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": [
    "## Content\n",
    "\n",
    "* [The game rules](#the-game-rules) A short overview over the rules of the game.\n",
    "* [Some common Otello strategies](#some-common-otello-strategies) introduces some easy approaches to a classic Otello AI and defines some behavioral expectations.\n",
    "* [Initial design decisions](#initial-design-decisions) an explanation about some initial design decision and assumptions\n",
    "* [Imports and dependencies](#imports-and-dependencies) explains what libraries where used"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The game rules\n",
    "\n",
    "Othello is played on a board with 8 x 8 fields for two player.\n",
    "The board geometry is equal to a chess game.\n",
    "The game is played with game stones that are black on one siede and white on the other.\n",
    "\n",
    "![Othello game board example](reversi_example.png)\n",
    "\n",
    "The player take turns.\n",
    "A player places a stone with his or her color up on the game board.\n",
    "The player can only place stones when he surrounds a number of stones with the opponents color with the new stone and already placed stones of his color.\n",
    "Those surrounded stones can either be horizontally, vertically and/or diagonally be placed.\n",
    "All stones thus surrounded will be flipped to be of the players color.\n",
    "Turns are only possible if the player is also changing the color of the opponents stones. If a player can't act he is skipped.\n",
    "The game ends if both players can't act. The player with the most stones wins.\n",
    "If the score is counted in detail unclaimed fields go to the player with more stones of his or her color on the board.\n",
    "The game begins with four stones places in the center of the game. Each player gets two. They are placed diagonally to each other.\n",
    "\n",
    "\n",
    "<img alt=\"Startaufstellung.png\" src=\"Startaufstellung.png\"/>\n",
    "\n",
    "## Some common Othello strategies\n",
    "\n",
    "As can be easily understood the placement of stones and on the bord is always a careful balance of attack and defence.\n",
    "If the player occupies huge homogenous stretches on the board it can be attacked easier.\n",
    "The boards corners provide safety from wich occupied territory is impossible to loos but since it is only possible to reach the corners if the enemy is forced to allow this or calculates the cost of giving a stable base to the enemy it is difficult to obtain.\n",
    "There are some text on otello computer strategies which implement greedy algorithms for reversi based on a modified score to each field.\n",
    "Those different values are score modifiers for a traditional greedy algorithm.\n",
    "If a players stone has captured such a filed the score reached is multiplied by the modifier.\n",
    "The total score is the score reached by the player subtracted with the score of the enemy.\n",
    "The scores change in the course of the game and converges against one. This gives some indications of what to expect from an Othello AI.\n",
    "\n",
    "<img alt=\"ComputerPossitionScore\" src=\"computer-score.png\"/>\n"
   ]
  },
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   "cell_type": "markdown",
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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."
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another constant needed is the initial start square at the center of the board."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1,  1],\n",
       "       [ 1, -1]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "START_SQUARE: Final[np.ndarray] = np.array(\n",
    "    [[ENEMY, PLAYER], [PLAYER, ENEMY]], dtype=int\n",
    ")\n",
    "START_SQUARE.setflags(write=False)\n",
    "START_SQUARE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new boards\n",
    "\n",
    "The first function implemented and tested is a function to generate the starting environment as a stack of games.\n",
    "As described above I simply placed a 2 by 2 square in the center of an empty stack of boards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, -1,  1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1, -1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_new_games(number_of_games: int) -> np.ndarray:\n",
    "    \"\"\"Generates a stack of initialised game boards.\n",
    "\n",
    "    Args:\n",
    "        number_of_games: The size of the board stack.\n",
    "\n",
    "    Returns: The generates stack of games as a stack n x 8 x 8.\n",
    "\n",
    "    \"\"\"\n",
    "    empty = np.zeros([number_of_games, BOARD_SIZE, BOARD_SIZE], dtype=int)\n",
    "    empty[:, 3:5, 3:5] = START_SQUARE\n",
    "    return empty\n",
    "\n",
    "\n",
    "get_new_games(1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_number_of_games = 3\n",
    "assert get_new_games(test_number_of_games).shape == (\n",
    "    test_number_of_games,\n",
    "    BOARD_SIZE,\n",
    "    BOARD_SIZE,\n",
    ")\n",
    "np.testing.assert_equal(\n",
    "    get_new_games(test_number_of_games).sum(axis=1),\n",
    "    np.zeros(\n",
    "        [\n",
    "            test_number_of_games,\n",
    "            8,\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "np.testing.assert_equal(\n",
    "    get_new_games(test_number_of_games).sum(axis=2),\n",
    "    np.zeros(\n",
    "        [\n",
    "            test_number_of_games,\n",
    "            8,\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "assert np.all(get_new_games(test_number_of_games)[:, 3:4, 3:4] != 0)\n",
    "del test_number_of_games"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation tools\n",
    "\n",
    "In this section a visualisation help was implemented for debugging of the game and a proper display of the results.\n",
    "For this visualisation ChatGPT was used as a prompted code generator that was later reviewed and refactored by hand to integrate seamlessly into the project as a whole.\n",
    "White stones represent the player, black stones the enemy. A single plot can be used as a subplot when the `ax` argument is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_othello_board(\n",
    "    board: np.ndarray | torch.Tensor,\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",
    "    if isinstance(board, torch.Tensor):\n",
    "        board = board.cpu().detach().numpy()\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": [
      "11.7 ms ± 480 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "1.13 s ± 56.4 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",
    "        tqdm_on: Uses tqdm to track the progress.\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": [
      "220 µs ± 4.43 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "40.8 µs ± 2.84 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
      "48 µs ± 4.37 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def final_boards_evaluation(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Evaluates the board at the end of the game.\n",
    "\n",
    "    All unused fields are added to the score of the player that has more stones with his color up.\n",
    "    This score only applies to the end of the game.\n",
    "    Normally the score is represented by the number of stones each player has.\n",
    "    In this case the score was combined by building the difference.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of game bords ot the end of the game.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    score1, score2 = np.sum(boards == 1, axis=(1, 2)), np.sum(boards == -1, axis=(1, 2))\n",
    "    player_1_won = score1 > score2\n",
    "    player_2_won = score1 < score2\n",
    "    score1_final = 64 - score2[player_1_won]\n",
    "    score2_final = 64 - score1[player_2_won]\n",
    "    score1[player_1_won] = score1_final\n",
    "    score2[player_2_won] = score2_final\n",
    "    return score1 - score2\n",
    "\n",
    "\n",
    "def evaluate_boards(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Counts the stones each player has on the board.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    return np.sum(boards, axis=(1, 2))\n",
    "\n",
    "\n",
    "def evaluate_who_won(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Checks who won or is winning a game.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        The information who won for both player. 1 meaning the player won, -1 means the opponent lost. 0 represents a patt.\n",
    "    \"\"\"\n",
    "    return np.sign(np.sum(boards, axis=(1, 2)))\n",
    "\n",
    "\n",
    "_boards = get_new_games(EXAMPLE_STACK_SIZE)\n",
    "%timeit final_boards_evaluation(_boards)\n",
    "%timeit evaluate_boards(_boards)\n",
    "%timeit evaluate_who_won(_boards)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute a chosen action\n",
    "\n",
    "After an evaluation what turns are possible there needs to be a function that executes a turn.\n",
    "This next sections does that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InvalidTurn(ValueError):\n",
    "    \"\"\"\n",
    "    This error is thrown if a given turn is not valid.\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111 ms ± 6.54 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def do_moves(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Executes a single move on a stack o Othello boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of Othello boards where the next stone should be placed.\n",
    "        moves: A stack of stone placement orders for the game. Formatted as coordinates in an array [x, y] of the place where the stone should be placed. Should contain [-1,-1] if no new placement is possible.\n",
    "\n",
    "    Returns:\n",
    "        The new state of the board.\n",
    "    \"\"\"\n",
    "\n",
    "    def _do_directional_move(\n",
    "        board: np.ndarray, rec_move: np.ndarray, rev_direction, step_one=True\n",
    "    ) -> bool:\n",
    "        \"\"\"Changes the color of enemy stones in one direction.\n",
    "\n",
    "        This function works recursive. The argument step_one should always be used in its default value.\n",
    "\n",
    "        Args:\n",
    "            board: A bord on which a stone was placed.\n",
    "            rec_move: The position on the board in x and y where this function is called from. Will be moved by recursive called.\n",
    "            rev_direction: The position where the stone was placed. Inside this recursion it will also be the last step that was checked.\n",
    "            step_one: Set to true if this is the first step in the recursion. False later on.\n",
    "\n",
    "        Returns:\n",
    "            True if a stone could be flipped.\n",
    "            All changes are made on the view of the numpy array and therefore not included in the return value.\n",
    "        \"\"\"\n",
    "        rec_position = rec_move + rev_direction\n",
    "        if np.any((rec_position >= 8) | (rec_position < 0)):\n",
    "            return False\n",
    "        next_field = board[tuple(rec_position.tolist())]\n",
    "        if next_field == 0:\n",
    "            return False\n",
    "        if next_field == 1:\n",
    "            return not step_one\n",
    "        if next_field == -1:\n",
    "            if _do_directional_move(board, rec_position, rev_direction, step_one=False):\n",
    "                board[tuple(rec_position.tolist())] = 1\n",
    "                return True\n",
    "            return False\n",
    "\n",
    "    def _do_move(_board: np.ndarray, move: np.ndarray) -> None:\n",
    "        \"\"\"Executes a turn on a board.\n",
    "\n",
    "        Args:\n",
    "            _board: The game board on wich to place a stone.\n",
    "            move: The coordinates of a stone that should be placed. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
    "\n",
    "        Returns:\n",
    "            All changes are made on the view of the numpy array.\n",
    "        \"\"\"\n",
    "        if np.all(move == -1):\n",
    "            if not move_possible(_board, move):\n",
    "                raise InvalidTurn(\"An action should be taken. A turn is possible.\")\n",
    "            return\n",
    "\n",
    "        # noinspection PyTypeChecker\n",
    "        if _board[tuple(move.tolist())] != 0:\n",
    "            raise InvalidTurn(\"This turn is not possible.\")\n",
    "\n",
    "        action = False\n",
    "        for direction in DIRECTIONS:\n",
    "            if _do_directional_move(_board, move, direction):\n",
    "                action = True\n",
    "        if not action:\n",
    "            raise InvalidTurn(\"This turn is not possible.\")\n",
    "\n",
    "        # noinspection PyTypeChecker\n",
    "        _board[tuple(move.tolist())] = 1\n",
    "\n",
    "    boards = boards.copy()\n",
    "    for game in range(boards.shape[0]):\n",
    "        _do_move(boards[game], moves[game])\n",
    "    return boards\n",
    "\n",
    "\n",
    "%timeit do_moves(get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE))[0]\n",
    "\n",
    "plot_othello_board(\n",
    "    do_moves(\n",
    "        get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE)\n",
    "    )[0],\n",
    "    action=np.array([2, 3]),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An abstract reversi game policy\n",
    "\n",
    "For an easy use of policies an abstract class containing the policy generation / requests an action in an inherited instance of this class.\n",
    "This class filters the policy to only propose valid actions. Inherited instance do not need to care about this. This super class also manges exploration and exploitation with the epsilon value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GamePolicy(ABC):\n",
    "    \"\"\"\n",
    "    A game policy. Proposes where to place a stone next.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            epsilon: the epsilon / greedy value. Should be between zero and one. Set the mixture of policy and exploration. One means only the policy is used. Zero means only random policies are used. All mixtures inbetween between are possible.\n",
    "        \"\"\"\n",
    "        if 0 > epsilon > 1:\n",
    "            raise ValueError(\"Epsilon should be between zero and one.\")\n",
    "        self._epsilon: float = epsilon\n",
    "\n",
    "    @property\n",
    "    def epsilon(self):\n",
    "        return self._epsilon\n",
    "\n",
    "    @property\n",
    "    @abc.abstractmethod\n",
    "    def policy_name(self) -> str:\n",
    "        \"\"\"The name of this policy\"\"\"\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    @abc.abstractmethod\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        \"\"\"The internal policy is an unfiltered policy. It should only be called from inside this function\n",
    "\n",
    "        Args:\n",
    "            boards: A board where a policy should be calculated for.\n",
    "\n",
    "        Returns:\n",
    "            The policy for this board. Should have the same size as the boards array.\n",
    "        \"\"\"\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def get_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        \"\"\"Calculates the policy that should be followed.\n",
    "\n",
    "        Calculates the policy that should be followed.\n",
    "        This function does include the usage of epsilon to configure greediness and exploration.\n",
    "\n",
    "        Args:\n",
    "            boards: A set of boards that show the environment where the policy should be calculated for.\n",
    "\n",
    "        Returns:\n",
    "            A vector of indices. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
    "        \"\"\"\n",
    "        assert len(boards.shape) == 3\n",
    "        assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n",
    "\n",
    "        if self.epsilon <= 0:\n",
    "            policies = np.random.rand(*boards.shape)\n",
    "        else:\n",
    "            policies = self._internal_policy(boards)\n",
    "            if self.epsilon < 1:\n",
    "                random_choices = self.epsilon <= np.random.rand((boards.shape[0]))\n",
    "                policies[random_choices] = np.random.rand(np.sum(random_choices), 8 ,8)\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": [
      "1.36 s ± 131 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1.27 s ± 59.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def single_turn(\n",
    "    current_boards: np, policy: GamePolicy\n",
    ") -> tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"Execute a single turn on a board.\n",
    "\n",
    "    Places a new stone on the board. Turns captured enemy stones.\n",
    "\n",
    "    Args:\n",
    "        current_boards: The current board before the game.\n",
    "        policy: The game policy to be used.\n",
    "\n",
    "    Returns:\n",
    "        The new game board and the policy vector containing the index of the action used.\n",
    "    \"\"\"\n",
    "    policy_results = policy.get_policy(current_boards)\n",
    "\n",
    "    # if the constant VERIFY_POLICY is set to true the policy is verified. Should be good though.\n",
    "    # todo deactivate the policy verification after some testing.\n",
    "    if VERIFY_POLICY:\n",
    "        assert np.all(moves_possible(current_boards, policy_results)), (\n",
    "            current_boards[(moves_possible(current_boards, policy_results) == False)],\n",
    "            policy_results[(moves_possible(current_boards, policy_results) == False)],\n",
    "            np.where(moves_possible(current_boards, policy_results) == False),\n",
    "        )\n",
    "    return do_moves(current_boards, policy_results), policy_results\n",
    "\n",
    "\n",
    "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "VERIFY_POLICY = False  # type: ignore\n",
    "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "VERIFY_POLICY = True  # type: ignore\n",
    "_turn_result = single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "plot_othello_boards(_turn_result[0][:8], _turn_result[1][:8])\n",
    "del _turn_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulate a stack of games\n",
    "This function will simulate a stack of games and return an array of policies and histories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x4800 with 61 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def simulate_game(\n",
    "    nr_of_games: int,\n",
    "    policies: tuple[GamePolicy, GamePolicy],\n",
    "    tqdm_on: bool = False,\n",
    ") -> tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"Simulates a stack of games.\n",
    "\n",
    "    Args:\n",
    "        nr_of_games: The number of games that should be simulated.\n",
    "        policies: The policies that should be used to simulate the game.\n",
    "        tqdm_on: Switches tqdm on.\n",
    "\n",
    "    Returns:\n",
    "        A stack of board histories and actions.\n",
    "    \"\"\"\n",
    "    board_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 8, 8), dtype=np.int8)\n",
    "    action_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 2), dtype=np.int8)\n",
    "    current_boards = get_new_games(nr_of_games)\n",
    "    for turn_index in tqdm(range(SIMULATE_TURNS)) if tqdm_on else range(SIMULATE_TURNS):\n",
    "        policy_index = turn_index % 2\n",
    "        policy = policies[policy_index]\n",
    "        board_history_stack[turn_index, :, :, :] = current_boards\n",
    "        if policy_index == 0:\n",
    "            current_boards *= -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 *= -1\n",
    "\n",
    "    return board_history_stack, action_history_stack\n",
    "\n",
    "\n",
    "simulation_results = simulate_game(1, (RandomPolicy(1), RandomPolicy(1)))\n",
    "_unique_bords, _unique_actions = drop_duplicate_boards(\n",
    "    simulation_results[0].reshape(-1, 8, 8), simulation_results[1].reshape(-1, 2)\n",
    ")\n",
    "plot_othello_boards(_unique_bords, actions=_unique_actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 8, 8)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(simulation_results[0], (-1, 8, 8)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 2)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulation_results[1].reshape(-1, 2).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 340.90 MiB, increment: 0.12 MiB\n",
      "11.5 s ± 344 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%memit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n",
    "%timeit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical examination of the natural action space and result\n",
    "As for many project some evaluation of the project is in order.\n",
    "\n",
    "1. What is the expected distribution of scores\n",
    "2. What is the expected distribution of possible actions\n",
    "\n",
    "    a. over time\n",
    "    \n",
    "    b. ober space\n",
    "\n",
    "The easiest and robustest way to analyse this is when analyzing randomly played games."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this purpose we played a sample of 10k games and saved them for later analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70, 10000, 8, 8), (70, 10000, 2))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists(\"rnd_history.npy\") and not os.path.exists(\"rnd_action.npy\"):\n",
    "    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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000, 8, 8)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists(\"turn_possible.npy\"):\n",
    "    __board_history = _board_history.copy()\n",
    "    __board_history[1::2] = __board_history[1::2] * -1\n",
    "\n",
    "    _poss_turns = get_possible_turns(\n",
    "        __board_history.reshape((-1, 8, 8)), tqdm_on=True\n",
    "    ).reshape((SIMULATE_TURNS, -1, 8, 8))\n",
    "    np.save(\"turn_possible.npy\", _poss_turns)\n",
    "    del __board_history\n",
    "_poss_turns = np.load(\"turn_possible.npy\")\n",
    "_poss_turns.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those possible turms then where counted for all games in the history stack."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The action space size can be drawn into a histogram by turn and a curve over the mean action space size.\n",
    "This can be used to analyse in which area of the game that cant be solved absolutely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c36688a82346478b93118b2d2e49ccfb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=34, description='turn', max=69), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_poss_turns = np.sum(_poss_turns, axis=(2, 3))\n",
    "mean_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",
    "    # todo what happens here=\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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total mean action-space</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>5.687159e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black</th>\n",
       "      <td>3.753117e+20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total mean action-space\n",
       "white             5.687159e+18\n",
       "black             3.753117e+20"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000, 8, 8)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_poss_turns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71b3b16d0c884da09b6e9bd0b1e401f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=34, description='turn', max=69), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_poss_turn = np.mean(_poss_turns, axis=1)\n",
    "del _poss_turns\n",
    "\n",
    "\n",
    "@interact(turn=(0, 69))\n",
    "def turn_distribution_heatmap(turn):\n",
    "    turn_possibility_on_field = mean_poss_turn[turn]\n",
    "\n",
    "    sns.heatmap(\n",
    "        turn_possibility_on_field,\n",
    "        linewidth=0.5,\n",
    "        square=True,\n",
    "        annot=True,\n",
    "        xticklabels=\"ABCDEFGH\",\n",
    "        yticklabels=list(range(1, 9)),\n",
    "    )\n",
    "    plt.title(f\"Headmap of where stones can be placed on turn {turn}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.578125\n"
     ]
    }
   ],
   "source": [
    "def calculate_direct_score(board_history: np.ndarray) -> np.ndarray:\n",
    "    boards_evaluated = np.reshape(\n",
    "        evaluate_boards(np.reshape(board_history, (-1, 8, 8))), (SIMULATE_TURNS, -1)\n",
    "    )\n",
    "    direct_score = boards_evaluated - np.roll(boards_evaluated, shift=-1, axis=0)\n",
    "    direct_score[-1] = 0\n",
    "    return direct_score / 64\n",
    "\n",
    "\n",
    "print(np.max(np.abs(calculate_direct_score(_board_history))))\n",
    "assert len(calculate_direct_score(_board_history).shape) == 2\n",
    "assert calculate_direct_score(_board_history).shape[0] == SIMULATE_TURNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80dfe70149954bd7a4b8c591342f6e92",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=29, description='turn', max=59), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_history = calculate_direct_score(_board_history) * 64\n",
    "score_history[1::2] = score_history[1::2] * -1\n",
    "\n",
    "\n",
    "@interact(turn=(0, 59))\n",
    "def hist_direct_score(turn):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis / total size 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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_final_evaluation_for_history(board_history: np.ndarray) -> np.ndarray:\n",
    "    final_evaluation = final_boards_evaluation(board_history[-1])\n",
    "    return final_evaluation / 64\n",
    "\n",
    "\n",
    "print(np.max(np.abs(calculate_final_evaluation_for_history(_board_history))))\n",
    "assert len(calculate_final_evaluation_for_history(_board_history).shape) == 1\n",
    "_final_eval = calculate_final_evaluation_for_history(_board_history)\n",
    "plt.title(\"Histogram over the score distribution\")\n",
    "plt.hist((_final_eval * 64), density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_who_won(board_history: np.ndarray) -> np.ndarray:\n",
    "    who_won = evaluate_who_won(board_history[-1])\n",
    "    return who_won\n",
    "\n",
    "\n",
    "plt.title(\"Win 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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def history_changed(board_history: np.ndarray) -> np.ndarray:\n",
    "    return ~np.all(\n",
    "        np.roll(board_history, shift=1, axis=0) == board_history, axis=(2, 3)\n",
    "    )\n",
    "\n",
    "\n",
    "plt.title(\"Share of turns skipped\")\n",
    "plt.plot(1 - np.mean(history_changed(_board_history), axis=1))\n",
    "plt.xlabel(\"Turn\")\n",
    "plt.ylabel(\"Factor of skipped turns\")\n",
    "plt.yscale(\"log\", base=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_gamma_table(board_history, gamma_value: float):\n",
    "    unchanged = history_changed(board_history)\n",
    "    gamma_values = np.ones_like(unchanged, dtype=float)\n",
    "    gamma_values[unchanged] = gamma_value\n",
    "    return gamma_values\n",
    "\n",
    "\n",
    "get_gamma_table(_board_history, 0.8).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09677184,  0.0037773 ,  0.12190913,  0.03519891,  0.16118614,\n",
       "        0.00617017,  0.12490022, -0.03918723,  0.14632847, -0.01240192,\n",
       "        0.1016851 ,  0.00991888,  0.1295861 , -0.03332988,  0.07552515,\n",
       "       -0.10090606,  0.14730492, -0.08930635,  0.08367957, -0.09071304,\n",
       "        0.1600462 ,  0.08287025,  0.22077531, -0.07559336,  0.1789458 ,\n",
       "        0.02836975,  0.23077469,  0.01503086,  0.13597608, -0.18159241,\n",
       "       -0.03167801, -0.23491001,  0.05792499, -0.04478127,  0.06121092,\n",
       "       -0.04067385,  0.37884519,  0.04386898,  0.17202373, -0.05840784,\n",
       "        0.0441777 , -0.14009038,  0.02019953, -0.09193809,  0.15851489,\n",
       "        0.08095611,  0.45275764,  0.13625955,  0.36563693, -0.05076633,\n",
       "        0.28810459, -0.22580677, -0.16507096, -0.5579012 , -0.033314  ,\n",
       "       -0.15883   ,  0.23115   , -0.45325   , -0.37125   , -0.58125   ,\n",
       "       -0.21875   , -0.21875   , -0.21875   , -0.21875   , -0.21875   ,\n",
       "       -0.21875   , -0.21875   , -0.21875   , -0.21875   , -0.21875   ])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calculate_q_reword(\n",
    "    board_history: np.ndarray,\n",
    "    who_won_fraction: float = 0.2,\n",
    "    final_score_fraction=0.2,\n",
    "    gamma=0.8,\n",
    ") -> np.ndarray:\n",
    "    assert who_won_fraction + final_score_fraction <= 1\n",
    "    assert final_score_fraction >= 0\n",
    "    assert who_won_fraction >= 0\n",
    "\n",
    "    gama_table = get_gamma_table(board_history, gamma)\n",
    "    combined_score = np.zeros_like(gama_table)\n",
    "    combined_score += calculate_direct_score(board_history) * (\n",
    "        1 - who_won_fraction + final_score_fraction\n",
    "    )\n",
    "    combined_score[-1] += (\n",
    "        calculate_final_evaluation_for_history(board_history) * final_score_fraction\n",
    "    )\n",
    "    combined_score[-1] += calculate_who_won(board_history) * who_won_fraction\n",
    "    for turn in range(SIMULATE_TURNS - 1, 0, -1):\n",
    "        values = gama_table[turn] * combined_score[turn]\n",
    "        combined_score[turn - 1] += values\n",
    "\n",
    "    return combined_score\n",
    "\n",
    "\n",
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.8, who_won_fraction=0, final_score_fraction=1\n",
    ")[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.53249554e-06, -1.91561943e-06, -2.39452428e-06, -2.99315535e-06,\n",
       "       -3.74144419e-06, -4.67680524e-06, -5.84600655e-06, -7.30750819e-06,\n",
       "       -9.13438523e-06, -1.14179815e-05, -1.42724769e-05, -1.78405962e-05,\n",
       "       -2.23007452e-05, -2.78759315e-05, -3.48449144e-05, -4.35561430e-05,\n",
       "       -5.44451787e-05, -6.80564734e-05, -8.50705917e-05, -1.06338240e-04,\n",
       "       -1.32922800e-04, -1.66153499e-04, -2.07691874e-04, -2.59614843e-04,\n",
       "       -3.24518554e-04, -4.05648192e-04, -5.07060240e-04, -6.33825300e-04,\n",
       "       -7.92281625e-04, -9.90352031e-04, -1.23794004e-03, -1.54742505e-03,\n",
       "       -1.93428131e-03, -2.41785164e-03, -3.02231455e-03, -3.77789319e-03,\n",
       "       -4.72236648e-03, -5.90295810e-03, -7.37869763e-03, -9.22337204e-03,\n",
       "       -1.15292150e-02, -1.44115188e-02, -1.80143985e-02, -2.25179981e-02,\n",
       "       -2.81474977e-02, -3.51843721e-02, -4.39804651e-02, -5.49755814e-02,\n",
       "       -6.87194767e-02, -8.58993459e-02, -1.07374182e-01, -1.34217728e-01,\n",
       "       -1.67772160e-01, -2.09715200e-01, -2.62144000e-01, -3.27680000e-01,\n",
       "       -4.09600000e-01, -5.12000000e-01, -6.40000000e-01, -8.00000000e-01,\n",
       "       -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00,\n",
       "       -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00,\n",
       "       -1.00000000e+00, -1.00000000e+00])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.8, who_won_fraction=1, final_score_fraction=0\n",
    ")[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.09670969,   0.12088712,   3.9011089 ,   1.12638612,\n",
       "         5.15798265,   0.19747831,   3.99684789,  -1.25394014,\n",
       "         4.68257483,  -0.39678147,   3.25402317,   0.31752896,\n",
       "         4.1469112 ,  -1.066361  ,   2.41704875,  -3.22868907,\n",
       "         4.71413867,  -2.85732667,   2.67834167,  -2.90207292,\n",
       "         5.12240885,   2.65301107,   7.06626383,  -2.41717021,\n",
       "         5.72853724,   0.91067155,   7.38833944,   0.4854243 ,\n",
       "         4.35678037,  -5.80402453,  -1.00503067,  -7.50628834,\n",
       "         1.86713958,  -1.41607552,   1.9799056 ,  -1.27511801,\n",
       "        12.15610249,   1.44512812,   5.55641015,  -1.80448732,\n",
       "         1.49439085,  -4.38201144,   0.77248571,  -2.78439287,\n",
       "         5.26950892,   2.83688614,  14.79610768,   4.7451346 ,\n",
       "        12.18141825,  -1.02322719,   9.97096602,  -6.28629248,\n",
       "        -4.1078656 , -16.384832  ,   0.76896   ,  -2.7888    ,\n",
       "        10.264     , -10.92      ,  -7.4       , -13.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.8, who_won_fraction=0, final_score_fraction=0\n",
    ")[:, 0] * 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def weights_init_normal(m):\n",
    "    \"\"\"Takes in a module and initializes all linear layers with weight\n",
    "       values taken from a normal distribution.\n",
    "    Source: https://stackoverflow.com/a/55546528/11003343\n",
    "    \"\"\"\n",
    "\n",
    "    classname = m.__class__.__name__\n",
    "    # for every Linear layer in a model\n",
    "    if classname.find(\"Linear\") != -1:\n",
    "        y = m.in_features\n",
    "        # m.weight.data 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": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]], grad_fn=<TanhBackward0>)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BATCH_SIZE = 1000\n",
    "\n",
    "\n",
    "class DQLNet(nn.Module):\n",
    "    def __init__(self, load_from: str | None = None):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(8 * 8 * 2, 128 * 2)\n",
    "        # self.nb1 = nn.BatchNorm1d([128 * 2])\n",
    "        self.fc2 = nn.Linear(128 * 2, 128 * 3)\n",
    "        # self.nb2 = nn.BatchNorm1d([128 * 3])\n",
    "        self.fc3 = nn.Linear(128 * 3, 128 * 2)\n",
    "        self.fc4 = nn.Linear(128 * 2, 1)\n",
    "        if not load_from:\n",
    "            self.apply(weights_init_normal)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if isinstance(x, np.ndarray):\n",
    "            x = torch.from_numpy(x).float()\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        # x = self.nb1(x)\n",
    "        # x = self.dropout1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        # x = self.nb2(x)\n",
    "        x = self.fc3(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc4(x)\n",
    "        x = torch.tanh(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "DQLNet().forward(np.zeros((5, 2, 8, 8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SymmetryMode(Enum):\n",
    "    MULTIPLY = \"MULTIPLY\"\n",
    "    BREAK_SEQUENCE = \"BREAK_SEQUENCE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70, 100, 8, 8), (70, 100, 2))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_board_history, _action_history = simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n",
    "_board_history.shape, _action_history.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 3],\n",
       "       [5, 2],\n",
       "       [5, 1],\n",
       "       [6, 3],\n",
       "       [3, 5],\n",
       "       [5, 0],\n",
       "       [6, 2],\n",
       "       [7, 3],\n",
       "       [4, 2],\n",
       "       [2, 4],\n",
       "       [1, 3],\n",
       "       [6, 4],\n",
       "       [2, 2],\n",
       "       [2, 6],\n",
       "       [4, 1],\n",
       "       [0, 2],\n",
       "       [4, 5],\n",
       "       [3, 1],\n",
       "       [4, 0],\n",
       "       [3, 0],\n",
       "       [2, 5],\n",
       "       [1, 6],\n",
       "       [1, 4],\n",
       "       [2, 3],\n",
       "       [0, 7],\n",
       "       [0, 4],\n",
       "       [7, 1],\n",
       "       [5, 4],\n",
       "       [2, 1],\n",
       "       [5, 6],\n",
       "       [2, 0],\n",
       "       [1, 1],\n",
       "       [3, 2],\n",
       "       [6, 1],\n",
       "       [0, 5],\n",
       "       [1, 2],\n",
       "       [7, 2],\n",
       "       [7, 0],\n",
       "       [6, 0],\n",
       "       [1, 0],\n",
       "       [0, 3],\n",
       "       [4, 6],\n",
       "       [1, 7],\n",
       "       [1, 5],\n",
       "       [7, 4],\n",
       "       [7, 5],\n",
       "       [4, 7],\n",
       "       [3, 6],\n",
       "       [0, 1],\n",
       "       [3, 7],\n",
       "       [5, 5],\n",
       "       [6, 5],\n",
       "       [6, 6],\n",
       "       [0, 0],\n",
       "       [2, 7],\n",
       "       [5, 7],\n",
       "       [7, 7],\n",
       "       [0, 6],\n",
       "       [6, 7],\n",
       "       [7, 6]], dtype=int8)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_action_history[:60, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 100, 8, 8)\n",
      "(70, 100, 2)\n",
      "(70, 100, 2, 8, 8)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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[:, :, 0, :, :] = board_history\n",
    "    q_learning_format[:, :, 1, :, :] = -1\n",
    "\n",
    "    game_index = list(range(board_history.shape[1]))\n",
    "    for turn_index in range(SIMULATE_TURNS):\n",
    "        q_learning_format[\n",
    "            turn_index,\n",
    "            game_index,\n",
    "            1,\n",
    "            action_history[turn_index, game_index, 0],\n",
    "            action_history[turn_index, game_index, 1],\n",
    "        ] = 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",
    "print(_board_history.shape)\n",
    "print(_action_history.shape)\n",
    "print(action_to_q_learning_format(_board_history, _action_history).shape)\n",
    "plot_othello_boards(\n",
    "    action_to_q_learning_format(_board_history, _action_history)[:8, 0, 0]\n",
    ")\n",
    "plot_othello_boards(\n",
    "    action_to_q_learning_format(_board_history, _action_history)[:8, 0, 1]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43.4 ms ± 2.56 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "peak memory: 371.84 MiB, increment: 0.00 MiB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2, 2, 2, 70, 100, 2, 8, 8)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_symetry_action(\n",
    "    board_history: np.ndarray, action_history: np.ndarray\n",
    ") -> np.ndarray:\n",
    "    board_history = board_history.copy()\n",
    "    board_history[::2] *= -1\n",
    "    q_learning_format = np.zeros(\n",
    "        (2, 2, 2, SIMULATE_TURNS, board_history.shape[1], 2, 8, 8)\n",
    "    )\n",
    "    q_learning_format[0, 0, 0, :, :, :, :, :] = action_to_q_learning_format(\n",
    "        board_history, action_history\n",
    "    )\n",
    "    q_learning_format[1, 0, 0, :, :, :, :, :] = np.transpose(\n",
    "        q_learning_format[0, 0, 0, :, :, :, :, :], [0, 1, 2, 4, 3]\n",
    "    )\n",
    "    q_learning_format[:, 1, 0, :, :, :, :, :] = q_learning_format[\n",
    "        :, 0, 0, :, :, :, ::-1, :\n",
    "    ]\n",
    "    q_learning_format[:, :, 1, :, :, :, :, :] = q_learning_format[\n",
    "        :, :, 0, :, :, :, :, ::-1\n",
    "    ]\n",
    "    return q_learning_format\n",
    "\n",
    "\n",
    "%timeit build_symetry_action(_board_history, _action_history)\n",
    "%memit build_symetry_action(_board_history, _action_history)\n",
    "build_symetry_action(_board_history, _action_history).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def live_history(training_history: pd.DataFrame, 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": 50,
   "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[range(boards.shape[0]), action_x, action_y]\n",
    "            ].copy()\n",
    "            _q_learning_board[\n",
    "                range(_q_learning_board.shape[0]), 1, action_x, action_y\n",
    "            ] = 1\n",
    "\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(\n",
    "        self, generate_data_size: int\n",
    "    ) -> tuple[torch.Tensor, torch.Tensor]:\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",
    "        q_rewords[::2, :] *= -1\n",
    "        if self.symmetry_mode == SymmetryMode.MULTIPLY:\n",
    "            new_q_rewords = np.zeros((2, 2, 2) + q_rewords.shape)\n",
    "            for i, k, j in itertools.product((0, 1), (0, 1), (0, 1)):\n",
    "                new_q_rewords[i, k, j] = q_rewords\n",
    "            q_rewords = new_q_rewords\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, 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\n",
    "\n",
    "\n",
    "ql_policy1 = 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",
    "t1, t2 = ql_policy1._internal_policy(get_new_games(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Symmetry debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10, 8, 8)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_boards, train_actions = simulate_game(10, (RandomPolicy(0), RandomPolicy(0)))\n",
    "action_possible = ~np.all(train_actions[:, :] == -1, axis=2)\n",
    "q_leaning_formatted_action = action_to_q_learning_format(train_boards, train_actions)\n",
    "train_boards.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fvlyvvfZa62r6ypUrNWHChDbbr1+/Xr/73e+0du1aHX744RHN/Nvf/qYTTzxRgwYN6vQ2wWBQhx12WIffa2xs1A033KA77rhDM2fObL3+6KOPjsvs3r17t84+/PDDdd111+m73/1uu9vNmzdP+/bt03/9139p6dKl3c4GEL2edNSNN96o66+/Xrfcckub++vOK6+8ovT0dJ100kmd3iYQCHTaUS0O7JJIxaOj+vTpo2uuuab166FDh+raa6/VHXfcEVUWAF3r6WuoRHVEV6+hWixdulR///vf9eSTT0b8GuaZZ57RxIkTlZ6e3ultcnJyWmcfccQRmj59eoeLYv369Yv6ZwcQuVj7ye/3t3tuLl68WN/85jeVnZ3d5cx49NONN97Y+t9Dhw7VLbfcogsuuEANDQ1ddk88++knP/mJRo8erbPOOkuvvPJKp/cHOzhS6hA1YcIErVixovXrFStW6IwzztD48eNbr//ss8+0evXqNi+o9u/fr8suu0x33XVXVC8slixZoilTpnR5m5UrV2rgwIEaNWqUrrnmGn3yySet33vjjTf00UcfKS0tTV/4whd0+OGHq6SkRG+99VZcZh/o008/1WOPPaZTTjmlzfVvv/22br31Vv3lL3/p9qgLAD0TS0d9/PHHWr16tQYOHKjTTjtNgwYN0vjx4zs8oulgS5Ys0aRJk7o8ArSsrEwFBQUaMWKEpk6dqm3btrW7zYMPPqj+/fvr2GOP1ezZs7V///6IZsejow5UUVGhp556qsu/HgKITayvoaTEdURXr6EkadeuXbrqqqv0f//3f+rdu3ekP2rU/fTRRx/pr3/9a4f9NHnyZA0cOFBf/vKXtWTJkojvE0DketJPLdatW6f169e3ORCgM/HopwN9+umnevDBB3Xaaad1udgU6ewDddZPy5cv1+OPP6677ror4vuCxwwOSffee6/JysoyDQ0NpqqqygQCAfPxxx+bhx56yJx++unGGGNefPFFI8l8+OGHrdtdffXVZubMma1fSzKLFy/uclZdXZ3Jzs42b731Vqe3efjhh80zzzxjSktLzeLFi82YMWPMSSedZBobG1u/L8kMGTLEPPHEE2bt2rXm0ksvNf369TOffPJJj2aPHz/epKenm6ysLNO7d28jyRx11FFmy5Ytbe6nuLjY/N///Z8xxpgVK1YYSWbv3r1d/uwAYhNLR7366qtGkunbt6+5//77zRtvvGF+8IMfmIyMDPPee+91OW/kyJHm2Wef7fT7zz33nHnsscfMhg0bzPPPP29OPfVUM2TIEFNVVdV6m7vvvts8//zzprS01CxatMgcccQR5sILL+xybrw6qsW3vvUt06tXLyPJTJo0yXz22WddzgcQvVhfQyWqI7p7DdXU1GQmTpxofvnLXxpjjNmyZYuRZN58880uZ2/fvt1kZGR0+Vpn6NChJiMjw2RlZZnMzEwjyZxyyiltttm9e7f53e9+Z1577TXz+uuvm5tvvtn4fD7zzDPPdDkfQPRi7acDXXPNNWbMmDHdzopHP7X40Y9+1Poa50tf+pLZs2dPl7Pj1U979uwxhYWFZtWqVcYYYxYsWGDy8vK6/dnhLRalDlFlZWVGknnllVfM3/72N3P00UcbY4z56KOPTDAYNJ999pn56U9/akaMGNG6zTPPPGOKiopMdXV163WRLEo999xzbe4nEu+//76RZJYtW2aMMebBBx80kszdd9/depu6ujrTv39/8+c//7lHs8ePH29mzJhhysrKTFlZmXn55ZfNueeea0aOHNn6D84bb7zRXHLJJa3bsCgFJFYsHfWvf/3LSDKzZ89uc1/HHXecueWWWzqd9fbbb5usrKyoFnD27t1rcnNzzX333dfpbVpe9G3evLnT28Sro1rs2LHDbNq0yTzzzDPm6KOPNtdcc03EPxOAyMTSTx2JV0cc7ODXUH/4wx/MuHHjWv8RGOmi1J/+9Cdz5plndnmboUOHmh//+MemrKzMvPfee+bFF180p5xyijnttNPa/aPzQN/+9rfNl7/85ah+LgDd62k/7d+/3+Tl5Znf/va33c6KRz+12L17t3n33XfN3//+dzNu3Dhz7rnnmqampk7vJ179dOGFF5qbb765dRsWpdzEe5QOUUVFRRo8eLBWrFihFStWtL7Fo6CgQIWFhXrllVe0YsUKnXnmma3bLF++XO+//77y8/MVCAQUCDSfcuyiiy5q94kOB1qyZEmXJw/uyIgRI9S/f39t3rxZklrPXXXgOaSCwaBGjBjR4Vtoop2dl5enoqIiFRUVady4cZo/f77Kysr06KOPSvr8sM6Wn/uss86SJPXv318/+9nPovrZAHQvlo7qqCckacyYMd32xNlnn63MzMyI8+Xn5+uoo45q7aiOtBwe3tVt4tVRLQ477DCNHj1akydP1t1336158+Zpx44dEf5UACIRSz91JJ4dcaCDX0MtX75cr776qoLBoAKBgIqKiiRJX/ziFzV9+vQez+7fv7+Kioo0cuRInXnmmbrzzjtbH4POnHLKKV3+3ABi09N+euKJJ7R//35dfvnl3c6KRz+16N+/v4466iidffbZeuSRR/Tcc891+Qmd8eqn5cuX67e//W3rv/FmzpypyspKBQIB3X///VH9bEgcFqUOYRMmTNDKlSu1cuXKNotKp59+upYuXarXX3+9zXuNb7nlFpWWlmr9+vWtF0n6/e9/rwULFnQ4wxijv/71r1G931eStm/frk8++aT1H5ljx45VMBjUu+++23qbhoYGbd26VUOHDo3rbKn5hH+SWj9G/sknn9SGDRtaf+6Wjz395z//qVmzZkV9/wC6F21HDRs2TAUFBW16QpLee++9TntCaj5RZrQ9UVNTo/fff7/LD3to6cjObhPPjupIU1OTpOaPgAcQX9H2U0cS1REHv4b63//93zavYZ577jlJ0qOPPqrbbrutw/uoqanRihUrEtZP69evj/jDcgBEpyf9NH/+fE2ePFkDBgzocka8+qkj3b1+iWc/vfrqq23+bXvrrbcqJydH69ev14UXXhj1/SNBrB6nhYS6//77Ta9evUwgEDA7d+5svf6BBx4wOTk5RpKpqKjo8j7Uzdv31qxZY/r06WMaGho6vU11dbW56aabzKuvvmq2bNlili1bZk488UQzcuRIU1dX13q7G264wRxxxBHmhRdeMO+8846ZOXOmGThwoPn0009jnm1M81tjrrrqKrNjxw6zY8cOs379enPRRReZzMxM884773S4DW/fAxIvlo76/e9/b3Jzc83jjz9uysrKzE9+8hOTmZnZ6dtjdu3aZdLT083u3bu7zPL//X//n1m5cqXZsmWL+de//mW++tWvmv79+5uPP/7YGGPM5s2bza233mrWrl1rtmzZYp555hkzYsSI1vM3dCSeHfW3v/3N3H///Wbjxo1my5Yt5tlnnzVjxowx48aN6/K+AcQm2n5KVEdE+hrqQJG8fe/xxx83xx13XBePQLOhQ4eaW2+91ezYscNUVFSY1atXm/Hjx5sBAwa0nhNm4cKF5qGHHjKbNm0ymzZtMrfddptJS0sz999/f7f3DyB6sf4br6yszPh8PrN06dJuZ8Srn1577TXzxz/+0bz55ptm69at5sUXXzSnnXaaOfLIIzvtsHj208F4+56bWJQ6hLW8KBk9enSb67du3WokmVGjRnV7H90tSv3kJz8xU6dO7fI+9u/fb8455xwzYMAAk56eboYOHWquuuqq/7+dO0ZpLAqjAHynUQloUFCRKFhoYWNnIBAUwSYWaQSD4AJsJaiI9m4ihb0LSJfGHbgEsRZsg/JbCDIOk+gEver4ffDK3JPq8N7h8V6UaEREt9uNZrMZU1NTMTo6GhsbG30/rPeW7IinB76U0vM1Pj4ea2tr0el0ev7GKAUfb9COOjs7i9nZ2SgUClGpVOLy8rJnRqvVetNw02g0YmZmJoaGhqJUKkWj0XgxdF1fX8fq6mpMTEzE8PBwLCwsxMHBQdzd3fU88z07qtPpRKVSiWKxGCMjI7G4uBhHR0c6Cj7Iv/bTR3XEW++h/vbf+41Su7u7cXJy0jc74umh7/d+mpycjM3NzRdnn5+fx9LSUhQKhRgbG4tyuRwXFxevng0MZtD7p+Pj45ibm4uHh4dXM96rn66urmJ9ff25G+fn52Nvby9ubm56nvue/fQno9TX9CsiIscbWfyflpeX0+npadre3v5R2cD3UK/XU7VaTYeHh9mzdRTQz2d1xP39fZqenk7tdjuVy+Ws2cD3oJ/IyTelGFi3201bW1upVqv9qGzg+6hWq2lnZyd7ro4C+vnMjri9vU37+/tpZWUlezbw9ekncvOmFAAAAADZeVMKAAAAgOyMUgAAAABkZ5QCAAAAIDujFAAAAADZGaUAAAAAyM4oBQAAAEB2RikAAAAAsjNKAQAAAJCdUQoAAACA7IxSAAAAAGT3CDeOvuzQqyJOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_othello_boards(train_boards[:8, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_othello_boards(q_leaning_formatted_action[1:9, 0, 1])"
   ]
  },
  {
   "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": [
    "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": [
    "raise NotImplementedError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "boards_and_actions, score = ql_policy.generate_trainings_data(1)\n",
    "print(boards_and_actions.shape)\n",
    "print(score.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "boards_and_actions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_othello_boards(boards_and_actions[:8, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "score[:8, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sources\n",
    "\n",
    "* Game rules and example board images [https://en.wikipedia.org/wiki/Reversi](https://en.wikipedia.org/wiki/Reversi)\n",
    "* Game rules and example game images [https://de.wikipedia.org/wiki/Othello_(Spiel)](https://de.wikipedia.org/wiki/Othello_(Spiel))\n",
    "* Game strategy examples [https://de.wikipedia.org/wiki/Computer-Othello](https://de.wikipedia.org/wiki/Computer-Othello)\n",
    "* Image for 8 directions [https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281](https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "\n",
    "def sizeof_fmt(num, suffix=\"B\"):\n",
    "    \"\"\"by Fred Cirera,  https://stackoverflow.com/a/1094933/1870254, modified\"\"\"\n",
    "    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n",
    "        if abs(num) < 1024.0:\n",
    "            return \"%3.1f %s%s\" % (num, unit, suffix)\n",
    "        num /= 1024.0\n",
    "    return \"%.1f %s%s\" % (num, \"Yi\", suffix)\n",
    "\n",
    "\n",
    "for name, size in sorted(\n",
    "    ((name, sys.getsizeof(value)) for name, value in list(locals().items())),\n",
    "    key=lambda x: -x[1],\n",
    ")[:20]:\n",
    "    print(\"{:>30}: {:>8}\".format(name, sizeof_fmt(size)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "toc-autonumbering": true,
  "toc-showcode": false
 },
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}