{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deep Otello AI\n",
"\n",
"The game reversi is a very good game to apply deep learning methods to.\n",
"\n",
"Othello also known as reversi is a board game first published in 1883 by eiter Lewis Waterman or John W. Mollet in England (each one was denouncing the other as fraud).\n",
"It is a strickt turn based zero-sum game with a clear Markov chain and now hidden states like in card games with an unknown distribution of cards or unknown player allegiance.\n",
"There is like for the game go only one set of stones with two colors which is much easier to abstract than chess with its 6 unique pieces.\n",
"The game has a symmetrical game board wich allows to play with rotating the state around an axis to allow for a breaking of sequences or interesting ANN architectures, quadruple the data generation by simulation or interesting test cases where a symetry in turns should be observable if the AI reaches an \"objective\" policy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 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",
"\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",
"\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",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initial design decisions\n",
"\n",
"At the beginning of this project I made some design decisions.\n",
"The first onw was that I do not want to use a gym library because it limits the data formats accessible.\n",
"I choose to implement the hole game as entry in a stack in numpy arrays to be able to accommodate interfacing with a neural network easier and to use scipy pattern recognition tools to implement some game mechanics for a fast simulation cycle.\n",
"I chose to ignore player colors as far as I could instead a player perspective was used. Which allowed to change the perspective with a flipping of the sign. (multiplying with -1).\n",
"The array format should also allow for data multiplication or the breaking of strikt sequences by flipping the game along one the for axis, (horizontal, vertical, transpose along both diagonals).\n",
"\n",
"I wanted to implement different agents as classes that act on those game stacks.\n",
"\n",
"Since computation time is critical all computational have results are saved.\n",
"The analysis of those is then repeated in real time. If a recalculation of such a section is required the save file can be deleted and the code should be executed again."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%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, display\n",
"from pathlib import Path\n",
"import glob\n",
"import copy\n",
"from functools import lru_cache, wraps\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",
"from torch.nn import functional\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": {},
"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] = False\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": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[-1, -1],\n [-1, 0],\n [-1, 1],\n [ 0, -1],\n [ 0, 1],\n [ 1, -1],\n [ 1, 0],\n [ 1, 1]])"
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DIRECTIONS: Final[np.ndarray] = np.array(\n",
" [[i, j] for i in range(-1, 2) for j in range(-1, 2) if j != 0 or i != 0],\n",
" dtype=int,\n",
")\n",
"DIRECTIONS.setflags(write=False)\n",
"DIRECTIONS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another constant needed is the initial start square at the center of the board."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[-1, 1],\n [ 1, -1]])"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"START_SQUARE: Final[np.ndarray] = np.array(\n",
" [[ENEMY, PLAYER], [PLAYER, ENEMY]], dtype=int\n",
")\n",
"START_SQUARE.setflags(write=False)\n",
"START_SQUARE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating new boards\n",
"\n",
"The first function implemented and tested is a function to generate the starting environment as a stack of games.\n",
"As described above I simply placed a 2 by 2 square in the center of an empty stack of boards."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, -1, 1, 0, 0, 0],\n [ 0, 0, 0, 1, -1, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0]])"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def get_new_games(number_of_games: int) -> np.ndarray:\n",
" \"\"\"Generates a stack of initialised game boards.\n",
"\n",
" Args:\n",
" number_of_games: The size of the board stack.\n",
"\n",
" Returns: The generates stack of games as a stack n x 8 x 8.\n",
"\n",
" \"\"\"\n",
" empty = np.zeros([number_of_games, BOARD_SIZE, BOARD_SIZE], dtype=int)\n",
" empty[:, 3:5, 3:5] = START_SQUARE\n",
" return empty\n",
"\n",
"\n",
"get_new_games(1)[0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"test_number_of_games = 3\n",
"assert get_new_games(test_number_of_games).shape == (\n",
" test_number_of_games,\n",
" BOARD_SIZE,\n",
" BOARD_SIZE,\n",
")\n",
"np.testing.assert_equal(\n",
" get_new_games(test_number_of_games).sum(axis=1),\n",
" np.zeros(\n",
" [\n",
" test_number_of_games,\n",
" 8,\n",
" ]\n",
" ),\n",
")\n",
"np.testing.assert_equal(\n",
" get_new_games(test_number_of_games).sum(axis=2),\n",
" np.zeros(\n",
" [\n",
" test_number_of_games,\n",
" 8,\n",
" ]\n",
" ),\n",
")\n",
"assert np.all(get_new_games(test_number_of_games)[:, 3:4, 3:4] != 0)\n",
"del test_number_of_games"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualisation tools\n",
"\n",
"In this section a visualisation help was implemented for debugging of the game and a proper display of the results.\n",
"For this visualisation ChatGPT was used as a prompted code generator that was later reviewed and refactored by hand to integrate seamlessly into the project as a whole.\n",
"White stones represent the player, black stones the enemy. A single plot can be used as a subplot when the `ax` argument is used."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "
",
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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_othello_board(\n",
" board: np.ndarray | torch.Tensor,\n",
" action: np.ndarray | None = None,\n",
" score: float | 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",
" action: The action taken on each board.\n",
" score: The score reached with the turn.\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",
" # convert a tensor into an array\n",
" if isinstance(board, torch.Tensor):\n",
" board = board.cpu().detach().numpy()\n",
"\n",
" # ensure the shape of the array fits\n",
" assert board.shape == (8, 8)\n",
" plot_all = False\n",
"\n",
" # create a figure if no axis is given\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",
" # set the background color\n",
" ax.set_facecolor(\"#0f6b28\")\n",
"\n",
" # plot the actions\n",
" if action is not None:\n",
" ax.scatter(action[0], action[1], s=350 if plot_all else 200, c=\"red\")\n",
"\n",
" # plot black and white stones\n",
" for x_pos, y_pos in itertools.product(range(BOARD_SIZE), range(BOARD_SIZE)):\n",
" if board[x_pos, y_pos] == ENEMY:\n",
" color = \"white\"\n",
" elif board[x_pos, y_pos] == PLAYER:\n",
" color = \"black\"\n",
" else:\n",
" continue\n",
" ax.scatter(x_pos, y_pos, s=280 if plot_all else 140, c=color)\n",
"\n",
" # plot the lines separating the fields\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",
"\n",
" # define the size of the plot\n",
" ax.set_xlim(-0.5, 7.5)\n",
" ax.set_ylim(7.5, -0.5)\n",
"\n",
" # set the axis labels\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",
"\n",
" # overrides the x_label text if a score should be plotted\n",
" if score is None:\n",
" ax.set_xlabel(\n",
" f\"W{np.sum(board == ENEMY)} / {np.sum(board == 0)} / B{np.sum(board == PLAYER)}\"\n",
" )\n",
" else:\n",
" ax.set_xlabel(f\"Score: {score}\")\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": 9,
"metadata": {},
"outputs": [],
"source": [
"PLOTS_PER_ROW = 4\n",
"\n",
"\n",
"def plot_othello_boards(\n",
" boards: np.ndarray,\n",
" actions: np.ndarray | None = None,\n",
" scores: np.ndarray | None = None,\n",
") -> 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",
" actions: A list of actions taken on each of the boards.\n",
" scores: A list of scores reached at each board.\n",
" \"\"\"\n",
" # checking if the array input shapes do fit\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",
" if scores is not None:\n",
" assert len(scores.shape) == 1\n",
" assert boards.shape[0] == scores.shape[0]\n",
"\n",
" # plots the boards\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",
" score = scores[game_index] if scores is not None else None\n",
" plot_othello_board(boards[game_index], action=action, score=score, ax=ax)\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def drop_duplicate_boards(\n",
" boards: np.ndarray,\n",
" actions: np.ndarray | None,\n",
") -> tuple[np.ndarray, np.ndarray | None]:\n",
" \"\"\"Takes a sequence of boards and drops all boards that are unchanged.\n",
"\n",
" Args:\n",
" boards: A list of boards to be reduced.\n",
" actions: A list of actions to be reduced alongside the bords.\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": [
"## Hash Otello Boards\n",
"\n",
"A challenge for training any reinforcement learning algorithm is how to properly calibrate the exploration rate."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def hash_board(board: np.ndarray) -> int:\n",
" assert board.shape == (8, 8) or board.shape == (64,)\n",
" return hash(tuple(board.reshape(-1)))\n",
"\n",
"\n",
"def count_unique_boards(boards: np.ndarray) -> int:\n",
" return np.unique(\n",
" np.apply_along_axis(hash_board, axis=1, arr=boards.reshape(-1, 64))\n",
" ).size\n",
"\n",
"\n",
"a = count_unique_boards(np.random.randint(-1, 2, size=(10000, 8, 8)))"
]
},
{
"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": {
"tags": []
},
"outputs": [],
"source": [
"# Source https://gist.github.com/Susensio/61f4fee01150caaac1e10fc5f005eb75\n",
"def np_cache(*lru_args, array_argument_elements: tuple[int, ...] = (0,), **lru_kwargs):\n",
" \"\"\"\n",
" LRU cache implementation for functions whose parameter at ``array_argument_index`` is a numpy array of dimensions <= 2\n",
"\n",
" Example:\n",
" >>> from sem_env.utils.cache import np_cache\n",
" >>> array = np.array([[1, 2, 3], [4, 5, 6]])\n",
" >>> @np_cache(maxsize=256)\n",
" ... def multiply(array, factor):\n",
" ... return factor * array\n",
" >>> multiply(array, 2)\n",
" >>> multiply(array, 2)\n",
" >>> multiply.cache_info()\n",
" CacheInfo(hits=1, misses=1, maxsize=256, currsize=1)\n",
" \"\"\"\n",
"\n",
" def decorator(function):\n",
" @wraps(function)\n",
" def wrapper(*args, **kwargs):\n",
" args = list(args)\n",
" for array_argument_index in array_argument_elements:\n",
" np_array = args[array_argument_index]\n",
" if len(np_array.shape) > 2:\n",
" raise RuntimeError(\n",
" f\"np_cache is currently only supported for arrays of dim. less than 3 but got shape: {np_array.shape}\"\n",
" )\n",
" hashable_array = tuple(np_array.reshape(-1))\n",
"\n",
" args[array_argument_index] = hashable_array\n",
" return cached_wrapper(*args, **kwargs)\n",
"\n",
" @lru_cache(*lru_args, **lru_kwargs)\n",
" def cached_wrapper(*args, **kwargs):\n",
" args = list(args)\n",
" for array_argument_index in array_argument_elements:\n",
" hashable_array = args[array_argument_index]\n",
" array = np.array(hashable_array).reshape(8, 8)\n",
" args[array_argument_index] = array\n",
" return function(*args, **kwargs)\n",
"\n",
" # copy lru_cache attributes over too\n",
" wrapper.cache_info = cached_wrapper.cache_info\n",
" wrapper.cache_clear = cached_wrapper.cache_clear\n",
" return wrapper\n",
"\n",
" return decorator"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"153 µs ± 4.7 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops 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": 14,
"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",
"@np_cache(maxsize=2000, array_argument_elements=(0, 1))\n",
"def _get_possible_turns_for_board(board: np.ndarray, poss_turns: np.ndarray):\n",
" for idx, idy in itertools.product(range(BOARD_SIZE), range(BOARD_SIZE)):\n",
" if poss_turns[idx, idy]:\n",
" position = idx, idy\n",
" poss_turns[idx, idy] = any(\n",
" _recursive_steps(board[:, :], direction, position) > 0\n",
" for direction in DIRECTIONS\n",
" )\n",
" return poss_turns\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 = range(boards.shape[0])\n",
"\n",
" if tqdm_on:\n",
" iterate_over = tqdm(iterate_over, total=np.prod(boards.shape))\n",
" for game in iterate_over:\n",
" poss_turns[game] = _get_possible_turns_for_board(boards[game], poss_turns[game])\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": 15,
"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": 16,
"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": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"200 µs ± 4.9 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
"33.2 µs ± 786 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
"35.6 µs ± 346 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
]
}
],
"source": [
"def final_boards_evaluation(boards: np.ndarray) -> np.ndarray:\n",
" \"\"\"Evaluates the board at the end of the game.\n",
"\n",
" All unused fields are added to the score of the player that has more stones with his color up.\n",
" This score only applies to the end of the game.\n",
" Normally the score is represented by the number of stones each player has.\n",
" In this case the score was combined by building the difference.\n",
"\n",
" Args:\n",
" boards: A stack of game bords ot the end of the game.\n",
"\n",
" Returns:\n",
" the combined score for both player.\n",
" \"\"\"\n",
" score1, score2 = np.sum(boards == 1, axis=(1, 2)), np.sum(boards == -1, axis=(1, 2))\n",
" player_1_won = score1 > score2\n",
" player_2_won = score1 < score2\n",
" score1_final = 64 - score2[player_1_won]\n",
" score2_final = 64 - score1[player_2_won]\n",
" score = np.zeros(boards.shape[0])\n",
" score[player_1_won] = score1_final\n",
" score[player_2_won] = -score2_final\n",
" return score\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",
" assert boards.shape[1:] == (8, 8)\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": 18,
"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": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"93.8 ms ± 3.73 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
},
{
"data": {
"text/plain": "
",
"image/png": 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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def do_moves(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
" \"\"\"Executes a single move on a stack o Othello boards.\n",
"\n",
" Args:\n",
" boards: A stack of Othello boards where the next stone should be placed.\n",
" moves: A stack of stone placement orders for the game. Formatted as coordinates in an array [x, y] of the place where the stone should be placed. Should contain [-1,-1] if no new placement is possible.\n",
"\n",
" Returns:\n",
" The new state of the board.\n",
" \"\"\"\n",
"\n",
" def _do_directional_move(\n",
" board: np.ndarray, rec_move: np.ndarray, rev_direction, step_one=True\n",
" ) -> bool:\n",
" \"\"\"Changes the color of enemy stones in one direction.\n",
"\n",
" This function works recursive. The argument step_one should always be used in its default value.\n",
"\n",
" Args:\n",
" board: A bord on which a stone was placed.\n",
" rec_move: The position on the board in x and y where this function is called from. Will be moved by recursive called.\n",
" rev_direction: The position where the stone was placed. Inside this recursion it will also be the last step that was checked.\n",
" step_one: Set to true if this is the first step in the recursion. False later on.\n",
"\n",
" Returns:\n",
" True if a stone could be flipped.\n",
" All changes are made on the view of the numpy array and therefore not included in the return value.\n",
" \"\"\"\n",
" rec_position = rec_move + rev_direction\n",
" if np.any((rec_position >= 8) | (rec_position < 0)):\n",
" return False\n",
" next_field = board[tuple(rec_position.tolist())]\n",
" if next_field == 0:\n",
" return False\n",
" if next_field == 1:\n",
" return not step_one\n",
" if next_field == -1:\n",
" if _do_directional_move(board, rec_position, rev_direction, step_one=False):\n",
" board[tuple(rec_position.tolist())] = 1\n",
" return True\n",
" return False\n",
"\n",
" def _do_move(_board: np.ndarray, move: np.ndarray) -> None:\n",
" \"\"\"Executes a turn on a board.\n",
"\n",
" Args:\n",
" _board: The game board on wich to place a stone.\n",
" move: The coordinates of a stone that should be placed. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
"\n",
" Returns:\n",
" All changes are made on the view of the numpy array.\n",
" \"\"\"\n",
" if np.all(move == -1):\n",
" if not move_possible(_board, move):\n",
" raise InvalidTurn(\"An action should be taken. A turn is possible.\")\n",
" return\n",
"\n",
" # noinspection PyTypeChecker\n",
" if _board[tuple(move.tolist())] != 0:\n",
" raise InvalidTurn(\"This turn is not possible.\")\n",
"\n",
" action = False\n",
" for direction in DIRECTIONS:\n",
" if _do_directional_move(_board, move, direction):\n",
" action = True\n",
" if not action:\n",
" raise InvalidTurn(\"This turn is not possible.\")\n",
"\n",
" # noinspection PyTypeChecker\n",
" _board[tuple(move.tolist())] = 1\n",
"\n",
" boards = boards.copy()\n",
" for game in range(boards.shape[0]):\n",
" _do_move(boards[game], moves[game])\n",
" return boards\n",
"\n",
"\n",
"%timeit do_moves(get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE))[0]\n",
"\n",
"plot_othello_board(\n",
" do_moves(\n",
" get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE)\n",
" )[0],\n",
" action=np.array([2, 3]),\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## An abstract reversi game policy\n",
"\n",
"For an easy use of policies an abstract class containing the policy generation / requests an action in an inherited instance of this class.\n",
"This class filters the policy to only propose valid actions. Inherited instance do not need to care about this. This super class also manges exploration and exploitation with the epsilon value."
]
},
{
"cell_type": "code",
"execution_count": 20,
"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": 21,
"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": 22,
"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": 23,
"metadata": {},
"outputs": [],
"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"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"%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": 24,
"metadata": {},
"outputs": [
{
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oKPrJOTI79VbWKac3eP2DxrhiXOrfuY8yO/UOcTI4Ef3kH/opNOgnBCIa+kmio6Tq46sNP9xlz+oXET2S1kjaYMry8RUdheP5fb5c69atdf7553v/v8fj0dy5c3X22WcHNKBhGGoRlxTQMjWS4hLr/B2oIsOQ2UiG0qOhmbyILZKS0n3H8CdDY+zYDpHOkFFQIQ3pZWndvivLUK91a33GaQrbIVoyFBuGpeVqi3RHBbsNpMZfi3D3kz8ZGmPHdnBChr7pGZbXXVuf9J7aun+npQyNiZbXwo4MwXYU/eQ/+in4DPRTdGVo6v0kcYxXw47jq4WW1uxrs6SRhyssHV+Fu6OawmsRLRn87SfDNM1Av1UrSfrjH/+oefPmacGCBerZs2ejz3e73crLy9OoUaO0cGGoPg6BGTZsmA4cOKC0tDQtXbq03ufk5uZqypQpQY91//33a+TIkZYyhJvjM3g8GjBokDR+vDR7dvCDTZggvfCCVn/+uRTzv5MDHb8doihDTS/0799fLqtnxh2nOXZUuPvJnwx2aAoZ2rdvry5dgr/7y65du5Sfn28pgx3IUC3UHUU/NYx+Cj4D/RRdGegnjvH8yvDD8dVsSbeEYKy/SpogWTq+CndHOf61iKIM/vZTYFcW+8GTTz6pV199VU8//bRfZVXb4dJCDZ873sqwSopL1Ftjpmv0/IkqrQz81Mn8kgLv3w1lKN102FK2481a87peKVlmKUNj7NgOkc6wM8ZQfFGRpWw+jh5VRYyh4a/XreCmsB2iJUNxaaGlcRsSiY4KdhtIjb8W4e4nfzI0xo7t4IQMV2RfolnXT7W07tqeWPGc3l3zgaUMjYmW18KODKHsKPrpxOin4DPQT9GVoan3k8QxXg07jq+SPZbOR/HRWrJ8fBXujmoKr0W0ZPC3nwKelJo8ebLeeOMNPfnkk7rkkksCXVymaaqkMrjTJ0sryyyto+aksBNlcLcO/mtEklSVrHrH8CeDv8K5HSKdYXPbePXbtCmobF6bNmlTu3ifcZrCdoi2DKEQ6Y6yug1qxj5RhnD3kz8Z/BXO7eCEDBv2bracq856vtvMa9GEMgSLfmoc/RR8BvopOjMEK9L9JHGMVyOcx1e9DgV3Xa0avSTLx1fh7qim8FpEW4bGBHTh/RkzZujNN9/Un//8Z/30pz8NOFRT4EpNVEz7hKDWEdM+Qa5U698Nh/RZepIq1+ZJa9ZU30XPCrdbWr1alevWamUHa9+jRdPS3DuKfnKOdXs2au3ur+X2WOsnt8etvG+/0vrvQjT5DsejnxpHP4UG/YRANfd+kugoqfr4qq8h9Vdwd9/LltTHkOXjKzoKx/P7/bht2zbNmjVL48eP14ABA3Tw4EHvn+YmIbNtRJeHNK93iuJMSc8+W+d7ygFxuaTp0xVnVq8PzVu0dBT95ByzV8xVjGGtn1wxLs1eMTfEieBU9JM9y+N/6Cf4K1r6SaKjao6vJim4u+9NkoI+vqKjUJvf74SPP/5Ybrdbzz33nM4777w6f5qbuB6tZSTHSoGe5WlIRnKs4nq0DkuuaLI2NVGfdkhS1fw3pV27pMrKwFZQWSnt3Kmqt+br0w5JWtuEf6sB/0RLR9FPzrFw9VLtPrxXVe6qgJarclfp20PfKffL+q+bg+aHfmoE/RRy9BP8FS39JNFRNcdXV0rqIinQy+K7flhulBT08RUdhdr8npSaMGGCNm/eXO+f5saIjVHLEZ2l+Bj/S8uQFF+9nBFr9YRI1HZHTge5K8rlufhiqajI/4mpykqpqEieIUPkrijXHTkdwhsUjhAtHUU/OUdZZbl+NvNmFZUf83unqspdpaLyY7pixk0qqwzNdR3gfPTTiRYS/RQG9BP8FS39JNFRUvXxVbzL0Puqvli5vxNTrh+e/w9J8S4j6OMrOgq1Nf1PVpi4UuLV6squMlr5dy14o1WsWl3ZVa6U+DAnix47UuJ1W04HacsWec46S/ruu+ofNHSNqZrHv/uu+vlbt+q2nA7awWuCZoZ+co5tB3bqJ0+O0d7C/ZLU4PURah7fW7hfP3lyjLYf3GVbRsBO9JNz0E+Ar2jvqJrjq56SPpPU6YfHG5oUqHm80w/P7yGF7PiKjkKNgO++F01cKfFKvqabKrccVfm6AnnyfWdkY9onKCGzbfXpoM1g9txpFvZsLUOmZi7fJtfpvRU7eow0caI0YIDvk/PypOnTVfXWfLkrynXbkHQt7Nm0T7MFGkI/Ocf2g7s06LGfauQZQzVh8Dj179zH5znr92zS7BVzlfvlMn67h2aPfnIO+gnwFe0d9b/jq++13m1qoaRnJa2p57lZqr6G1ChVnyE1PqdDSI+v6ChITEo1yoiNUXzvFMX3TpH7YJlii6Tbsq/RrDWvqypZTfoODE3Fgp5ttDotSTOW79O5r76qytdeVVxWfykjQ2rdWjp6VNq8WZVr8xRnSivTkzTxwlM5QwrNHv3kHGWV5Xp91SK9vmqRMjv1Vp/0nrp38K16YsVz+mrvN1q3Z2OkIwK2op+cg34CfEV7R9U+vrphX6muNaQNprRZ0lFVf1UvQ1Jfo/qi5v9OT9LEC8PzDRQ6CkxKBcCVmqik9CSNHDlSr5QsU0llaaQjRY0dKfH66cguyjpYpms3FursPRvVa91axXtMVcQY2tQuXiv7pmhe7xQuao6oRD85x7o9G7V1/07d3OcqvbvmA14LRD36yTnoJ8BXtHaUz/HVvlKNPFxR5/jqlQ5Jth5f0VHRiUkpNClrUxO1NvVHkqQWcUlafM1fNfz1WygsAAAAAAgQx1eItOb1BVlEnxjewgAAAAAQEhxfwWa84wAAAAAAAGA7wzRN046B3G638vLyNPiCwSoxrV013zAMtW/RVvklBbIS2yypkkxJhmS0sPbNRTKQgQyhz9DCSNCKf61Q//795XK5LGUIVrAdFew2kIJ/LchABjKEJ0OkO4p+IgMZyNCQpt5PUvPYlyUDGcjgy99+sv+aUqZkHquyuqgOFB8gAxnI0NwytEywvmyoWdwOIXsdyEAGMjgvg1M6KtpfBzKQgQy+mng//bBo09+XJQMZyODLz36yf1LKkIyW0TtbSAYykKG+FVgaNjwsdlRz+Y0rGchAhvpWYm2xkKOfyEAGMvisxNpiIccxHhnIQAafFfj3NNsnpYwkl1qP7m5p2RZxSVo87gUNnzve0t0Ajs7ZKvNYlYwWsWp9AxnIQAanZHC/tdfSuOFgtaOC3QZS8K8FGchAhvBkcEpH0U9kIAMZjtfU+0lqHvuyZCADGXz5209c6BwAAAAAAAC2Y1IKAACgufN4Ip0AAADAh/3XlAIAAEBYZR0s07UbC3XO3lJlFFQo/plB2hljaHPbeH2WnqR5vVO0NjUx0jEBAECUY1IqAO6DZSo9Wqrc3FyVbjosd2tDLpt36MhQLbNTb/VNz1D79u11RfYl2rB3s9bt2WhrBidsB6CGE96PZKhGPyGSTi2s0Izl+3TuvlJVGlJcVn9pSC8pOVnxRUXqt2mTeq3N04T1hfq0Q5LuyOmgHSnxYc3khM8EGZyTgX5CbU54T5KhmhM+m2yH6MSkVCPMKo8qtxxV+boCefLLJUlT3p/i/XlM+wQlZLZVXI/WMmLD821IMlRLjEvQqAHDNGHwOGWdcrr38VnXT5Ukrd39tWavmKuFq5eqrLI8LBmcsB2AGk54P5KhGv0EJ7jymyOaufx7ueITpBtuUNykSVJ2ts/z4taskZ59VmfNf1Mr39yh23I6aGHP1iHN4oTPBBmck4F+Qm1OeE+SoZoTPptsBzApdQLuwgode+9bmUVVDT7Hk1+u0uXfq+y/+Wo5orNcIf5tIxmqnZbWVe/c/qJOaZcuj1n/dTH6dszQjGsf1z1Db9cVM27S9oO7QprBCdsBqOGE9yMZqtFPcIIrvzmi2R/uk3r0UMwHH0hdujR8HanMTOnllxX78MOKGTJEL3y4VYZMLejZJiRZnPCZIINzMtBPqM0J70kyVHPCZ5PtAIkLnTfIXVih4gU7ZRY3/OaszSyuUvGCnXIXVpAhxBlOS+uqj+58U+kpJ8swDLliXPU+zxXjkmEYSk85WR/fNV/dUruELIMTtgNQwwnvRzJUo5/gBN0KKzRz+ffVE1IrV0odO0qGIbnqfz/K5ar+eceOilm1SureXTOXf69TQ/CecMJnggzOyUA/oTYnvCfJUM0Jn022A2oEPCm1a9cu3XTTTcrOztYFF1ygF198MRy5Isqs8ujYe99KFR7J9HchSRXVy5lVwd/hhgzVEuMS9M7tLyo5oaViXf6d2BfrilVyQkstuuMlJcYlBJ3BCdsB/qGfGlpIza4bnJCBfkIgwtlP05fvU0x8QvUZUsnJUlycfwvGxUnJyYr58EO54hM0Y/m+oHI44TNBBudkoJ+aDjv2n5zwniRDNSd8NtkOqC2gSSmPx6MJEyaobdu2euedd/Too4/queee0+LFi8OVLyIqtxytPn3P3zdnDVMyi6pUueUoGUKUYdSAYTqlXbrfZVUj1hWrzid11MgzhgadwQnbAY2jnxrRzLrBCRnoJ/grnP2UdaBM5+4rVdyYq6u/sufvhFSNuDipa1fFjh6jc/eVKutgmeUsTvhMkME5GeinpsGu/ScnvCfJUM0Jn022A2oLaFIqPz9fvXv31iOPPKKuXbtq8ODBOuecc7R69epw5YuI8nUFEV2eDP8zYfC4Br9f3Bi3x60Jg8cFncEJ2wGNo5/sWZ4M/0M/wV/h7KdrNxWq0pA0aVLD15BqjNstTZyoSkO6dmOh5SxO+EyQwTkZ6Kemwa79Jye8J8lQzQmfTbYDagtoUiotLU3PPPOMWrVqJdM0tXr1av33v//VoEGDwpXPdu6DZd4r7lvlyS+XO4jfNJKhWman3so65fQGv1/cGFeMS/0791Fmp96WMzhhO8A/9JN/mkM3OCED/YRAhLOfztlbqris/tV32WvoGlKNcbmkAQMUl9VfZ+8rtbQKJ3wmyOCcDPRT02HH/pMT3pNkqOaEzybbAcezfPe9nJwc7d27VxdeeKEuueQSv5czDEMt4pIsjZkUl1jn70AVGYbMRjKUHrW2M3a82CIpKd13DDL4n6FvekZIMvRJ76mt+3dayuCE7dAYOz4X4c5QbBiWlmuI1X6SrG+HYLeB1PhrEe73Ixn8zxDufvIngxO2Q2Ps+FzYkSGUHRXqfsooqJCG9ApNuIwM9Vq3tt7t3BQ+E2RwTgb6yb4MTu6nGk7YpydDNScc27Ad/BNNx3iGaZqBfotSkrR+/Xrl5+frkUce0ZAhQ/TAAw+c8Plut1t5eXkaNWqUFi5caGXIoA0bNkwHDhxQWlqali5dWu9zcnNzNWXKlKDHuv/++zVy5EgyBJGhffv26tIl+Lsr7Nq1S/n5+ZYyOGE7hJsTMtT0Qv/+/eWy+pv+WgLtJ6lpdFS4349k8D9DuPvJnwxO2A52cEKGUHZUSPvJ49GAQYOk8eOl2bODyiVJmjBBeuEFrf78cymm7sn0TeEzQQbnZKCf7OPYfqrFCfv0ZKjmhGMbtoM9nJDB336yfKZUv379JEnl5eW68847dffddys+Pr7R5Q6XFmr43PGWxkyKS9RbY6Zr9PyJKq0M/FS5/JIC798NZSjddNhStuPNWvO6XilZRoYgMlyRfYlmXT816AxPrHhO7675wFIGJ2yHxtjxuQh3huLSQkvjNsRqP0nWOyrYbSA1/lqE+/1IBv8zhLuf/MnghO3QGDs+F3ZkCGVHhbqfdsYYii8qCk24o0dVEWNo+Ou3+PyoKXwmyOCcDPSTfRmc3E81nLBPT4ZqTji2YTv4J5qO8QKalMrPz1deXp4uuugi72Pdu3dXZWWliouL1a5du0bXYZqmSiqDO12utLLM0jpqTgo7UQZ369CcAluVrHrHIIP/GTbs3RySDBu+29ykt4O/wvm5sCtDMELRTzVZgtkOVrdBzdgnyhDu9yMZ/M8Q7n7yJ4MTtoO/wvm5sDODVeHsp81t49Vv06ag8nlt2qRN7eIt/ffKCZ8JMjgnA/1kfwar7Nh/csI+PRmqOeHYhu0QmGg4xgvoQud79uzRHXfcof3793sf27Bhg9q1a+d3YTmdKzVRMe0TglpHTPsEuVKtfzecDNXW7dmotbu/ltvjtrS82+NW3rdfaf131nfWnbAd4B/6yT/NoRuckIF+QiDC2U+fpSepcm2etGZN9V30rHC7pdWrVblurVZ2sHbdCSd8JsjgnAz0U9Nhx/6TE96TZKjmhM8m2wHHC2hSql+/furTp4/uv/9+bd26VStWrNCTTz6pX/7yl+HKFxEJmW0jujwZ/mf2irmKMQJ6m3q5YlyavWJu0BmcsB3QOPrJnuXJ8D/0E/wVzn6a1ztFcaakZ5/1uQ6U31wuafp0xZnV67PKCZ8JMjgnA/3UNNi1/+SE9yQZqjnhs8l2QG0BvRNcLpdmzZqlpKQkjRkzRr///e913XXX6frrrw9XvoiI69FaRnKsFOhZfYZkJMcqrkdrMoQow8LVS7X78F5VuasCWq7KXaVvD32n3C/rvw5BIJywHdA4+qkRzawbnJCBfoK/wtlPa1MT9WmHJFXNf1PatUuqrAxsBZWV0s6dqnprvj7tkKS1QfzW1wmfCTI4JwP91DTYtf/khPckGao54bPJdkBtAU9PnnzyyZoxY4ZWr16tf//73/rlL38pI8S3c480IzZGLUd0luJj/H+TGpLiq5czYi3+ppIMPsoqy/WzmTerqPyY36VV5a5SUfkxXTHjJpVVlgedwQnbAf6hnxpaSM2uG5yQgX5CIMLZT3fkdJC7olyeiy+Wior8n5iqrJSKiuQZMkTuinLdkdMhqBxO+EyQwTkZ6Kemw479Jye8J8lQzQmfTbYDamNLNsCVEq9WV3aV0cq/a8EbrWLV6squcqX4d4cKMvhv24Gd+smTY7S3sPq77g19/7jm8b2F+/WTJ8do+8FdIcvghO0A1HDC+5EM1egnOMGOlHjdltNB2rJFnrPOkr77rvoHDV1jqubx776rfv7Wrbotp4N2hOA94YTPBBmck4F+Qm1OeE+SoZoTPptsB9QI6O570caVEq/ka7qpcstRla8rkCffd0Y2pn2CEjLbVp/+F4bZUjJU235wlwY99lONPGOoJgwep/6d+/g8Z/2eTZq9Yq5yv1wWktnz4zlhOwA1nPB+JEM1+glOsLBnaxkyNXP5NrlO763Y0WOkiROlAQN8n5yXJ02frqq35stdUa7bhqRrYc/QfQ3BCZ8JMjgnA/2E2pzwniRDNSd8NtkOkJiUapQRG6P43imK750i98EyxRZJt2Vfo1lrXldVsmy54j4ZqpVVluv1VYv0+qpFyuzUW33Se+rewbfqiRXP6au932jdno1hz+CE7QDUcML7kQzV6Cc4wYKebbQ6LUkzlu/Tua++qsrXXlVcVn8pI0Nq3Vo6elTavFmVa/MUZ0or05M08cJTQ3KG1PGc8Jkgg3My0E+ozQnvSTJUc8Jnk+0AJqUC4EpNVFJ6kkaOHKlXSpappLKUDBHKsG7PRm3dv1M397lK7675IGq3A1DDCe9HMlSjnxBJO1Li9dORXZR1sEzXbizU2Xs2qte6tYr3mKqIMbSpXbxW9k3RvN4pQV3UPBBO+EyQwTkZ6CfU5oT3JBmqOeGzyXaITkxKAQAANDNrUxO1NvVHkqQWcUlafM1fNfz1W9i5BgAAjsIXIgEAAJq7GHb5AACA87CHAgAAAAAAANsZpmmadgzkdruVl5enwRcMVolp7ar5hmGofYu2yi8pkJXYZkmVZEoyJKOFtW8ukoEMZAh9hhZGglb8a4X69+8vl8tlKUOwgu2oYLeBFPxrQQYykCE8GSLdUfQTGchAhoY09X6Smse+LBnIQAZf/vaT/deUMiXzWJXVRXWg+AAZyECG5pahZYL1ZUPN4nYI2etABjKQwXkZnNJR0f46kIEMZPDVxPvph0Wb/r4sGchABl9+9pP9k1KGZLSM3tlCMpCBDPWtwNKw4WGxo5rLb1zJQAYy1LcSa4uFHP1EBjKQwWcl1hYLOY7xyEAGMviswL+n2T4pZSS51Hp0d0vLtohL0uJxL2j43PGW7h5zdM5WmceqZLSIVesbyEAGMjglg/utvZbGDQerHRXsNpCCfy3IQAYyhCeDUzqKfiIDGchwvKbeT1Lz2JclAxnI4MvffuJC5wAAAAAAALAdk1IAAAAAAACwHZNSAAAAAAAAsB2TUghYZqfeuiL7ErVv315XZF+izE69ozIDAOdxQje4D5apdNNh5ebmqnTTYbkPltmeAYDz0E8AnMwJ/eCEDLCf/XffQ5OUGJegUQOGacLgcco65XTv47OunypJWrv7a81eMVcLVy9VWWV5s80AwHmc0A1mlUeVW46qfF2BPPnVY0x5f4r35zHtE5SQ2VZxPVrLiOX3QUC0oJ8AOJkT+sEJGRBZTEqhUaelddU7t7+oU9qly2N66n1O344ZmnHt47pn6O26YsZN2n5wV7PLAMB5nNAN7sIKHXvvW5lFVQ0+x5NfrtLl36vsv/lqOaKzXCnxIc0AwHnoJwBO5oR+cEIGRB5TjTih09K66qM731R6yskyDEOuGFe9z3PFuGQYhtJTTtbHd81Xt9QuzSoDAOdxQje4CytUvGCnzOKGd6ZqM4urVLxgp9yFFSHLAMB56CcATuaEfnBCBjiD5UmpCRMm6N577w1lFjhMYlyC3rn9RSUntFSsy7+T6mJdsUpOaKlFd7ykxLiEZpEBTRMd1bw5oRvMKo+OvfetVOGRTH8XklRRvZxZVf+ZE2j+6KfmjX5CU0Y/NX9O6AcnZIBzWJqUWrJkiVasWBHqLHCYUQOG6ZR26X7vUNWIdcWq80kdNfKMoc0iA5oeOqr5c0I3VG45Wn26ub87UzVMySyqUuWWo0FnQNNDPzV/9BOaKvopOjihH5yQAc4R8KRUYWGhpk2bpn79+oUjDxxkwuBxDV4DoTFuj1sTBo9rFhnQtNBR0cEJ3VC+riCiy6PpoZ+iA/2Epoh+ih5O6AcnZIBzBDwp9cc//lGXX365unfvHo48cIjMTr2VdcrpDV4DoTGuGJf6d+4T1O2OnZABTQ8d1fw5oRvcB8u8d4ixypNfzq2Oowz91PzRT2iq6Kfo4IR+cEIGOEtA5xV/9tln+uKLL7R48WI98sgjlgY0DEMt4pIsLZsUl1jn70AVGYZMMviVoW96hqV1H69Pek9t3b+zyWZoTLS8H8KdodgwLC13vEh2VLDbQAr+tYiWDOHuBn8ylB4tDUmG2CIpKb3+7dwUXotoyRCKjqKfoiMD/eSfaHk/2JGhqfeT1Dz2ZZtKhnD3Q1PJ0JhoeT+EO4O//WSYpunXNznLy8s1fPhwPfjggzr//PO9F8B74okn/BrI7XYrLy9Po0aN0sKFC/1aJtSGDRumAwcOKC0tTUuXLiXDCTK0b99eXboEfweYXbt2KT8/v8lmCDcyVKvphf79+8vlsvbbZToqejKEuxv8yZCbm6spU6YEneH+++/XyJEjLWWwAxmqBdtR9FP0ZKCf7EOGavSTM16HppIh3P3QVDKEGxmq+dtPfp8pNWPGDPXt21fnn39+UMEOlxZq+NzxlpZNikvUW2Oma/T8iSqtDPx0vfySAu/fZDhxhiuyL9Gs66daylfbEyue07trPmiyGRoTLe+HcGcoLi20NG5tke6oYLeBFPxrES0Zwt0N/mQo3XQ46PEladaa1/VKyTJLGRoTLe8HOzIE21H0U/RkoJ/8Ey3vBzsyNPV+kprHvmxTyRDufmgqGRoTLe+HcGfwt5/8npRasmSJ8vPzlZ2dLUmqqKiQJP3jH//QmjVr/A5mmqZKKoM7Za+0sszSOmpOCiND4xk27N0cVDbver7bXO8YTSWDv5r7+8GuDMFwSkdZ3QY1Y5Oh8Qzh7gZ/Mrhbh+Yrp1XJspzBX839/WBnBqvop+jJQD8Fprm/H+zMYJVT+klqHvuyTs8Q7n5oKhn81dzfD3ZlaIzfk1J/+9vfVFVV5f3/Tz31lCTpzjvvDDAamoJ1ezZq7e6v1bdjhqWLdbo9bq3fs0nrv9vUpDOg6aCjoocTusGVmqiY9glBXagzpn2CXKnWryOCpoN+ih70E5oa+im6OKEfnJABzuL33fc6duyoLl26eP+0bNlSLVu2DMn35uFMs1fMVYwR8A0aJVXfPWb2irnNIgOaBjoqujihGxIy20Z0eTQd9FN0oZ/QlNBP0ccJ/eCEDHAOa//FRFRYuHqpdh/eqyp3VeNPrqXKXaVvD32n3C/rvw5BU8sAwHmc0A1xPVrLSI6VAj0L3ZCM5FjF9WgddAYAzkM/AXAyJ/SDEzLAOSxPSj3xxBN+35UBTVNZZbl+NvNmFZUf83vHqspdpaLyY7pixk0qq7R+SqaTMqBpoqOaNyd0gxEbo5YjOkvxMf7vVBmS4quXM2L5vVC0op+aN/oJTRn91Pw5oR+ckAHOwauJE9p2YKd+8uQY7S3cL6n6Wgf1qXl8b+F+/eTJMdp+cFezygDAeZzQDa6UeLW6squMVv5dotFoFatWV3aVKyU+ZBkAOA/9BMDJnNAPTsgAZ/D7QueIXtsP7tKgx36qkWcM1YTB49S/cx+f56zfs0mzV8xV7pfLwnJ2khMyAHAeJ3SDKyVeydd0U+WWoypfV1DvhTtj2icoIbNt9enq/HYPiAr0EwAnc0I/OCEDIo9JKfilrLJcr69apNdXLVJmp97qk95T9w6+VU+seE5f7f1G6/ZsjIoMAJzHCd1gxMYovneK4nunyH2wTLFF0m3Z12jWmtdVlSzuEANEKfoJgJM5oR+ckAGRxVQjArZuz0a9u+YD5efn6901H0RkMsgJGQA4jxO6wZWaqKSMtho5cqSSMtqyMwVAEv0EwNmc0A9OyAD7MSkFAAAAAAAA2zEpBQAAAAAAANsxKQUAAAAAAADbMSkFAAAAAAAA2xmmaZp2DOR2u5WXl6fBFwxWiWntlreGYah9i7bKLymQldhmSZVkSjIko4W1Gw+SgQxkCH2GFkaCVvxrhfr37y+Xy2UpQ7CC7ahgt4EU/GtBBjKQITwZIt1R9BMZyECGhjT1fpKax74sGchABl/+9pO1dMEwJfNYldVFdaD4ABnIQIbmlqFlgvVlQ83idgjZ60AGMpDBeRmc0lHR/jqQgQxk8NXE++mHRZv+viwZyEAGX372k/2TUoZktIze2UIykIEM9a3A0rDhYbGjmstvXMlABjLUtxJri4Uc/UQGMpDBZyXWFgs5jvHIQAYy+KzAv6fZPillJLnUenR3S8u2iEvS4nEvaPjc8SqpLA14+aNztso8ViWjRaxa30AGMpDBKRncb+21NG44WO2oYLeBFPxrQQYykCE8GZzSUfQTGchAhuM19X6Smse+LBnIQAZf/vYTFzoHAAAAAACA7ZiUAgCgufJ4Ip0AAAAAaJD915QCAABhkXWwTNduLNQ5e0uVUVCh+GcGaWeMoc1t4/VZepLm9U7R2tTESMcEAAAAJDEpBQvcB8tUerRUubm5Kt10WO7Whlw2H+RkduqtvukZat++va7IvkQb9m7Wuj0bbc0AwHmc0E+RyHBqYYVmLN+nc/eVqtKQNpjSQklFkpI9pnodKteNh8s1YX2hPu2QpDtyOmhHSnxYMwGoK1r7yYkZAPhywmeTDNGJSSn4xazyqHLLUZWvK5Anv1ySNOX9Kd6fx7RPUEJmW8X1aC0jNjzfCk2MS9CoAcM0YfA4ZZ1yuvfxWddPlSSt3f21Zq+Yq4Wrl6qssjwsGQA4jxP6KZIZrvzmiGYu/14VblOvSHrWlPLqeV5/U5ok6cp9pVr55g7dltNBC3u2DmkWAHVFez85KQMAX074bJIBTEqhUe7CCh1771uZRVUNPseTX67S5d+r7L/5ajmis1wh/g38aWld9c7tL+qUdunymPVfI6VvxwzNuPZx3TP0dl0x4yZtP7grpBkAOI8T+imSGa785ohmf7hP30i6VNIuNXyxyHWSfiHpUUn/cJt64cO9MmRqQc82IckCoK5o7ycnZQDgywmfTTJAsnCh8w8//FAZGRl1/kyaNCkc2eAA7sIKFS/YKbO44Q9pbWZxlYoX7JS7sCJkGU5L66qP7nxT6SknyzAMuWJc9T7PFeOSYRhKTzlZH981X91Su4QsA5oG+im6OKGfIpmhW2GFZi7/Xt9I+rGkPT883tClzWse3yPpHElbJM1c/r1ODeH2QMPop+gS7f3kpAxoHP0UfZzw2SQDagR8ptTWrVt14YUXavLkyd7HEhISQhoKzmBWeXTsvW+lCo9k+ruQpIrq5ZKv6Rb06Y2JcQl65/YXlZzQUrEu/96usa5YJSe01KI7XtKgx37KV/miCP0UPZzQT5HOMH35PpW7TV0q6agkt5/LuX94/iWS1rtNzVi+Tz8dySR+uNFP0SPS3UAGBIp+ii5O+GySAbUFvBW3bdumnj17KjU11fundWuuSdEcVW45Wn0ao78f0hqmZBZVqXLL0aAzjBowTKe0S/d7QqpGrCtWnU/qqJFnDA06A5oO+il6OKGfIpkh60CZzt1XqoWq/sqevxNSNdw/LLdQ0rn7SpV1sMxyFviHfooe0d5PTsoA/9BP0cUJn00yoDZLk1Jdu3YNQxQ4Tfm6goguL0kTBo9r8BpSjXF73JoweFzQGdB00E/Rwwn9FMkM124qVKUhPSsL/yH/QYyql680pGs3FlrOAv/QT9Ej2vvJSRngH/opujjhs0kG1BbQvqxpmtqxY4f+/e9/65JLLtFFF12kp556ShUVfKeyuXEfLPPeecAqT3653EH89j2zU29lnXJ6g9eQaowrxqX+nfsos1NvyxnQdNBP0cMJ/RTpDOfsLdWGH+6yZ23avnq5NZI2mNLZ+0otrgX+oJ+iR6S7gQwIFP0UXZzw2SQDjhfQd6L27t2r0tJSxcfH65lnntGePXv02GOPqaysTA888IBf6zAMQy3ikiyFTYpLrPN3oIoMQyYZ/MpQejQ0ByixRVJSuu8Y/mTom54Rkgx90ntq6/6dljI0JlreD+HOUGwYlparLRT9JFnfDsFuAyn41yJaMoS7n5pChoyCCi0MSQJps6SRhyvq/Xc2hfeDHRmC7Sj6KXoyRLobyOC/5vKebOr9JDWPfdmmksEJx3hk8E9zeE/620+GaZoBfYuysLBQbdq0kfHDAP/4xz901113ac2aNXK5Gj6jxe12Ky8vT6NGjdLChaHalQ7MsGHDdODAAaWlpWnp0qVkOEGG3NxcTZkyJeix7r//fo0cOdJShvbt26tLl+Avvrtr1y7l5+dbyhBuZKhW0wv9+/c/YY80xmo/SXRUU8oQ7n5yfAaPRwMGDdJsSbcEnUD6q6QJklZ//rkUU/cE6qbwfrBDKDqKfoqODFHfTw7KYAcnZKCfnPE6NJUMTjjGI4M9nJDB334K+O57KSkpdf7/aaedpvLych05ckTt2rVrdPnDpYUaPnd8oMNKqp6he2vMdI2eP1GllYGfKpdfUuD9mwwnzlC66bClbMebteZ1vVKyzFKGK7Iv0azrpwad4YkVz+ndNR9YytCYaHk/hDtDcWmhpXGPF2w/SdY7KthtIAX/WkRLhnD3U1PIsDPGULIn0Ctz1q+1pIoYQ8Nf953iagrvBzsyhKKj6KfoyBDpbiCD/5rLe7Kp95PUPPZlm0oGJxzjkcE/zeE96W8/BTQp9cknn+jOO+/Uv/71LyUlVZ8CtnHjRqWkpPhdWKZpqqQyuNPlSivLLK2j5qQwMjSewd06+K9TSVJVsuodw58MG/ZuDkmGDd9ttpzBX839/WBXhmCEop9qsgSzHaxug5qxydB4hnD3U1PIsLltvHodCu5aCDV6SdrULj6sPdlc3pNW0U/RkyHS3UCGwDWX96RVTuknqXnsyzo9gxOO8cgQmObwnmxMQBc6z87OVkJCgh544AFt375dK1as0LRp03TzzTcHHBDO5kpNVEz7hKDWEdM+Qa5U69+RX7dno9bu/lpuT6A3O6/m9riV9+1XWv/dJssZ0HTQT9HDCf0U6QyfpSepryH1V3B338uW1MeQVnawdq0B+Id+ih6R7gYyIFD0U3RxwmeTDDheQPuyrVq10ksvvaTDhw9r1KhR+v3vf68xY8ZQWs1UQmbbiC4vSbNXzFWMYe2QyxXj0uwVc4POgKaBfoouTuinSGaY1ztFcaY0ScHdfW+SpDizen0IH/opukR7PzkpAxpHP0UfJ3w2yYDaAr6mVI8ePfTKK6+EIwscJq5Ha5X9N19mcZUUyJnBhmS0ilVcj9ZBZ1i4eqnuGXq70lNOVqzL/7drlbtKewv3K/fL+q9DgOaJfooeTuinSGZYm5qoTzsk6cp9pXpU0h5JgZxT6pLUSdIoSZ92SNJaftMXdvRT9Ij2fnJSBviHfoouTvhskgG1WT3rH1HAiI1RyxGdpfgYyd+v3RqS4quXM2KDf3uVVZbrZzNvVlH5MVW5q/xapspdpaLyY7pixk0qqwzNNVcAOIsT+inSGe7I6aB4l6H3VX2xcn/vueT64fn/kBTvMnRHToegcgCoK9LdQAYAJ+KEzyYZUBtbEifkSolXqyu7ymjl31lKRqtYtbqyq1wp8SHLsO3ATv3kyTHaW7hfkhq8xlTN43sL9+snT47R9oO7QpYBgPM4oZ8imWFHSrxuy+mgnpI+U/WZT1LD/2GvebzTD8/vIem2nA7aEcLtAaBatPeTkzIA8OWEzyYZUCPgr+8h+rhS4pV8TTdVbjmq8nUF8uT7nn0U0z5BCZltFdejdVhmjbcf3KVBj/1UI88YqgmDx6l/5z4+z1m/Z5Nmr5ir3C+XcYYUECWc0E+RzLCwZ2sZMjVz+fda7za1UNKzktbU89wsVV9DapSqz5Aan9NBC3ty6jkQLtHeT07KAMCXEz6bZIDEpBT8ZMTGKL53iuJ7p8h9sEyxRdJt2ddo1prXVZUsW+48UFZZrtdXLdLrqxYps1Nv9UnvqXsH36onVjynr/Z+o3V7NoY9AwD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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_board, _action = simulate_game(1, (GreedyPolicy(0), GreedyPolicy(0)))\n",
"\n",
"plot_othello_boards(\n",
" *drop_duplicate_boards(_board.reshape(-1, 8, 8), _action.reshape(-1, 2))\n",
")"
]
},
{
"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": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "((70, 10000, 8, 8), (70, 10000, 2))"
},
"execution_count": 29,
"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": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "(70, 10000, 8, 8)"
},
"execution_count": 30,
"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": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "interactive(children=(IntSlider(value=34, description='turn', max=69), Output()), _dom_classes=('widget-intera…",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "805278b968eb41e6afe6f5e997ed44a9"
}
},
"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": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": " Total mean action-space\nwhite 5.687159e+18\nblack 3.753117e+20",
"text/html": "
![\"Startaufstellung.png\"](\"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",
"![\"ComputerPossitionScore\"](\"computer-score.png\"/)
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initial design decisions\n",
"\n",
"At the beginning of this project I made some design decisions.\n",
"The first onw was that I do not want to use a gym library because it limits the data formats accessible.\n",
"I choose to implement the hole game as entry in a stack in numpy arrays to be able to accommodate interfacing with a neural network easier and to use scipy pattern recognition tools to implement some game mechanics for a fast simulation cycle.\n",
"I chose to ignore player colors as far as I could instead a player perspective was used. Which allowed to change the perspective with a flipping of the sign. (multiplying with -1).\n",
"The array format should also allow for data multiplication or the breaking of strikt sequences by flipping the game along one the for axis, (horizontal, vertical, transpose along both diagonals).\n",
"\n",
"I wanted to implement different agents as classes that act on those game stacks.\n",
"\n",
"Since computation time is critical all computational have results are saved.\n",
"The analysis of those is then repeated in real time. If a recalculation of such a section is required the save file can be deleted and the code should be executed again."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%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, display\n",
"from pathlib import Path\n",
"import glob\n",
"import copy\n",
"from functools import lru_cache, wraps\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",
"from torch.nn import functional\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": {},
"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] = False\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": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[-1, -1],\n [-1, 0],\n [-1, 1],\n [ 0, -1],\n [ 0, 1],\n [ 1, -1],\n [ 1, 0],\n [ 1, 1]])"
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DIRECTIONS: Final[np.ndarray] = np.array(\n",
" [[i, j] for i in range(-1, 2) for j in range(-1, 2) if j != 0 or i != 0],\n",
" dtype=int,\n",
")\n",
"DIRECTIONS.setflags(write=False)\n",
"DIRECTIONS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another constant needed is the initial start square at the center of the board."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[-1, 1],\n [ 1, -1]])"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"START_SQUARE: Final[np.ndarray] = np.array(\n",
" [[ENEMY, PLAYER], [PLAYER, ENEMY]], dtype=int\n",
")\n",
"START_SQUARE.setflags(write=False)\n",
"START_SQUARE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating new boards\n",
"\n",
"The first function implemented and tested is a function to generate the starting environment as a stack of games.\n",
"As described above I simply placed a 2 by 2 square in the center of an empty stack of boards."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, -1, 1, 0, 0, 0],\n [ 0, 0, 0, 1, -1, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0]])"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def get_new_games(number_of_games: int) -> np.ndarray:\n",
" \"\"\"Generates a stack of initialised game boards.\n",
"\n",
" Args:\n",
" number_of_games: The size of the board stack.\n",
"\n",
" Returns: The generates stack of games as a stack n x 8 x 8.\n",
"\n",
" \"\"\"\n",
" empty = np.zeros([number_of_games, BOARD_SIZE, BOARD_SIZE], dtype=int)\n",
" empty[:, 3:5, 3:5] = START_SQUARE\n",
" return empty\n",
"\n",
"\n",
"get_new_games(1)[0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"test_number_of_games = 3\n",
"assert get_new_games(test_number_of_games).shape == (\n",
" test_number_of_games,\n",
" BOARD_SIZE,\n",
" BOARD_SIZE,\n",
")\n",
"np.testing.assert_equal(\n",
" get_new_games(test_number_of_games).sum(axis=1),\n",
" np.zeros(\n",
" [\n",
" test_number_of_games,\n",
" 8,\n",
" ]\n",
" ),\n",
")\n",
"np.testing.assert_equal(\n",
" get_new_games(test_number_of_games).sum(axis=2),\n",
" np.zeros(\n",
" [\n",
" test_number_of_games,\n",
" 8,\n",
" ]\n",
" ),\n",
")\n",
"assert np.all(get_new_games(test_number_of_games)[:, 3:4, 3:4] != 0)\n",
"del test_number_of_games"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualisation tools\n",
"\n",
"In this section a visualisation help was implemented for debugging of the game and a proper display of the results.\n",
"For this visualisation ChatGPT was used as a prompted code generator that was later reviewed and refactored by hand to integrate seamlessly into the project as a whole.\n",
"White stones represent the player, black stones the enemy. A single plot can be used as a subplot when the `ax` argument is used."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "