_files = glob.glob(f\"{TRAINING_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 _ in tqdm(range(epochs)):\n",
    "            for _ 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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ql_policy.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ql_policy.train(200, 10, 1000, 100, [RandomPolicy(0), GreedyPolicy(0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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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