├── src ├── __init__.py ├── training.py ├── neural_network.py ├── util.py ├── evaluation.py ├── data_processing.py ├── board.py ├── checkers.py └── state.py ├── .gitignore ├── model ├── model.pth └── model_test.pth ├── static ├── img │ └── pieces │ │ ├── bB.png │ │ ├── bK.png │ │ ├── bN.png │ │ ├── bP.png │ │ ├── bQ.png │ │ ├── bR.png │ │ ├── wB.png │ │ ├── wK.png │ │ ├── wN.png │ │ ├── wP.png │ │ ├── wQ.png │ │ └── wR.png ├── chessboard.min.css ├── index.html ├── chessboard.min.js └── jquery.min.js ├── README.md └── main.py /src/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | data/ 3 | .idea/ -------------------------------------------------------------------------------- /model/model.pth: 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https://raw.githubusercontent.com/nedeljkovignjevic/checkers-game/HEAD/static/img/pieces/wR.png -------------------------------------------------------------------------------- /static/chessboard.min.css: -------------------------------------------------------------------------------- 1 | /*! chessboard.js v0.3.0 | (c) 2013 Chris Oakman | MIT License chessboardjs.com/license */ 2 | .clearfix-7da63{clear:both}.board-b72b1{border:2px solid #404040;-moz-box-sizing:content-box;box-sizing:content-box}.square-55d63{float:left;position:relative;-webkit-touch-callout:none;-webkit-user-select:none;-khtml-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.white-1e1d7{background-color:#f0d9b5;color:#b58863}.black-3c85d{background-color:#b58863;color:#f0d9b5}.highlight1-32417,.highlight2-9c5d2{-webkit-box-shadow:inset 0 0 3px 3px yellow;-moz-box-shadow:inset 0 0 3px 3px yellow;box-shadow:inset 0 0 3px 3px yellow}.notation-322f9{cursor:default;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;position:absolute}.alpha-d2270{bottom:1px;right:3px}.numeric-fc462{top:2px;left:2px} -------------------------------------------------------------------------------- /src/training.py: -------------------------------------------------------------------------------- 1 | from torch.utils.data import DataLoader 2 | from torch import nn 3 | from torch import optim 4 | 5 | from src.neural_network import Net, CheckersDataset 6 | 7 | 8 | def train(): 9 | device = 'cuda' 10 | dataset = CheckersDataset() 11 | train_loader = DataLoader(dataset, batch_size=128, shuffle=True) 12 | 13 | model = Net() 14 | optimizer = optim.Adam(model.parameters()) 15 | f_loss = nn.MSELoss() 16 | 17 | model.cuda() 18 | model.train() 19 | 20 | for epoch in range(5): 21 | all_loss = 0 22 | n_loss = 0 23 | for data, target in train_loader: 24 | target = target.unsqueeze(-1) 25 | data, target = data.to(device), target.to(device) 26 | data, target = data.float(), target.float() 27 | 28 | optimizer.zero_grad() 29 | output = model(data) 30 | 31 | loss = f_loss(output, target) 32 | loss.backward() 33 | optimizer.step() 34 | 35 | all_loss += loss.item() 36 | n_loss += 1 37 | 38 | print(f'{epoch} {all_loss / n_loss}') 39 | 40 | -------------------------------------------------------------------------------- /src/neural_network.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | from torch.utils.data import Dataset 6 | 7 | 8 | class CheckersDataset(Dataset): 9 | """ 10 | Checkers dataset -> http://www.fierz.ch/download.php 11 | About 20_000 games 12 | """ 13 | 14 | def __init__(self): 15 | data = np.load('data/processed.npz') 16 | self.x = data['arr_0'] 17 | self.y = data['arr_1'] 18 | 19 | def __len__(self): 20 | return self.x.shape[0] 21 | 22 | def __getitem__(self, index): 23 | return self.x[index], self.y[index] 24 | 25 | 26 | class Net(nn.Module): 27 | """ 28 | TODO: Try with RL, TD, NEAT (neuroevolution) ... 29 | """ 30 | 31 | def __init__(self): 32 | super(Net, self).__init__() 33 | self.fc1 = nn.Linear(32, 64) 34 | self.fc2 = nn.Linear(64, 64) 35 | self.fc3 = nn.Linear(64, 128) 36 | self.fc4 = nn.Linear(128, 128) 37 | self.fc5 = nn.Linear(128, 256) 38 | self.fc6 = nn.Linear(256, 256) 39 | self.last = nn.Linear(256, 1) 40 | 41 | def forward(self, x): 42 | x = F.relu(self.fc1(x)) 43 | x = F.relu(self.fc2(x)) 44 | x = F.relu(self.fc3(x)) 45 | x = F.relu(self.fc4(x)) 46 | x = F.relu(self.fc5(x)) 47 | x = F.relu(self.fc6(x)) 48 | 49 | x = x.view(-1, 256) 50 | x = self.last(x) 51 | 52 | return F.tanh(x) 53 | -------------------------------------------------------------------------------- /src/util.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | """ 4 | Getting all legal steps for 'get_states' function from State class 5 | """ 6 | 7 | 8 | def non_legal_positions(): 9 | return [1, 3, 5, 7, 10 | 8, 10, 12, 14, 11 | 17, 19, 21, 23, 12 | 24, 26, 28, 30, 13 | 33, 35, 37, 39, 14 | 40, 42, 44, 46, 15 | 49, 51, 53, 55, 16 | 56, 58, 60, 62] 17 | 18 | 19 | def black_steps(): 20 | return [-7, -9] 21 | 22 | 23 | def black_jump_steps(): 24 | return [-14, -18] 25 | 26 | 27 | def white_steps(): 28 | return [7, 9] 29 | 30 | 31 | def white_jump_steps(): 32 | return [14, 18] 33 | 34 | 35 | def king_steps(): 36 | return [7, 9, -7, -9] 37 | 38 | 39 | def king_jump_steps(): 40 | return [14, 18, -14, 18] 41 | 42 | 43 | mapa = {1: 6, # Dictionary for data processing 44 | 2: 4, 45 | 3: 2, 46 | 4: 0, 47 | 5: 15, 48 | 6: 13, 49 | 7: 11, 50 | 8: 9, 51 | 9: 22, 52 | 10: 20, 53 | 11: 18, 54 | 12: 16, 55 | 13: 31, 56 | 14: 29, 57 | 15: 27, 58 | 16: 25, 59 | 17: 38, 60 | 18: 36, 61 | 19: 34, 62 | 20: 32, 63 | 21: 47, 64 | 22: 45, 65 | 23: 43, 66 | 24: 41, 67 | 25: 54, 68 | 26: 52, 69 | 27: 50, 70 | 28: 48, 71 | 29: 63, 72 | 30: 61, 73 | 31: 59, 74 | 32: 57} 75 | 76 | 77 | def map_state(board: list): 78 | state = np.empty(32, int) 79 | for key in mapa.keys(): 80 | if board[mapa[key]] == 'w': 81 | state[key - 1] = -1 82 | elif board[mapa[key]] == 'b': 83 | state[key - 1] = 1 84 | elif board[mapa[key]] == 'W': 85 | state[key - 1] = -2 86 | elif board[mapa[key]] == "B": 87 | state[key - 1] = 2 88 | else: 89 | state[key - 1] = 0 90 | 91 | return state 92 | -------------------------------------------------------------------------------- /src/evaluation.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from math import floor 3 | 4 | from src.neural_network import Net 5 | from src.util import map_state 6 | 7 | 8 | MODEL = Net() 9 | MODEL.load_state_dict(torch.load('model/model_test.pth', map_location=torch.device('cpu'))) 10 | MODEL.eval() 11 | 12 | 13 | def alpha_beta(state, depth, a, b, max_player): 14 | """ 15 | Minimax with alpha–beta pruning implementation 16 | """ 17 | 18 | if depth == 0 or state.is_terminal_state(): 19 | return value_network(state) 20 | 21 | if max_player is True: 22 | value = -10000 23 | for s in state.get_states(): 24 | value = max(value, alpha_beta(s, depth - 1, a, b, False)) 25 | a = max(a, value) 26 | if a >= b: 27 | break 28 | return value 29 | 30 | else: 31 | value = 10000 32 | for s in state.get_states(): 33 | value = min(value, alpha_beta(s, depth - 1, a, b, True)) 34 | b = min(b, value) 35 | if b <= a: 36 | break 37 | return value 38 | 39 | 40 | def heuristic_value(state): 41 | """" 42 | Using heurustic method to evaluate board sate 43 | """ 44 | 45 | board = state.board 46 | 47 | value = 0 48 | b2 = 0 49 | for i in range(63, -1, -1): 50 | if b2 == 8: b2 = 0 51 | 52 | if board[i] == '-': 53 | b2 += 1 54 | if b2 == 8: b2 = 0 55 | continue 56 | 57 | b1 = floor(i / 8) 58 | if board[i] == 'w': 59 | value -= 5 + 7 - b1 + abs(b2 - 4 + abs(b1 - 4)) 60 | elif board[i] == 'b': 61 | value += 5 + b1 + abs(b2 - 4) + abs(b1 - 4) 62 | elif board[i] == 'W': 63 | value -= 14 + abs(b2 - 4) + abs(b1 - 4) 64 | elif board[i] == 'B': 65 | value += 14 + abs(b2 - 4) + abs(b1 - 4) 66 | b2 += 1 67 | 68 | return value 69 | 70 | 71 | def value_network(state): 72 | """ 73 | Using neural network to evaluate board state 74 | """ 75 | 76 | board = state.board 77 | state = map_state(board) 78 | state = torch.FloatTensor(state).unsqueeze(0) 79 | prediction = MODEL(state) 80 | return prediction 81 | 82 | -------------------------------------------------------------------------------- /static/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 31 | 32 | Checkers 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 |
44 | 45 |
46 |

47 | 48 | 49 | 87 | 88 | 89 | 90 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Checkers game 2 | Implementation of checkers (draughts) strategy board game with AI based bot 3 | 4 | ![check](https://user-images.githubusercontent.com/54076398/78457052-dac23c80-76a7-11ea-8d28-89a322a5bdc7.jpg) 5 | 6 | This project is intended to showcase the ability of neural networks to learn to play a game of checkers.
7 | The trained network is used to guide a search algorithm - minimax with alpha-beta pruning
8 | to select the most promising moves in games.
9 | 10 | Neural network is used for evaluating the board state, it takes board state as input and outputs value between -1 and 1
11 | (values close to -1 -> white wins, values close to 1 -> black wins). AlphaZero introduced this as "value network".

12 | Currently, neural network is just MLP (multilayer perceptron) model with 6 hidden layers:
13 | -> 32 neurons for input layer | 64, 64, 128, 128, 256, 256 for hidden layers and 1 neuron for output layer 14 |

15 | I'm working on temporal difference learning method (TD leaf) that seems way better then MLP atm
16 | (need some time to fully-train the model).

17 | Main idea is to use supervised learning to (pre)train some model and then to improve that model with self-play, what DeepMind did with AlphaZero. 18 |

19 | 20 | ## Dataset used for training 21 | -> http://www.fierz.ch/download.php 22 | 23 | About 20000 games, results are mostly draw (about 14000) - not so great for neural nets but i can't find better one atm. 24 |

25 | 26 | ## Requirements 27 | 1. python 3 28 | 2. python-chess 29 | 3. flask 30 | 4. numpy 31 | 5. pytorch 32 |

33 | 34 | ## Usage 35 | ``` 36 | 1. python main.py # runs web server on localhost:5000 37 | 2. Web browse to localhost:5000 38 | 39 | At this phase game does not support (interface) validation and multiple jumps so dont use it right now :) 40 | Console version is fully featured (it supports validation and multiple jumps) 41 | ``` 42 | 43 | ## References 44 | - Dataset -> http://www.fierz.ch/download.php 45 | - Format -> http://www.bobnewell.net/filez/reinfeld2ndedition.pdf 46 | - ML update -> http://www.bobnewell.net/nucleus/checkers.php?itemid=1177 47 | - TD -> https://www.researchgate.net/publication/221185124_Temporal_Difference_Approach_to_Playing_Give-Away_Checkers 48 | - TD -> http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.6366&rep=rep1&type=pdf 49 | - https://www.researchgate.net/publication/3302690_Evolving_neural_networks_to_play_checkers_without_expert_knowledge 50 | -------------------------------------------------------------------------------- /src/data_processing.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from src.board import init_board, print_board 3 | from src.checkers import play_move, Turn 4 | from src.util import mapa, map_state 5 | 6 | 7 | def get_dataset(): 8 | """ 9 | Parse checkers OCA dataset and save as numpy array 10 | 11 | 1/2-1/2: 0 -> draw 12 | 0-1: 1 -> black wins 13 | 1-0: -1 -> white wins 14 | """ 15 | 16 | states, values = [], [] 17 | value = {'1/2-1/2': 0, '0-1': 1, '1-0': -1} 18 | n_games = {'1/2-1/2': 0, '0-1': 0, '1-0': 0} 19 | parsed_game = 0 20 | with open('data/OCA_2.0.txt') as file: 21 | for part in file.read().split('\n\n'): 22 | tokens = part.split() 23 | val = tokens[-1] 24 | n_games[val] = n_games[val] + 1 25 | print(f'Parsing game {parsed_game}') 26 | 27 | board = init_board() 28 | skiping = True 29 | turn = Turn.WHITE 30 | for token in tokens: 31 | if skiping is True: 32 | if token == '1.': 33 | skiping = False 34 | continue 35 | else: 36 | if '.' in token or token == val: 37 | continue 38 | if 'x' in token: # multiple jump 39 | new_token = token.split('x') 40 | n = len(new_token) 41 | for i in range(n - 1): 42 | try: 43 | move = [mapa[int(new_token[i])], mapa[int(new_token[i + 1])]] 44 | except: 45 | break 46 | play_move(board, move, turn) 47 | # print_board(board) 48 | # print() 49 | else: 50 | t = token.split('-') 51 | try: 52 | move = [mapa[int(t[0])], mapa[int(t[1])]] 53 | except: 54 | break 55 | play_move(board, move, turn) 56 | # print_board(board) 57 | # print() 58 | 59 | states.append(map_state(board)) 60 | values.append(value[val]) 61 | turn = Turn.BLACK if turn == Turn.WHITE else Turn.WHITE 62 | 63 | parsed_game += 1 64 | 65 | print(f'1/2-1/2 -> {n_games["1/2-1/2"]}') 66 | print(f'0-1 -> {n_games["0-1"]}') 67 | print(f'1-0 -> {n_games["1-0"]}') 68 | 69 | x, y = np.array(states), np.array(values) 70 | np.savez("data/processed.npz", x, y) 71 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from flask import Flask, request 2 | from chess import Board as ChessBoard 3 | from chess import Piece, QUEEN, Color, WHITE, BLACK 4 | 5 | from src.board import init_board, init_gui_board, print_board, game_over 6 | from src.state import State, Turn 7 | from src.checkers import move_function, play_move 8 | 9 | 10 | BOARD = ChessBoard(None) 11 | init_gui_board(BOARD) 12 | state_board = init_board() 13 | 14 | app = Flask(__name__) 15 | 16 | 17 | def human_move(src, dest): 18 | piece = BOARD.remove_piece_at(src) 19 | if dest > 55: 20 | BOARD.set_piece_at(dest, Piece(QUEEN, Color(WHITE))) 21 | else: 22 | BOARD.set_piece_at(dest, piece) 23 | 24 | jump = play_move(state_board, [src, dest], Turn.WHITE) 25 | if jump is not None: 26 | BOARD.remove_piece_at(jump) 27 | 28 | print_board(state_board) 29 | 30 | 31 | def computer_move(move): 32 | jump = play_move(state_board, move, Turn.BLACK) 33 | 34 | piece = BOARD.remove_piece_at(move[0]) 35 | if move[1] < 8: 36 | BOARD.set_piece_at(move[1], Piece(QUEEN, BLACK)) 37 | else: 38 | BOARD.set_piece_at(move[1], piece) 39 | 40 | if jump is not None: 41 | BOARD.remove_piece_at(jump) 42 | 43 | print_board(state_board) 44 | 45 | 46 | @app.route('/') 47 | def hello(): 48 | index_html = open("static/index.html").read() 49 | return index_html.replace('start', BOARD.fen()) 50 | 51 | 52 | # @app.route("/self-play") 53 | # def self_play(): 54 | # """ 55 | # TODO: Implement this 56 | # """ 57 | # pass 58 | 59 | 60 | @app.route("/move") 61 | def move(): 62 | if game_over(state_board): 63 | print("GAME IS OVER") 64 | response = app.response_class( 65 | response="game over", 66 | status=200 67 | ) 68 | return response 69 | 70 | source = int(request.args.get('from', default='')) 71 | destination = int(request.args.get('to', default='')) 72 | 73 | # human move 74 | human_move(source, destination) 75 | 76 | # computer_move 77 | state = State(state_board, Turn.BLACK, []) 78 | move = move_function(state) 79 | computer_move(move) 80 | 81 | response = app.response_class( 82 | response=BOARD.fen(), 83 | status=200 84 | ) 85 | 86 | return response 87 | 88 | 89 | @app.route("/new-game") 90 | def new_game(): 91 | BOARD.clear_board() 92 | init_gui_board(BOARD) 93 | state_board.clear() 94 | temp = init_board() 95 | for i in range(64): 96 | state_board.append(temp[i]) 97 | 98 | response = app.response_class( 99 | response=BOARD.fen(), 100 | status=200 101 | ) 102 | return response 103 | 104 | 105 | if __name__ == '__main__': 106 | 107 | app.run(debug=True) 108 | # game_loop() 109 | # self_play() 110 | -------------------------------------------------------------------------------- /src/board.py: -------------------------------------------------------------------------------- 1 | from src.state import State, Turn 2 | import chess 3 | 4 | 5 | def init_board(): 6 | """ 7 | Initialize checkers board (8x8) 8 | """ 9 | 10 | board = ['w', '-', 'w', '-', 'w', '-', 'w', '-', 11 | '-', 'w', '-', 'w', '-', 'w', '-', 'w', 12 | 'w', '-', 'w', '-', 'w', '-', 'w', '-', 13 | '-', '-', '-', '-', '-', '-', '-', '-', 14 | '-', '-', '-', '-', '-', '-', '-', '-', 15 | '-', 'b', '-', 'b', '-', 'b', '-', 'b', 16 | 'b', '-', 'b', '-', 'b', '-', 'b', '-', 17 | '-', 'b', '-', 'b', '-', 'b', '-', 'b'] 18 | 19 | return board 20 | 21 | 22 | def print_board(board): 23 | for i in range(56, -1, -8): 24 | for j in range(8): 25 | print(board[i+j], end=' ') 26 | print() 27 | 28 | 29 | def game_over(board): 30 | """ 31 | 1: num_pieces >=4 32 | 2: num_pieces = 1 -> 1 wins 33 | """ 34 | 35 | b_counter = 0 36 | w_counter = 0 37 | for i in range(64): 38 | if board[i] == "b" or board[i] == "B": 39 | b_counter += 1 40 | elif board[i] == "w" or [i] == "W": 41 | w_counter += 1 42 | 43 | state = State(board, Turn.WHITE, []) 44 | 45 | if w_counter == 0 or (b_counter > 3 and w_counter < 2) or len(state.get_states()) == 0: 46 | print("GAME OVER! You lost!") 47 | return True 48 | 49 | if b_counter == 0 or (w_counter > 3 and b_counter < 2): 50 | print("CONGRATS! You won!") 51 | return True 52 | 53 | return False 54 | 55 | 56 | def init_gui_board(board): 57 | board.set_piece_at(chess.B8, chess.Piece.from_symbol('k'), chess.KING) 58 | board.set_piece_at(chess.D8, chess.Piece.from_symbol('k'), chess.KING) 59 | board.set_piece_at(chess.F8, chess.Piece.from_symbol('k'), chess.KING) 60 | board.set_piece_at(chess.H8, chess.Piece.from_symbol('k'), chess.KING) 61 | board.set_piece_at(chess.A7, chess.Piece.from_symbol('k'), chess.KING) 62 | board.set_piece_at(chess.C7, chess.Piece.from_symbol('k'), chess.KING) 63 | board.set_piece_at(chess.E7, chess.Piece.from_symbol('k'), chess.KING) 64 | board.set_piece_at(chess.G7, chess.Piece.from_symbol('k'), chess.KING) 65 | board.set_piece_at(chess.B6, chess.Piece.from_symbol('k'), chess.KING) 66 | board.set_piece_at(chess.D6, chess.Piece.from_symbol('k'), chess.KING) 67 | board.set_piece_at(chess.F6, chess.Piece.from_symbol('k'), chess.KING) 68 | board.set_piece_at(chess.H6, chess.Piece.from_symbol('k'), chess.KING) 69 | 70 | board.set_piece_at(chess.A3, chess.Piece.from_symbol('K'), chess.KING) 71 | board.set_piece_at(chess.C3, chess.Piece.from_symbol('K'), chess.KING) 72 | board.set_piece_at(chess.E3, chess.Piece.from_symbol('K'), chess.KING) 73 | board.set_piece_at(chess.G3, chess.Piece.from_symbol('K'), chess.KING) 74 | board.set_piece_at(chess.B2, chess.Piece.from_symbol('K'), chess.KING) 75 | board.set_piece_at(chess.D2, chess.Piece.from_symbol('K'), chess.KING) 76 | board.set_piece_at(chess.F2, chess.Piece.from_symbol('K'), chess.KING) 77 | board.set_piece_at(chess.H2, chess.Piece.from_symbol('K'), chess.KING) 78 | board.set_piece_at(chess.A1, chess.Piece.from_symbol('K'), chess.KING) 79 | board.set_piece_at(chess.C1, chess.Piece.from_symbol('K'), chess.KING) 80 | board.set_piece_at(chess.E1, chess.Piece.from_symbol('K'), chess.KING) 81 | board.set_piece_at(chess.G1, chess.Piece.from_symbol('K'), chess.KING) 82 | return board 83 | -------------------------------------------------------------------------------- /src/checkers.py: -------------------------------------------------------------------------------- 1 | from src.board import init_board, print_board, game_over 2 | from src.evaluation import alpha_beta 3 | from src.state import State, Turn 4 | 5 | 6 | def game_loop(): 7 | """ 8 | Checkers console game loop 9 | """ 10 | 11 | board = init_board() 12 | print_board(board) 13 | print() 14 | 15 | while True: 16 | 17 | if game_over(board): 18 | break 19 | 20 | while True: 21 | coordinates_input = input("\nPlayer 1 input coordinates (what to move and where eg. 16 25): ") 22 | move = validate_move(coordinates_input, board) 23 | if move is not None: 24 | play_move(board, move, Turn.WHITE) 25 | print_board(board) 26 | break 27 | 28 | computer_move(board, Turn.BLACK) 29 | 30 | 31 | def self_play(): 32 | """ 33 | AI vs AI loop 34 | """ 35 | 36 | board = init_board() 37 | print_board(board) 38 | print() 39 | 40 | while True: 41 | 42 | if game_over(board): 43 | break 44 | 45 | computer_move(board, Turn.WHITE) 46 | computer_move(board, Turn.BLACK) 47 | 48 | 49 | def computer_move(board: list, turn: Turn): 50 | """ 51 | Play the computer move 52 | """ 53 | 54 | state = State(board, turn, []) 55 | print(f"\n{str(turn)} thinking...") 56 | move = move_function(state) 57 | jump_move = play_move(board, move, turn) 58 | print(move) 59 | print_board(board) 60 | while jump_move is not None: 61 | temp = board 62 | state = State(board, turn, []) 63 | move = move_function(state) 64 | jump_move = play_move(board, move, turn) 65 | if jump_move is None: 66 | board = temp 67 | else: 68 | print(move) 69 | print_board(board) 70 | 71 | 72 | def move_function(state: State): 73 | """ 74 | Return best computer move 75 | """ 76 | 77 | best_move = None 78 | val = -10000 79 | depth = 5 80 | for state in state.get_states(): 81 | score = alpha_beta(state, depth, -10000, 10000, True) 82 | if score > val: 83 | val, best_move = score, state.move 84 | 85 | return best_move 86 | 87 | 88 | def play_move(board: list, move: list, turn: Turn): 89 | """ 90 | Update values on board 91 | """ 92 | 93 | piece = board[move[0]] 94 | board[move[0]] = '-' 95 | 96 | if turn == turn.BLACK: 97 | board[move[1]] = piece if move[1] > 8 else piece.upper() 98 | elif turn == turn.WHITE: 99 | board[move[1]] = piece if move[1] < 56 else piece.upper() 100 | 101 | if abs(move[0] - move[1]) > 9: 102 | x = move[1] - move[0] 103 | mid_i = move[0] + int(x / 2) 104 | board[mid_i] = '-' 105 | return mid_i 106 | 107 | return None 108 | 109 | 110 | def validate_move(coordinates_input: str, board: list): 111 | # noinspection PyBroadException 112 | try: 113 | tokens = coordinates_input.split() 114 | src, dest = int(tokens[0]), int(tokens[1]) 115 | except Exception: 116 | return None 117 | 118 | move = [src, dest] 119 | state = State(board, Turn.WHITE, []) 120 | legal_moves = [el.move for el in state.get_states()] 121 | print(f'Available moves: {legal_moves}') 122 | if move in legal_moves: 123 | return move 124 | else: 125 | return None 126 | -------------------------------------------------------------------------------- /src/state.py: -------------------------------------------------------------------------------- 1 | import enum 2 | from copy import deepcopy 3 | from src.util import black_steps, black_jump_steps, white_steps, white_jump_steps, king_steps, king_jump_steps, non_legal_positions 4 | 5 | 6 | class Turn(enum.Enum): 7 | WHITE = 0 8 | BLACK = 1 9 | 10 | 11 | class State(object): 12 | """ 13 | Modelling state on checkers board 14 | """ 15 | 16 | def __init__(self, board: list, turn: Turn, move: list): 17 | self.board = board 18 | self.turn = turn 19 | self.move = move 20 | 21 | def change_turn(self): 22 | if self.turn == Turn.BLACK: 23 | self.turn = Turn.WHITE 24 | else: 25 | self.turn = Turn.BLACK 26 | 27 | def is_terminal_state(self): 28 | b_counter = 0 29 | w_counter = 0 30 | for i in range(63): 31 | if self.board[i] == "b" or self.board[i] == "B": 32 | b_counter += 1 33 | elif self.board[i] == "w" or self.board[i] == "W": 34 | w_counter += 1 35 | 36 | if b_counter == 0 or w_counter == 0 or len(self.get_states()) == 0: 37 | return True 38 | elif b_counter > 3 and w_counter == 1 or w_counter > 3 and b_counter == 1: 39 | return True 40 | 41 | def get_states(self): 42 | if self.turn == Turn.BLACK: 43 | player = "b" 44 | else: 45 | player = "w" 46 | 47 | states = [] 48 | for i in range(64): 49 | if self.board[i].lower() == player: 50 | get_legal_jumps(self.board, i, self.turn, states) 51 | 52 | if len(states) == 0: 53 | for i in range(64): 54 | if self.board[i].lower() == player: 55 | get_legal_moves(self.board, i, self.turn, states) 56 | 57 | return states 58 | 59 | 60 | """ 61 | ----------------------------------------------------------------------------------------------------------------- 62 | Getting all legal moves for 'get_states' function from State class 63 | ----------------------------------------------------------------------------------------------------------------- 64 | """ 65 | 66 | 67 | def get_legal_jumps(board: list, i: int, turn: Turn, states: list): 68 | 69 | if turn == Turn.BLACK: 70 | 71 | if board[i] == "B": 72 | jumps(board, i, turn, states, king_jump_steps(), 55, 64, "w") 73 | elif board[i] == "b": 74 | jumps(board, i, turn, states, black_jump_steps(), 55, 64, "w") 75 | 76 | elif turn == Turn.WHITE: 77 | 78 | if board[i] == "W": 79 | jumps(board, i, turn, states, king_jump_steps(), 0, 8, "b") 80 | elif board[i] == "w": 81 | jumps(board, i, turn, states, white_jump_steps(), 0, 8, "b") 82 | 83 | 84 | def get_legal_moves(board: list, i: int, turn: Turn, states: list): 85 | 86 | if turn == Turn.BLACK: 87 | 88 | if board[i] == "B": 89 | moves(board, i, turn, states, king_steps(), 55, 64) 90 | elif board[i] == "b": 91 | moves(board, i, turn, states, black_steps(), 55, 64) 92 | 93 | elif turn == Turn.WHITE: 94 | 95 | if board[i] == "W": 96 | moves(board, i, turn, states, king_steps(), 0, 8) 97 | elif board[i] == "w": 98 | moves(board, i, turn, states, white_steps(), 0, 8) 99 | 100 | 101 | def moves(board, i, turn, states, steps, lower_border, upper_border): 102 | """ 103 | Fill the list 'states' with legal moves 104 | """ 105 | 106 | legal_positions = non_legal_positions() 107 | for step in steps: 108 | index = step + i 109 | if index in legal_positions: 110 | continue 111 | if 63 >= index >= 0 and board[index] == '-': 112 | board_copy = deepcopy(board) 113 | board_copy[index], board_copy[i] = board_copy[i], '-' 114 | if lower_border < i < upper_border: 115 | board_copy[index] = board_copy[index].upper() 116 | s = State(board_copy, turn, [i, index]) 117 | states.append(s) 118 | 119 | 120 | def jumps(board, i, turn, states, steps, lower_border, upper_border, value): 121 | """ 122 | Fill the list 'states' with jump moves 123 | """ 124 | 125 | legal_positions = non_legal_positions() 126 | for step in steps: 127 | 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