1758 lines
525 KiB
Plaintext
1758 lines
525 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Deep Otello AI\n",
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"\n",
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"The game reversi is a very good game to apply deep learning methods to.\n",
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"\n",
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"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",
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"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",
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"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",
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"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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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"## Content\n",
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"\n",
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"* [The game rules](#the-game-rules) A short overview over the rules of the game.\n",
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"* [Some common Otello strategies](#some-common-otello-strategies) introduces some easy approaches to a classic Otello AI and defines some behavioral expectations.\n",
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"* [Initial design decisions](#initial-design-decisions) an explanation about some initial design decision and assumptions\n",
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"* [Imports and dependencies](#imports-and-dependencies) explains what libraries where used"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"## The game rules\n",
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"\n",
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"Othello is played on a board with 8 x 8 fields for two player.\n",
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"The board geometry is equal to a chess game.\n",
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"The game is played with game stones that are black on one siede and white on the other.\n",
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"\n",
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"The player take turns.\n",
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"A player places a stone with his or her color up on the game board.\n",
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"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",
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"Those surrounded stones can either be horizontally, vertically and/or diagonally be placed.\n",
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"All stones thus surrounded will be flipped to be of the players color.\n",
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"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",
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"The game ends if both players can't act. The player with the most stones wins.\n",
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"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",
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"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",
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"\n",
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"\n",
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"<img alt=\"Startaufstellung.png\" src=\"Startaufstellung.png\"/>\n",
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"\n",
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"## Some common Othello strategies\n",
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"\n",
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"As can be easily understood the placement of stones and on the bord is always a careful balance of attack and defence.\n",
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"If the player occupies huge homogenous stretches on the board it can be attacked easier.\n",
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"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",
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"There are some text on otello computer strategies which implement greedy algorithms for reversi based on a modified score to each field.\n",
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"Those different values are score modifiers for a traditional greedy algorithm.\n",
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"If a players stone has captured such a filed the score reached is multiplied by the modifier.\n",
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"The total score is the score reached by the player subtracted with the score of the enemy.\n",
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"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",
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"\n",
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"<img alt=\"ComputerPossitionScore\" src=\"computer-score.png\"/>\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Initial design decisions\n",
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"\n",
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"At the beginning of this project I made some design decisions.\n",
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"The first onw was that I do not want to use a gym library because it limits the data formats accessible.\n",
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"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",
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"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",
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"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",
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"\n",
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"I wanted to implement different agents as classes that act on those game stacks.\n",
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"\n",
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"Since computation time is critical all computational have results are saved.\n",
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"The analysis of those is then repeated in real time. If a recalculation of such a section is required the save file can be deleted and the code should be executed again."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"%load_ext blackcellmagic"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Imports and dependencies\n",
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"\n",
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"The following direct dependencies where used for this project:\n",
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"```toml\n",
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"jupyter = \"^1.0.0\"\n",
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"matplotlib = \"^3.6.3\"\n",
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"numpy = \"^1.24.1\"\n",
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"pytest = \"^7.2.1\"\n",
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"python = \"3.10.*\"\n",
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"scipy = \"^1.10.0\"\n",
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"tqdm = \"^4.64.1\"\n",
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"jupyterlab = \"^3.6.1\"\n",
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"torchvision = \"^0.14.1\"\n",
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"torchaudio = \"^0.13.1\"\n",
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"```\n",
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"* `Jupyter` and `jupyterlab` on pycharm was used as a IDE / Ipython was used to implement this code.\n",
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"* `matplotlib` was used for visualisation and statistics.\n",
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"* `numpy` was used for array support and mathematical functions\n",
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"* `tqdm` was used for progress bars\n",
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"* `scipy` contains fast pattern recognition tools for images. It was used to make an initial estimation about where possible turns should be.\n",
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"* `torch` supplied the ANN functionalities."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from multiprocessing import Pool\n",
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"import itertools\n",
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"import numpy as np\n",
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"import abc\n",
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"from typing import Final\n",
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"from scipy.ndimage import binary_dilation\n",
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"import matplotlib.pyplot as plt\n",
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"from abc import ABC"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Constants\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"BOARD_SIZE: Final[int] = 8 # defines the board side length as 8\n",
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"PLAYER: Final[int] = 1 # defines the number symbolising the player as 1\n",
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"ENEMY: Final[int] = -1 # defines the number symbolising the enemy as -1\n",
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"EXAMPLE_STACK_SIZE: Final[int] = 1000 # defines the game stack size for examples\n",
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"IMPOSSIBLE: Final[np.ndarray] = np.array([-1, -1], dtype=int)\n",
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"IMPOSSIBLE.setflags(write=False)\n",
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"SIMULATE_TURNS: Final[int] = 70\n",
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"VERIFY_POLICY: Final[bool] = True"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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",
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"![8-directions.png](8-directions.png \"Offset in 8 directions\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[-1, -1],\n",
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" [-1, 0],\n",
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" [-1, 1],\n",
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" [ 0, -1],\n",
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" [ 0, 1],\n",
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" [ 1, -1],\n",
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" [ 1, 0],\n",
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" [ 1, 1]])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"DIRECTIONS: Final[np.ndarray] = np.array(\n",
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" [[i, j] for i in range(-1, 2) for j in range(-1, 2) if j != 0 or i != 0],\n",
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" dtype=int,\n",
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")\n",
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"DIRECTIONS.setflags(write=False)\n",
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"DIRECTIONS"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Another constant needed is the initial start square at the center of the board."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[-1, 1],\n",
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" [ 1, -1]])"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"START_SQUARE: Final[np.ndarray] = np.array(\n",
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" [[ENEMY, PLAYER], [PLAYER, ENEMY]], dtype=int\n",
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")\n",
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"START_SQUARE.setflags(write=False)\n",
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"START_SQUARE"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Creating new boards\n",
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"\n",
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"The first function implemented and tested is a function to generate the starting environment as a stack of games.\n",
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"As described above I simply placed a 2 by 2 square in the center of an empty stack of boards."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, -1, 1, 0, 0, 0],\n",
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" [ 0, 0, 0, 1, -1, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0]])"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def get_new_games(number_of_games: int) -> np.ndarray:\n",
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" \"\"\"Generates a stack of initialised game boards.\n",
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"\n",
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" Args:\n",
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" number_of_games: The size of the board stack.\n",
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"\n",
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" Returns: The generates stack of games as a stack n x 8 x 8.\n",
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"\n",
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" \"\"\"\n",
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" empty = np.zeros([number_of_games, BOARD_SIZE, BOARD_SIZE], dtype=int)\n",
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" empty[:, 3:5, 3:5] = START_SQUARE\n",
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" return empty\n",
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"\n",
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"\n",
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"get_new_games(1)[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"test_number_of_games = 3\n",
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"assert get_new_games(test_number_of_games).shape == (\n",
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" test_number_of_games,\n",
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" BOARD_SIZE,\n",
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" BOARD_SIZE,\n",
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")\n",
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"np.testing.assert_equal(\n",
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" get_new_games(test_number_of_games).sum(axis=1),\n",
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" np.zeros(\n",
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" [\n",
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" test_number_of_games,\n",
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" 8,\n",
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" ]\n",
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" ),\n",
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")\n",
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"np.testing.assert_equal(\n",
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" get_new_games(test_number_of_games).sum(axis=2),\n",
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" np.zeros(\n",
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" [\n",
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" test_number_of_games,\n",
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" 8,\n",
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" ]\n",
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" ),\n",
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")\n",
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"assert np.all(get_new_games(test_number_of_games)[:, 3:4, 3:4] != 0)\n",
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"del test_number_of_games"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Visualisation tools\n",
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"\n",
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"In this section a visualisation help was implemented for debugging of the game and a proper display of the results.\n",
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"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",
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"White stones represent the player, black stones the enemy. A single plot can be used as a subplot when the `ax` argument is used."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 300x300 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"def plot_othello_board(board, ax=None) -> None:\n",
|
|
" \"\"\"Plots a single otello board.\n",
|
|
"\n",
|
|
" If a matplot axis object is given the board will be plotted into that axis. If not an axis object will be generated.\n",
|
|
" The image generated will be shown directly.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" board: The bord that should be plotted. Only a single games is allowed. A numpy array of the form 8x8 is expected.\n",
|
|
" ax: If needed a matplotlib axis object can be defined that is used to place the board as a sublot into a bigger context.\n",
|
|
" \"\"\"\n",
|
|
" assert board.shape == (8, 8)\n",
|
|
" plot_all = False\n",
|
|
" if ax is None:\n",
|
|
" fig_size = 3\n",
|
|
" plot_all = True\n",
|
|
" fig, ax = plt.subplots(figsize=(fig_size, fig_size))\n",
|
|
"\n",
|
|
" ax.set_facecolor(\"#66FF00\")\n",
|
|
" for x_pos, y_pos in itertools.product(range(BOARD_SIZE), range(BOARD_SIZE)):\n",
|
|
" if board[x_pos, y_pos] == -1:\n",
|
|
" color = \"white\"\n",
|
|
" elif board[x_pos, y_pos] == 1:\n",
|
|
" color = \"black\"\n",
|
|
" else:\n",
|
|
" continue\n",
|
|
" ax.scatter(y_pos, x_pos, s=300 if plot_all else 150, c=color)\n",
|
|
" for x_pos in range(-1, 8):\n",
|
|
" ax.axhline(x_pos + 0.5, color=\"black\", lw=2)\n",
|
|
" ax.axvline(x_pos + 0.5, color=\"black\", lw=2)\n",
|
|
" ax.set_xlim(-0.5, 7.5)\n",
|
|
" ax.set_ylim(7.5, -0.5)\n",
|
|
" ax.set_xticks(np.arange(8))\n",
|
|
" ax.set_xticklabels(list(\"ABCDEFGH\"))\n",
|
|
" ax.set_yticks(np.arange(8))\n",
|
|
" ax.set_yticklabels(list(\"12345678\"))\n",
|
|
" if plot_all:\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"plot_othello_board(get_new_games(1)[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def plot_othello_boards(boards: np.ndarray) -> None:\n",
|
|
" \"\"\"Plots multiple boards into subplots.\n",
|
|
"\n",
|
|
" The plots are shown directly.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" boards: Plots the boards given into subplots. The maximum number of boards accepted is 70.\n",
|
|
" \"\"\"\n",
|
|
" assert len(boards.shape) == 3\n",
|
|
" assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n",
|
|
" assert boards.shape[0] < 70\n",
|
|
"\n",
|
|
" plots_per_row = 4\n",
|
|
" rows = int(np.ceil(boards.shape[0] / plots_per_row))\n",
|
|
" fig, axs = plt.subplots(rows, plots_per_row, figsize=(12, 3 * rows))\n",
|
|
" for game_index, ax in enumerate(axs.flatten()):\n",
|
|
" if game_index >= boards.shape[0]:\n",
|
|
" fig.delaxes(ax)\n",
|
|
" else:\n",
|
|
" plot_othello_board(boards[game_index], ax)\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def drop_duplicate_boards(boards: np.ndarray) -> np.ndarray:\n",
|
|
" \"\"\"Drop boards that follow each other and are duplicates will be dropped.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" boards: A set of boards to be reduced.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" A sequence of boards where boards that where equal are dropped.\n",
|
|
" \"\"\"\n",
|
|
" return boards[~np.all(boards == np.roll(boards, axis=0, shift=1), axis=(1, 2))]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Find possible actions to take\n",
|
|
"\n",
|
|
"The frist step in the implementation of an AI like this is to get an overview over the possible actions that can be taken in a situation.\n",
|
|
"Here was the design choice taken to first find fields that are empty and have at least one neighbouring enemy stone.\n",
|
|
"This was implemented with element wise check for a stone and a binary dilation marking all fields neighboring an enemy stone.\n",
|
|
"For that the `SURROUNDING` mask was used. Both aries are then element wise combined using and.\n",
|
|
"The resulting array contains all filed where a turn could potentially be made. Those are then check in detail.\n",
|
|
"The previous element wise operations on the numpy array increase the spead for this operation dramatically.\n",
|
|
"\n",
|
|
"The check for a possible turn is done in detail by following each direction step by step as long as there are enemy stones in that direction.\n",
|
|
"If the board end is reached or en empty filed before reaching a field occupied by the player that direction does not surround enemy stones.\n",
|
|
"If one direction surrounds enemy stone a turn is possible.\n",
|
|
"This detailed step is implemented as a recursion and need to go at leas one step to return True."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([[[1, 1, 1],\n",
|
|
" [1, 0, 1],\n",
|
|
" [1, 1, 1]]])"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"SURROUNDING: Final = np.array(\n",
|
|
" [[[1, 1, 1], [1, 0, 1], [1, 1, 1]]]\n",
|
|
") # defines the binary dilation mask to check if a field is next to an enemy stones\n",
|
|
"SURROUNDING"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"8.02 ms ± 181 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
|
|
"800 ms ± 5.98 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([[[False, False, False, False, False, False, False, False],\n",
|
|
" [False, False, False, False, False, False, False, False],\n",
|
|
" [False, False, False, True, False, False, False, False],\n",
|
|
" [False, False, True, False, False, False, False, False],\n",
|
|
" [False, False, False, False, False, True, False, False],\n",
|
|
" [False, False, False, False, True, False, False, False],\n",
|
|
" [False, False, False, False, False, False, False, False],\n",
|
|
" [False, False, False, False, False, False, False, False]]])"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"def _recursive_steps(board, rec_direction, rec_position, step_one=True) -> bool:\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 False\n",
|
|
" next_field = board[tuple(rec_position.tolist())]\n",
|
|
" if next_field == 0:\n",
|
|
" return False\n",
|
|
" if next_field == -1:\n",
|
|
" return _recursive_steps(board, rec_direction, rec_position, step_one=False)\n",
|
|
" if next_field == 1:\n",
|
|
" return not step_one\n",
|
|
"\n",
|
|
"\n",
|
|
"def get_possible_turns(boards: np.ndarray) -> np.ndarray:\n",
|
|
" \"\"\"Analyses a stack of boards.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" boards: A stack of boards to check.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" A stack of game boards containing boolean values showing where turns are possible for the player.\n",
|
|
" \"\"\"\n",
|
|
" assert len(boards.shape) == 3, \"The number fo input dimensions does not fit.\"\n",
|
|
" assert boards.shape[1:] == (\n",
|
|
" BOARD_SIZE,\n",
|
|
" BOARD_SIZE,\n",
|
|
" ), \"The input dimensions do not fit.\"\n",
|
|
"\n",
|
|
" _poss_turns = boards == 0 # checks where fields are empty.\n",
|
|
" _poss_turns &= binary_dilation(\n",
|
|
" boards == -1, SURROUNDING\n",
|
|
" ) # checks where fields are next to an enemy filed an empty\n",
|
|
" for game, idx, idy in itertools.product(\n",
|
|
" range(boards.shape[0]), range(BOARD_SIZE), range(BOARD_SIZE)\n",
|
|
" ):\n",
|
|
" position = idx, idy\n",
|
|
" if _poss_turns[game, idx, idy]:\n",
|
|
" _poss_turns[game, idx, idy] = any(\n",
|
|
" _recursive_steps(boards[game, :, :], direction, position)\n",
|
|
" for direction in DIRECTIONS\n",
|
|
" )\n",
|
|
" return _poss_turns\n",
|
|
"\n",
|
|
"\n",
|
|
"# some simple testing to ensure the function works after simple changes\n",
|
|
"# this testing is complete, its more of a smoke-test\n",
|
|
"test_array = get_new_games(3)\n",
|
|
"expected_result = np.zeros_like(test_array, dtype=bool)\n",
|
|
"expected_result[:, 4, 5] = expected_result[:, 2, 3] = True\n",
|
|
"expected_result[:, 5, 4] = expected_result[:, 3, 2] = True\n",
|
|
"np.testing.assert_equal(get_possible_turns(test_array), expected_result)\n",
|
|
"\n",
|
|
"\n",
|
|
"%timeit get_possible_turns(get_new_games(10)) # checks turn possibility evaluation time for 10 initial games\n",
|
|
"%timeit get_possible_turns(get_new_games(EXAMPLE_STACK_SIZE)) # check turn possibility evaluation time for EXAMPLE_STACK_SIZE initial games\n",
|
|
"\n",
|
|
"# shows a singe game\n",
|
|
"get_possible_turns(get_new_games(3))[:1]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Besides the ability to generate an array of possible turns there needs to be a functions that check if a given turn is possible.\n",
|
|
"On is needed for the action space validation. The other is for validating a players turn."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def move_possible(board: np.ndarray, move: np.ndarray) -> bool:\n",
|
|
" \"\"\"Checks if a turn is possible.\n",
|
|
"\n",
|
|
" Checks if a turn is possible. If no turn is possible to input array [-1, -1] is expected.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" board: A board where it should be checkt if a turn is possible.\n",
|
|
" move: The move that should be taken. Expected is the index of the filed where a stone should be placed [x, y]. If no placement is possible [-1, -1] is expected as an input.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" True if the move is possible\n",
|
|
" \"\"\"\n",
|
|
" if np.all(move == -1):\n",
|
|
" return not np.any(get_possible_turns(np.reshape(board, (1, 8, 8))))\n",
|
|
" return any(\n",
|
|
" _recursive_steps(board[:, :], direction, move) for direction in DIRECTIONS\n",
|
|
" )\n",
|
|
"\n",
|
|
"\n",
|
|
"# Some testing for this function and the underlying recursive functions that are called.\n",
|
|
"assert move_possible(get_new_games(1)[0], np.array([2, 3])) is True\n",
|
|
"assert move_possible(get_new_games(1)[0], np.array([3, 2])) is True\n",
|
|
"assert move_possible(get_new_games(1)[0], np.array([2, 2])) is False\n",
|
|
"assert move_possible(np.zeros((8, 8)), np.array([3, 2])) is False\n",
|
|
"assert move_possible(np.ones((8, 8)) * 1, np.array([-1, -1])) is True\n",
|
|
"assert move_possible(np.ones((8, 8)) * -1, np.array([-1, -1])) is True\n",
|
|
"assert move_possible(np.ones((8, 8)) * 0, np.array([-1, -1])) is True"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def moves_possible(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
|
|
" \"\"\"Checks if a stack of moves can be executed on a stack of boards.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" boards: A board where the next stone should be placed.\n",
|
|
" moves: A stack stones to be placed. Each move is formatted as an array in the form of [x, y] if no turn is possible the value [-1, -1] is expected.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" An array marking for each and every game and move in the stack if the move can be executed.\n",
|
|
" \"\"\"\n",
|
|
" arr_moves_possible = np.zeros(boards.shape[0], dtype=bool)\n",
|
|
" for game in range(boards.shape[0]):\n",
|
|
" if np.all(\n",
|
|
" moves[game] == -1\n",
|
|
" ): # can be all or any. All should be faster since most times neither value will be -1.\n",
|
|
" arr_moves_possible[game] = not np.any(\n",
|
|
" get_possible_turns(np.reshape(boards[game], (1, 8, 8)))\n",
|
|
" )\n",
|
|
" else:\n",
|
|
" arr_moves_possible[game] = any(\n",
|
|
" _recursive_steps(boards[game, :, :], direction, moves[game])\n",
|
|
" for direction in DIRECTIONS\n",
|
|
" )\n",
|
|
" return arr_moves_possible\n",
|
|
"\n",
|
|
"\n",
|
|
"np.testing.assert_array_equal(\n",
|
|
" moves_possible(np.ones((3, 8, 8)) * 1, np.array([[-1, -1]] * 3)),\n",
|
|
" np.array([True] * 3),\n",
|
|
")\n",
|
|
"\n",
|
|
"np.testing.assert_array_equal(\n",
|
|
" moves_possible(get_new_games(3), np.array([[2, 3], [3, 2], [3, 2]])),\n",
|
|
" np.array([True] * 3),\n",
|
|
")\n",
|
|
"np.testing.assert_array_equal(\n",
|
|
" moves_possible(get_new_games(3), np.array([[2, 2], [1, 1], [0, 0]])),\n",
|
|
" np.array([False] * 3),\n",
|
|
")\n",
|
|
"np.testing.assert_array_equal(\n",
|
|
" moves_possible(np.ones((3, 8, 8)) * -1, np.array([[-1, -1]] * 3)),\n",
|
|
" np.array([True] * 3),\n",
|
|
")\n",
|
|
"np.testing.assert_array_equal(\n",
|
|
" moves_possible(np.zeros((3, 8, 8)), np.array([[-1, -1]] * 3)),\n",
|
|
" np.array([True] * 3),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Reword functions\n",
|
|
"\n",
|
|
"For any kind of reinforcement learning is a reword function needed.\n",
|
|
"For otello this would be the final score, the information who won or changes to the score.\n",
|
|
"A combination of those three would also be possible.\n",
|
|
"It is probably not be possible to weight the current score to high in a reword function since that would be to close to a classic greedy algorithm.\n",
|
|
"But some direct influence would increase the learning speed.\n",
|
|
"In the next section are all three reword functions implemented to be combined and weight later on as needed."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"174 µs ± 6.34 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
|
|
"31.6 µs ± 1.6 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
|
|
"30.8 µs ± 1.58 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": 16,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class InvalidTurn(ValueError):\n",
|
|
" \"\"\"\n",
|
|
" This error is thrown if a given turn is not valid.\n",
|
|
" \"\"\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"91.5 ms ± 5.4 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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WmU+oKf0wRvPRUEA0YtAPOdiEubpsn7SnekT01VdfweFwICUlBXl5ebh48aIe7SKdfX/91hXTOvKeU24PuZ0VnZGAFF1rJ6APrIjVtQZpR1UQPfTQQ1izZg22b9+OwsJCnD9/HsOGDcP169ebXMfn88Hr9TZYSLw7Zj7Ug6zco3a7BPRpcNuGHiREIQGputYg7ajaNcvJ+f/P8c3MzMRDDz2EXr16YfPmzZgypfErW10uF37/+9+3rpWkOb9PXB0LrIbUNqoOtV6r/lm6++678YMf/ADl5Y38s3dLQUEBPB5PcHG73a0pSRqJMuj/0cbq1MKYFDSqDrVeq4Kouroa586dQ/fuTc+4ZLVaYbPZGiwknj0V0OjynaZJt+rc5huUQ4Zf19LKXfpN/wNJkUVVEP3yl7/E/v37ceHCBXz66af4yU9+gujoaDz99NN6tY90EtNZmcpDT7Y+jc9b5EMNvoG+R8q/wTnOW9SGqAqiS5cu4emnn0bfvn3x05/+FPHx8Th06BASEhL0ah/pyDlG3+uInDlNv38aH+l6HdFpNHPtAEUcVb+GGzdu1KsdJED6DGU+IT3Itcpzx5pyAMvwCOboUjsaMdiPZq6mpIjDe81MrEu6MqmZ1qMiyaJst7mHH17BGXyBnZqPiupwE19gJypRpul2SV8MIpMbthyI0jiIoizKdltSjOmow01N776vw00UI4Q7bimiMIhMzpYMZGm8e5a1JLRpY7/FBWzEHE3vvt+I2Zw2tg1iEBHSpgKD39BmW0MWAGkqZm09iCJ8gN8AQNgjo8B6H+AVHMSqsLZBYnHyfAIADPwN0OleZZI0f626m2Eli7I7lrVEXQgFbMNCePEfPIV3EA2Lqpth63ATdbiJjZjNEGrDOCKioLSpwIRSwKHMX9/iQezA+45sZb1wQijgIIowH+kogzJzf0sHsQPvl2Ev5iODIdTGcUREDdiSgbE76z3XbFsjN8hKysWKzhzlFH1zZ8fU+BYX8DYer/dcs5w7bpBVrpg+h9PYhv0o5NmxdoJBRI3qkg5kva382egnvV7BGWzCXGzCXD7p1SQkWZb1ngyiAa/XC7vdDkhArMPIyspzuGU/IEUpzwI3S23R9dl3c/a9pgLKVDAeT4v3mIoLIiIyhVCCSNyuGUdEpqnPvpuz74ERUSiEBVGnRCDvkrE1i5OAmsvKF2Km2qLrs+/m7Ps6hxKEoeDpeyISjkFERMIxiIhIOAYREQnHICIi4RhERCQcg4iIhGMQEZFwqoPo8uXLmDhxIuLj49GxY0c88MADKCkp0aNtRGQSqq6srqqqQlZWFrKzs7Ft2zYkJCTgq6++QpcuXfRqHxGZgKogevPNN+F0OrF69ergz5KTQ5icmIioGap2zT788EMMHjwYEyZMQLdu3TBgwACsWLGi2XV8Ph+8Xm+DhYioPlVB9PXXX6OwsBD33XcfduzYgeeffx5z5szB2rVrm1zH5XLBbrcHF6fT2epGE1H7oiqI/H4/Bg4ciIULF2LAgAH4xS9+gWnTpmHZsmVNrlNQUACPxxNc3G53qxtNRO2LqiDq3r070tPTG/zs/vvvx8WLF5tcx2q1wmazNViIiOpTFURZWVk4e/Zsg599+eWX6NWrl6aNIiJzURVEL774Ig4dOoSFCxeivLwc69evx1//+lfk5+fr1T4iMgFVQTRkyBBs2bIFGzZsQL9+/fD6669j8eLFyMvL06t9RGQCqqeKzc3NRW5urh5tISKT4r1mRCQcg4iIhGMQEZFwDCIiEo5BRETCMYiISDgGEREJxyAiIuEkWZZlIwt6vV7Y7XZAAmIdRlZWnsMt+wEpSnkWuFlqi67Pvpuz7zUVAGTA4/G0eLO7uCAiIlMIJYhU3+KhGY6ITFOffTdn3wMjolAIC6JOiUDeJWNrFicBNZeVL8RMtUXXZ9/N2fd1DiUIQ8GD1UQkHIOIiIRjEBGRcAwiIhKOQUREwjGIiEg4BhERCccgIiLhVAVR7969IUnSHQsfJ0REraHqyuqjR4+irq4u+Pr06dMYOXIkJkyYoHnDiMg8VAVRQkJCg9eLFi1Cnz598PDDD2vaKCIyl7DvNfv++++xbt06vPTSS5AkqcnP+Xw++Hy+4Guv1xtuSSJqp8I+WP3BBx/g2rVrmDx5crOfc7lcsNvtwcXpdIZbkojaqbCDqKioCDk5OXA4mp/Lo6CgAB6PJ7i43e5wSxJROxXWrtm///1v7N69G++//36Ln7VarbBareGUISKTCGtEtHr1anTr1g1jx47Vuj1EZEKqg8jv92P16tWYNGkSLBZxEzwSUfuhOoh2796Nixcv4rnnntOjPURkQqqHNKNGjYLB8+0TUTvHe82ISDgGEREJxyAiIuEYREQkHIOIiIRjEBGRcAwiIhJOkg2+KMjr9cJutwMSENv8/bKa4zPQ2Xf23Tg1FQBkwOPxwGazNftZcUFERKYQShCJu1mMIyLT1Gffzdn3wIgoFMKCqFMikHfJ2JrFSUDNZeULMVNt0fXZd3P2fZ1DCcJQ8GA1EQnHICIi4RhERCQcg4iIhGMQEZFwDCIiEo5BRETCMYiISDhVQVRXV4fXXnsNycnJ6NixI/r06YPXX3+dc1gTUauourL6zTffRGFhIdauXYuMjAyUlJTg2Wefhd1ux5w5c/RqIxG1c6qC6NNPP8X48eODD1bs3bs3NmzYgCNHjujSOCIyB1W7Zj/60Y+wZ88efPnllwCAkydP4pNPPkFOTo4ujSMic1A1Ipo3bx68Xi/S0tIQHR2Nuro6LFiwAHl5eU2u4/P54PP5gq+9Xm/4rSWidknViGjz5s0oLi7G+vXrcfz4caxduxZ//OMfsXbt2ibXcblcsNvtwcXpdLa60UTUvqgKol/96leYN28ennrqKTzwwAP4+c9/jhdffBEul6vJdQoKCuDxeIKL2+1udaOJqH1RtWt248YNREU1zK7o6Gj4/f4m17FarbBareG1johMQVUQjRs3DgsWLEDPnj2RkZGBzz//HH/+85/x3HPP6dU+IjIBVUH0zjvv4LXXXsPMmTNx9epVOBwOTJ8+Hb/97W/1ah8RmYCqIIqLi8PixYuxePFinZpDRGbEe82ISDgGEREJxyAiIuEYREQkHIOIiIRjEBGRcAwiIhKOQUREwkmywfO8ejwe3H333QCU53Eb6UYlABmABHRKNE9t0fXZdzG1RdcPPPf+2rVrsNvtzX7W8CC6dOkSpwIhMhG3242kpKRmP2N4EPn9flRUVCAuLg6SJKla1+v1wul0wu12w2az6dTCyKzPvpuvtuj6ra0tyzKuX78Oh8Nxx6wdt1N1r5kWoqKiWkzHlthsNiG/FJFQn303X23R9VtTu6VdsgAerCYi4RhERCRcmwoiq9WK3/3ud8JmfBRZn303X23R9Y2sbfjBaiKi27WpERERtU8MIiISjkFERMIxiIhIuDYVRJ999hmio6MxduxYw2pOnjwZkiQFl/j4eIwePRr/+te/DGtDZWUlZs+ejZSUFFitVjidTowbNw579uzRtW79vsfExODee+/FyJEjsWrVqmafZadH/frL6NGjda/dXP3y8nLda1dWVmLu3LlITU1Fhw4dcO+99yIrKwuFhYW4ceOGbnUnT56MJ5544o6f79u3D5Ik4dq1a7rUbVNBVFRUhNmzZ+PAgQOoqKgwrO7o0aNx5coVXLlyBXv27IHFYkFubq4htS9cuIBBgwbh448/xltvvYVTp05h+/btyM7ORn5+vu71A32/cOECtm3bhuzsbMydOxe5ubmora01rH79ZcOGDbrXba5+cnKyrjW//vprDBgwADt37sTChQvx+eef47PPPsOvf/1rbN26Fbt379a1vgiG3+IRrurqamzatAklJSWorKzEmjVr8MorrxhS22q1IjFRuXU5MTER8+bNw7Bhw/DNN98gISFB19ozZ86EJEk4cuQIYmNjgz/PyMgw5MGW9fveo0cPDBw4ED/84Q/x6KOPYs2aNZg6daph9UUQUX/mzJmwWCwoKSlp8J2npKRg/PjxaI9X3LSZEdHmzZuRlpaGvn37YuLEiVi1apWQL6S6uhrr1q1Damoq4uPjda313//+F9u3b0d+fn6DX8iAwHQqRnvkkUfQv39/vP/++0Lqt2fffvstdu7c2eR3DkD1zeJtQZsJoqKiIkycOBGAMlz2eDzYv3+/IbW3bt2Kzp07o3PnzoiLi8OHH36ITZs2tXhHcWuVl5dDlmWkpaXpWiccaWlpuHDhgu516v/dB5aFCxfqXrep+hMmTNC1XuA779u3b4Of33PPPcE2vPzyy7q2obG/85ycHF1rtolds7Nnz+LIkSPYsmULAMBiseBnP/sZioqKMGLECN3rZ2dno7CwEABQVVWFd999Fzk5OThy5Ah69eqlW91IHoLLsmzIv8z1/+4Dunbtqnvdpuo3NUrR25EjR+D3+5GXlwefz6drrcb+zg8fPhwcCOihTQRRUVERamtr4XA4gj+TZRlWqxVLliwJeaqBcMXGxiI1NTX4euXKlbDb7VixYgXeeOMN3ered999kCQJZWVlutUI15kzZ3Q/aAvc+XdvNKPrp6amQpIknD17tsHPU1JSAAAdO3bUvQ2N9fnSpUu61oz4XbPa2lr87W9/w5/+9CecOHEiuJw8eRIOh8PQMygBkiQhKioK//vf/3St07VrVzz++ONYunQpampq7nhfr1OpLfn4449x6tQpPPnkk0Lqt2fx8fEYOXIklixZ0uh33l5F/Iho69atqKqqwpQpU+4Y+Tz55JMoKirCjBkzdG2Dz+dDZWUlAGXXbMmSJaiursa4ceN0rQsAS5cuRVZWFh588EH84Q9/QGZmJmpra7Fr1y4UFhbizJkzutYP9L2urg7/+c9/sH37drhcLuTm5uKZZ57RtXb9+vVZLBbcc889utcW5d1330VWVhYGDx6M+fPnIzMzE1FRUTh69CjKysowaNAg0U3UnhzhcnNz5TFjxjT63uHDh2UA8smTJ3WrP2nSJBnK9OMyADkuLk4eMmSI/Pe//123mrerqKiQ8/Pz5V69esl33XWX3KNHD/nHP/6xvHfvXl3r1u+7xWKRExIS5Mcee0xetWqVXFdXp2vt2+vXX/r27at77UD98ePHG1LrdhUVFfKsWbPk5ORkOSYmRu7cubP84IMPym+99ZZcU1OjW92m+rx3714ZgFxVVaVLXU4DQkTCRfwxIiJq/xhERCQcg4iIhGMQEZFwDCIiEo5BRETCMYiISDgGEREJxyAiIuEYREQkHIOIiIRjEBGRcP8P3ZHAPKDQyJ0AAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<Figure size 300x300 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"def do_moves(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
|
|
" \"\"\"Executes a single move on a stack o Othello boards.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" boards: A stack of Othello boards where the next stone should be placed.\n",
|
|
" moves: A stack of stone placement orders for the game. Formatted as coordinates in an array [x, y] of the place where the stone should be placed. Should contain [-1,-1] if no new placement is possible.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" The new state of the board.\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" def _do_directional_move(\n",
|
|
" board: np.ndarray, rec_move: np.ndarray, rev_direction, step_one=True\n",
|
|
" ) -> bool:\n",
|
|
" \"\"\"Changes the color of enemy stones in one direction.\n",
|
|
"\n",
|
|
" This function works recursive. The argument step_one should always be used in its default value.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" board: A bord on which a stone was placed.\n",
|
|
" rec_move: The position on the board in x and y where this function is called from. Will be moved by recursive called.\n",
|
|
" rev_direction: The position where the stone was placed. Inside this recursion it will also be the last step that was checked.\n",
|
|
" step_one: Set to true if this is the first step in the recursion. False later on.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" True if a stone could be flipped.\n",
|
|
" All changes are made on the view of the numpy array and therefore not included in the return value.\n",
|
|
" \"\"\"\n",
|
|
" rec_position = rec_move + rev_direction\n",
|
|
" if np.any((rec_position >= 8) | (rec_position < 0)):\n",
|
|
" return False\n",
|
|
" next_field = board[tuple(rec_position.tolist())]\n",
|
|
" if next_field == 0:\n",
|
|
" return False\n",
|
|
" if next_field == 1:\n",
|
|
" return not step_one\n",
|
|
" if next_field == -1:\n",
|
|
" if _do_directional_move(board, rec_position, rev_direction, step_one=False):\n",
|
|
" board[tuple(rec_position.tolist())] = 1\n",
|
|
" return True\n",
|
|
" return False\n",
|
|
"\n",
|
|
" def _do_move(_board: np.ndarray, move: np.ndarray) -> None:\n",
|
|
" \"\"\"Executes a turn on a board.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" _board: The game board on wich to place a stone.\n",
|
|
" move: The coordinates of a stone that should be placed. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" All changes are made on the view of the numpy array.\n",
|
|
" \"\"\"\n",
|
|
" if np.all(move == -1):\n",
|
|
" if not move_possible(_board, move):\n",
|
|
" raise InvalidTurn(\"An action should be taken. A turn is possible.\")\n",
|
|
" return\n",
|
|
"\n",
|
|
" # noinspection PyTypeChecker\n",
|
|
" if _board[tuple(move.tolist())] != 0:\n",
|
|
" raise InvalidTurn(\"This turn is not possible.\")\n",
|
|
"\n",
|
|
" action = False\n",
|
|
" for direction in DIRECTIONS:\n",
|
|
" if _do_directional_move(_board, move, direction):\n",
|
|
" action = True\n",
|
|
" if not action:\n",
|
|
" raise InvalidTurn(\"This turn is not possible.\")\n",
|
|
"\n",
|
|
" # noinspection PyTypeChecker\n",
|
|
" _board[tuple(move.tolist())] = 1\n",
|
|
"\n",
|
|
" boards = boards.copy()\n",
|
|
" for game in range(boards.shape[0]):\n",
|
|
" _do_move(boards[game], moves[game])\n",
|
|
" return boards\n",
|
|
"\n",
|
|
"\n",
|
|
"%timeit do_moves(get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE))[0]\n",
|
|
"\n",
|
|
"plot_othello_board(\n",
|
|
" do_moves(\n",
|
|
" get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE)\n",
|
|
" )[0]\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## An abstract reversi game policy\n",
|
|
"\n",
|
|
"For an easy use of policies an abstract class containing the policy generation / requests an action in an inherited instance of this class.\n",
|
|
"This class filters the policy to only propose valid actions. Inherited instance do not need to care about this. This super class also manges exploration and exploitation with the epsilon value."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class GamePolicy(ABC):\n",
|
|
" \"\"\"\n",
|
|
" A game policy. Proposes where to place a stone next.\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" def __init__(self, epsilon: float):\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" epsilon: the epsilon / greedy value. Should be between zero and one. Set the mixture of policy and exploration. One means only the policy is used. Zero means only random policies are used. All mixtures inbetween between are possible.\n",
|
|
" \"\"\"\n",
|
|
" if 0 > epsilon > 1:\n",
|
|
" raise ValueError(\"Epsilon should be between zero and one.\")\n",
|
|
" self._epsilon: float = epsilon\n",
|
|
"\n",
|
|
" @property\n",
|
|
" def epsilon(self):\n",
|
|
" return self._epsilon\n",
|
|
"\n",
|
|
" @property\n",
|
|
" @abc.abstractmethod\n",
|
|
" def policy_name(self) -> str:\n",
|
|
" \"\"\"The name of this policy\"\"\"\n",
|
|
" raise NotImplementedError()\n",
|
|
"\n",
|
|
" @abc.abstractmethod\n",
|
|
" def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
|
|
" \"\"\"The internal policy is an unfiltered policy. It should only be called from inside this function\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" boards: A board where a policy should be calculated for.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" The policy for this board. Should have the same size as the boards array.\n",
|
|
" \"\"\"\n",
|
|
" raise NotImplementedError()\n",
|
|
"\n",
|
|
" def get_policy(self, boards: np.ndarray) -> np.ndarray:\n",
|
|
" \"\"\"Calculates the policy that should be followed.\n",
|
|
"\n",
|
|
" Calculates the policy that should be followed.\n",
|
|
" This function does include the usage of epsilon to configure greediness and exploration.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" boards: A set of boards that show the environment where the policy should be calculated for.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" A vector of indices. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
|
|
" \"\"\"\n",
|
|
" assert len(boards.shape) == 3\n",
|
|
" assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n",
|
|
"\n",
|
|
" if self.epsilon <= 0:\n",
|
|
" policies = np.random.rand(*boards.shape)\n",
|
|
" else:\n",
|
|
" policies = self._internal_policy(boards)\n",
|
|
" if self.epsilon < 1:\n",
|
|
" policies = policies * self.epsilon + np.random.rand(*boards.shape) * (\n",
|
|
" 1 - self.epsilon\n",
|
|
" )\n",
|
|
"\n",
|
|
" # todo talk to team about backpropagation of score and epsilon for greedy factor\n",
|
|
"\n",
|
|
" # todo possibly change this function to only validate the purpose turn and not all turns\n",
|
|
" possible_turns = get_possible_turns(boards)\n",
|
|
" policies[possible_turns == False] = -1.0\n",
|
|
" max_indices = [\n",
|
|
" np.unravel_index(policy.argmax(), policy.shape) for policy in policies\n",
|
|
" ]\n",
|
|
" policy_vector = np.array(max_indices, dtype=int)\n",
|
|
" no_turn_possible = np.all(policy_vector == 0, 1) & (policies[:, 0, 0] == -1.0)\n",
|
|
"\n",
|
|
" policy_vector[no_turn_possible, :] = IMPOSSIBLE\n",
|
|
" return policy_vector"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## A first policy\n",
|
|
"\n",
|
|
"To quantify the quality of a game AI there needs to be some benchmarks.\n",
|
|
"The easiest benchmark is to play against a random player.\n",
|
|
"The easiest player to use as a benchmark is the random player.\n",
|
|
"For this and testing purpose the random policy was implemented."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class RandomPolicy(GamePolicy):\n",
|
|
" \"\"\"\n",
|
|
" A policy playing a random turn by setting epsilon to 0.\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" def __init__(self, epsilon: float):\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": "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": 20,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"982 ms ± 33.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
|
|
"881 ms ± 13.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1200x600 with 8 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"def single_turn(\n",
|
|
" current_boards: np, policy: GamePolicy\n",
|
|
") -> tuple[np.ndarray, np.ndarray]:\n",
|
|
" \"\"\"Execute a single turn on a board.\n",
|
|
"\n",
|
|
" Places a new stone on the board. Turns captured enemy stones.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" current_boards: The current board before the game.\n",
|
|
" policy: The game policy to be used.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" The new game board and the policy vector containing the index of the action used.\n",
|
|
" \"\"\"\n",
|
|
" policy_results = policy.get_policy(current_boards)\n",
|
|
"\n",
|
|
" # if the constant VERIFY_POLICY is set to true the policy is verified. Should be good though.\n",
|
|
" # todo deactivate the policy verification after some testing.\n",
|
|
" if VERIFY_POLICY:\n",
|
|
" assert np.all(moves_possible(current_boards, policy_results)), (\n",
|
|
" current_boards[(moves_possible(current_boards, policy_results) == False)],\n",
|
|
" policy_results[(moves_possible(current_boards, policy_results) == False)],\n",
|
|
" np.where(moves_possible(current_boards, policy_results) == False),\n",
|
|
" )\n",
|
|
" return do_moves(current_boards, policy_results), policy_results\n",
|
|
"\n",
|
|
"\n",
|
|
"%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
|
|
"VERIFY_POLICY = False # type: ignore\n",
|
|
"%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
|
|
"VERIFY_POLICY = True # type: ignore\n",
|
|
"plot_othello_boards(\n",
|
|
" single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))[0][:8]\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Simulate a stack of games\n",
|
|
"This function will simulate a stack of games and return an array of policies and histories."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1200x4800 with 61 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"def simulate_game(\n",
|
|
" nr_of_games: int,\n",
|
|
" policies: tuple[GamePolicy, GamePolicy],\n",
|
|
") -> 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",
|
|
"\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=int)\n",
|
|
" action_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 2), dtype=int)\n",
|
|
" current_boards = get_new_games(nr_of_games)\n",
|
|
" for turn_index in range(SIMULATE_TURNS):\n",
|
|
" policy_index = turn_index % 2\n",
|
|
" policy = policies[policy_index]\n",
|
|
" board_history_stack[turn_index] = current_boards\n",
|
|
" if policy_index == 0:\n",
|
|
" current_boards = current_boards * -1\n",
|
|
" current_boards, action_taken = single_turn(current_boards, policy)\n",
|
|
" action_history_stack[turn_index] = action_taken\n",
|
|
"\n",
|
|
" if policy_index == 0:\n",
|
|
" current_boards = current_boards * -1\n",
|
|
"\n",
|
|
" return board_history_stack, action_history_stack\n",
|
|
"\n",
|
|
"\n",
|
|
"simulation_results = simulate_game(1, (RandomPolicy(1), RandomPolicy(1)))\n",
|
|
"plot_othello_boards(\n",
|
|
" drop_duplicate_boards(np.reshape(simulation_results[0], (-1, 8, 8)))\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"8.08 s ± 262 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%timeit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n",
|
|
"simulation_results = simulate_game(10, (RandomPolicy(1), RandomPolicy(1)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(70, 10, 8, 8)\n",
|
|
"(70, 10, 2)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(simulation_results[0].shape)\n",
|
|
"print(simulation_results[1].shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 118,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"board_history, action_history = simulation_results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 199,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"text/plain": [
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" [ 0.1, 0. , 0.1, 0.1, 0.1, 0. , 0. , 0.1, 0.1, 0.1],\n",
|
|
" [-0. , -0. , -0.2, -0.1, -0. , -0.2, -0.1, -0.1, -0.1, -0. ],\n",
|
|
" [ 0. , 0. , 0.2, 0.2, 0.1, 0.1, 0.1, 0. , 0. , 0.1],\n",
|
|
" [-0.1, -0. , -0. , -0. , -0.1, -0.1, -0. , -0.1, -0.1, -0.1],\n",
|
|
" [ 0. , 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0. , 0.2, 0.2],\n",
|
|
" [-0. , -0.1, -0.1, -0.1, -0. , -0. , -0.1, -0.2, -0.1, -0. ],\n",
|
|
" [ 0.1, 0. , 0. , 0.1, 0. , 0.1, 0.3, 0.1, 0.1, 0.2],\n",
|
|
" [-0.2, -0. , -0.1, -0. , -0.1, -0.1, -0. , -0.1, -0.1, -0. ],\n",
|
|
" [ 0.1, 0. , 0.2, 0.2, 0. , 0.1, 0.1, 0.1, 0.1, 0. ],\n",
|
|
" [-0.1, -0. , -0.1, -0. , -0.2, -0.1, -0. , -0. , -0.1, -0.1],\n",
|
|
" [ 0.1, 0. , 0.2, 0.1, 0. , 0.1, 0. , 0.1, 0. , 0.1],\n",
|
|
" [-0. , -0.1, -0.2, -0.1, -0.1, -0. , -0. , -0.1, -0.1, -0. ],\n",
|
|
" [ 0. , 0.1, 0. , 0. , 0.1, 0. , 0.2, 0.1, 0.2, 0.1],\n",
|
|
" [-0.1, -0.2, -0. , -0. , -0.1, -0.1, -0.1, -0.2, -0. , -0.1],\n",
|
|
" [ 0.1, 0.1, 0.1, 0. , 0.2, 0.1, 0.1, 0.1, 0. , 0. ],\n",
|
|
" [-0.1, -0.1, -0.2, -0.1, -0.1, -0.1, -0.1, -0.1, -0.3, -0. ],\n",
|
|
" [ 0.1, 0. , 0. , 0.1, 0.1, 0. , 0.1, 0.1, 0.2, 0. ],\n",
|
|
" [-0.4, -0. , -0.1, -0.1, -0.1, -0.1, -0.2, -0. , -0.2, -0. ],\n",
|
|
" [ 0. , 0. , 0.1, 0.1, 0. , 0. , 0.1, 0.4, 0.1, 0.1],\n",
|
|
" [-0.1, -0.1, -0. , -0.2, -0.2, -0.2, -0.2, -0.1, -0.1, -0. ],\n",
|
|
" [ 0.1, 0. , 0.2, 0.1, 0.1, 0. , 0.3, 0.1, 0.1, 0. ],\n",
|
|
" [-0.1, -0.1, -0.2, -0.4, -0.1, -0.2, -0.3, 0. , -0. , -0.1],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , -0.2, 0. , 0. , 0. , -0.1, 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0.1, 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]])"
|
|
]
|
|
},
|
|
"execution_count": 199,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"def calcualte_direct_score(board_history: np.ndarray) -> np.ndarray:\n",
|
|
" boards_evaluated = np.reshape(\n",
|
|
" evaluate_boards(np.reshape(board_history, (-1, 8, 8))), (SIMULATE_TURNS, -1)\n",
|
|
" )\n",
|
|
" direct_score = boards_evaluated - np.roll(boards_evaluated, shift=-1, axis=0)\n",
|
|
" direct_score[-1] = 0\n",
|
|
" return direct_score / 64\n",
|
|
"\n",
|
|
"\n",
|
|
"assert len(calcualte_direct_score(board_history).shape) == 2\n",
|
|
"assert calcualte_direct_score(board_history).shape[0] == SIMULATE_TURNS\n",
|
|
"calcualte_direct_score(board_history).shape\n",
|
|
"calcualte_direct_score(board_history).round(1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 200,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([ 0.4, 0.4, 0.1, 0.1, 0.3, 0.5, -0.2, -0.2, -0.1, -0.3])"
|
|
]
|
|
},
|
|
"execution_count": 200,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"def caluclate_final_evaluation_for_histoy(board_history: np.ndarray) -> np.ndarray:\n",
|
|
" final_evaluation = final_boards_evaluation(board_history[-1])\n",
|
|
" return final_evaluation / 64\n",
|
|
"\n",
|
|
"\n",
|
|
"assert len(caluclate_final_evaluation_for_histoy(board_history).shape) == 1\n",
|
|
"caluclate_final_evaluation_for_histoy(board_history).shape\n",
|
|
"caluclate_final_evaluation_for_histoy(board_history).round(1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 156,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([ 1, 1, 1, 1, 1, 1, -1, -1, -1, -1])"
|
|
]
|
|
},
|
|
"execution_count": 156,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"def calulate_who_won(board_history: np.ndarray) -> np.ndarray:\n",
|
|
" who_won = evaluate_who_won(boards[-1])\n",
|
|
" return who_won\n",
|
|
"\n",
|
|
"\n",
|
|
"calulate_who_won(board_history)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 172,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([[ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, False, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, False, True, True, True, False, True, True, True,\n",
|
|
" True],\n",
|
|
" [ True, True, True, True, True, True, True, False, True,\n",
|
|
" True],\n",
|
|
" [False, False, False, False, False, False, False, False, False,\n",
|
|
" False],\n",
|
|
" [False, True, False, False, False, True, False, False, False,\n",
|
|
" False],\n",
|
|
" [False, False, False, False, False, True, False, False, False,\n",
|
|
" False],\n",
|
|
" [False, False, False, False, False, False, False, False, False,\n",
|
|
" False],\n",
|
|
" [False, False, False, False, False, False, False, False, False,\n",
|
|
" False],\n",
|
|
" [False, False, False, False, False, False, False, False, False,\n",
|
|
" False],\n",
|
|
" [False, False, False, False, False, False, False, False, False,\n",
|
|
" False],\n",
|
|
" [False, False, False, False, False, False, False, False, False,\n",
|
|
" False],\n",
|
|
" [False, False, False, False, False, False, False, False, False,\n",
|
|
" False]])"
|
|
]
|
|
},
|
|
"execution_count": 172,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"def history_changed(board_history: np.ndarray) -> np.ndarray:\n",
|
|
" return ~np.all(np.roll(boards, shift=1, axis=0) == boards, axis=(2, 3))\n",
|
|
"\n",
|
|
"\n",
|
|
"history_changed(board_history)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 189,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(70, 10)"
|
|
]
|
|
},
|
|
"execution_count": 189,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"def get_gamma_table(board_history, gamma_value):\n",
|
|
" unchanged = history_changed(board_history)\n",
|
|
" gamma_values = np.ones_like(unchanged, dtype=float)\n",
|
|
" gamma_values[unchanged] = 0.8\n",
|
|
" return gamma_values\n",
|
|
"\n",
|
|
"\n",
|
|
"get_gamma_table(board_history, 0.8).shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 204,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([[ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [-0. , -0. , -0. , -0. , -0. , -0. , -0. , -0. , -0. , -0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0.1, 0.1, 0. , 0. , 0. ],\n",
|
|
" [-0. , -0. , -0. , -0. , -0. , -0. , -0. , -0. , -0. , -0. ],\n",
|
|
" [ 0.1, 0.1, 0. , 0.1, 0.1, 0. , 0. , 0.1, 0. , 0. ],\n",
|
|
" [-0. , -0.1, -0. , -0.1, -0.1, -0.1, -0.1, -0. , -0. , -0.1],\n",
|
|
" [ 0.1, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [-0.1, -0. , -0.1, -0.1, -0.1, -0. , -0.1, -0. , -0. , -0.1],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0.1, 0. , 0. , 0. , 0. ],\n",
|
|
" [-0.1, -0. , -0.1, -0.1, -0.1, -0. , -0.1, -0. , -0.1, -0. ],\n",
|
|
" [ 0. , 0.1, 0.1, 0.1, 0. , 0. , 0.1, 0. , 0. , 0. ],\n",
|
|
" [-0.1, -0. , -0. , -0.1, -0. , -0. , -0.1, -0.1, -0. , -0. ],\n",
|
|
" [ 0.1, 0. , 0.1, 0.1, 0. , 0.1, 0.1, 0. , 0. , 0. ],\n",
|
|
" [-0. , -0. , -0. , -0.1, -0. , -0. , -0. , -0.1, -0.1, -0. ],\n",
|
|
" [ 0. , 0. , 0.1, 0. , 0. , 0.1, 0.1, 0. , 0.1, 0.1],\n",
|
|
" [-0.1, -0. , -0. , -0. , -0. , -0. , -0.1, -0. , -0. , -0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.1],\n",
|
|
" [-0. , -0. , -0.1, -0.1, -0.1, -0.1, -0. , -0.1, -0. , -0.1],\n",
|
|
" [ 0. , 0.1, 0. , 0.1, 0.1, 0. , 0.1, 0.2, 0.1, 0.1],\n",
|
|
" [-0. , -0.1, -0.1, -0. , -0.2, -0. , -0. , -0.1, -0. , -0.1],\n",
|
|
" [ 0. , 0.1, 0.1, 0. , 0.1, 0. , 0. , 0. , 0.1, 0.1],\n",
|
|
" [-0. , -0. , -0. , -0. , -0.1, -0. , -0. , -0. , -0.1, -0. ],\n",
|
|
" [ 0. , 0.1, 0.1, 0.1, 0. , 0.1, 0.1, 0. , 0. , 0.1],\n",
|
|
" [-0.1, -0.1, -0.1, -0. , -0. , -0. , -0.1, -0. , -0.1, -0.1],\n",
|
|
" [ 0. , 0.1, 0. , 0. , 0. , 0. , 0.1, 0.1, 0. , 0. ],\n",
|
|
" [-0. , -0.1, -0. , -0.1, -0. , -0. , -0. , -0. , -0.1, -0. ],\n",
|
|
" [ 0.1, 0. , 0.1, 0. , 0.1, 0.1, 0.1, 0. , 0.1, 0. ],\n",
|
|
" [-0. , -0. , -0. , -0.1, -0. , -0.1, -0.1, -0. , -0.1, -0.1],\n",
|
|
" [ 0.1, 0. , 0.1, 0.1, 0.1, 0. , 0. , 0.1, 0.1, 0.1],\n",
|
|
" [-0.1, -0.1, -0.1, -0. , -0. , -0.1, -0. , -0. , -0.1, -0.1],\n",
|
|
" [ 0. , 0. , 0.1, 0. , 0. , 0.1, 0. , 0.1, 0.1, 0.1],\n",
|
|
" [-0. , -0.2, -0. , -0.1, -0. , -0.1, -0.1, -0.1, -0.1, -0. ],\n",
|
|
" [ 0. , 0.1, 0. , 0. , 0. , 0. , 0.1, 0. , 0. , 0. ],\n",
|
|
" [-0.1, -0. , -0. , -0.1, -0. , -0. , -0.1, -0. , -0.1, -0.1],\n",
|
|
" [ 0.1, 0.1, 0.1, 0.1, 0. , 0. , 0.1, 0.2, 0.2, 0.1],\n",
|
|
" [-0. , -0. , -0. , -0.1, -0. , -0. , -0.2, -0. , -0.1, -0.1],\n",
|
|
" [ 0. , 0.1, 0. , 0. , 0.1, 0.1, 0.1, 0.1, 0. , 0.1],\n",
|
|
" [-0.1, -0.1, -0.1, -0. , -0.1, -0. , -0. , -0.2, -0. , -0.1],\n",
|
|
" [ 0.1, 0. , 0.1, 0.1, 0.1, 0. , 0. , 0.1, 0.1, 0.1],\n",
|
|
" [-0. , -0. , -0.2, -0.1, -0. , -0.2, -0.1, -0.1, -0.1, -0. ],\n",
|
|
" [ 0. , 0. , 0.2, 0.2, 0.1, 0.1, 0.1, 0. , 0. , 0.1],\n",
|
|
" [-0.1, -0. , -0. , -0. , -0.1, -0.1, -0. , -0.1, -0.1, -0.1],\n",
|
|
" [ 0. , 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0. , 0.2, 0.2],\n",
|
|
" [-0. , -0.1, -0.1, -0.1, -0. , -0. , -0.1, -0.2, -0.1, -0. ],\n",
|
|
" [ 0.1, 0. , 0. , 0.1, 0. , 0.1, 0.3, 0.1, 0.1, 0.2],\n",
|
|
" [-0.2, -0. , -0.1, -0. , -0.1, -0.1, -0. , -0.1, -0.1, -0. ],\n",
|
|
" [ 0.1, 0. , 0.2, 0.2, 0. , 0.1, 0.1, 0.1, 0.1, 0. ],\n",
|
|
" [-0.1, -0. , -0.1, -0. , -0.2, -0.1, -0. , -0. , -0.1, -0.1],\n",
|
|
" [ 0.1, 0. , 0.2, 0.1, 0. , 0.1, 0. , 0.1, 0. , 0.1],\n",
|
|
" [-0. , -0.1, -0.2, -0.1, -0.1, -0. , -0. , -0.1, -0.1, -0. ],\n",
|
|
" [ 0. , 0.1, 0. , 0. , 0.1, 0. , 0.2, 0.1, 0.2, 0.1],\n",
|
|
" [-0.1, -0.2, -0. , -0. , -0.1, -0.1, -0.1, -0.2, -0. , -0.1],\n",
|
|
" [ 0.1, 0.1, 0.1, 0. , 0.2, 0.1, 0.1, 0.1, 0. , 0. ],\n",
|
|
" [-0.1, -0.1, -0.2, -0.1, -0.1, -0.1, -0.1, -0.1, -0.3, -0. ],\n",
|
|
" [ 0.1, 0. , 0. , 0.1, 0.1, 0. , 0.1, 0.1, 0.2, 0. ],\n",
|
|
" [-0.4, -0. , -0.1, -0.1, -0.1, -0.1, -0.2, -0. , -0.2, -0. ],\n",
|
|
" [ 0. , 0. , 0.1, 0.1, 0. , 0. , 0.1, 0.4, 0.1, 0.1],\n",
|
|
" [-0.1, -0.1, -0. , -0.2, -0.2, -0.2, -0.2, -0.1, -0.1, -0. ],\n",
|
|
" [ 0.1, 0. , 0.2, 0.1, 0.1, 0. , 0.3, 0.1, 0.1, 0. ],\n",
|
|
" [-0.1, -0.1, -0.2, -0.4, -0.1, -0.2, -0.3, 0. , -0. , -0.1],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , -0.2, 0. , 0. , 0. , -0.1, 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0.1, 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
|
|
" [ 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, -0.4, -0.4, -0.4, -0.4]])"
|
|
]
|
|
},
|
|
"execution_count": 204,
|
|
"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",
|
|
" gama_table = get_gamma_table(board_history, gamma)\n",
|
|
" direct_score = calcualte_direct_score(board_history) * (\n",
|
|
" 1 - who_won_fraction + final_score_fraction\n",
|
|
" )\n",
|
|
" direct_score[-1] += calulate_fina_score(board_history) * final_score_fraction\n",
|
|
" direct_score[-1] += calulate_who_won(board_history) * who_won_fraction\n",
|
|
" return direct_score\n",
|
|
"\n",
|
|
"\n",
|
|
"calculate_q_reword(board_history).round(1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 181,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'rewords' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
|
"\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)",
|
|
"Cell \u001B[1;32mIn[181], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mrewords\u001B[49m\n\u001B[0;32m 2\u001B[0m evaluate_boards(boards)\u001B[38;5;241m.\u001B[39mshape\n",
|
|
"\u001B[1;31mNameError\u001B[0m: name 'rewords' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"rewords\n",
|
|
"evaluate_boards(boards).shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def calculate_simple_rewords()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Sources\n",
|
|
"\n",
|
|
"* Game rules and example board images [https://en.wikipedia.org/wiki/Reversi](https://en.wikipedia.org/wiki/Reversi)\n",
|
|
"* Game rules and example game images [https://de.wikipedia.org/wiki/Othello_(Spiel)](https://de.wikipedia.org/wiki/Othello_(Spiel))\n",
|
|
"* Game strategy examples [https://de.wikipedia.org/wiki/Computer-Othello](https://de.wikipedia.org/wiki/Computer-Othello)\n",
|
|
"* Image for 8 directions [https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281](https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|