created working reinforcement learning model
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parent
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@ -1,4 +1,4 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (draughts)" project-jdk-type="Python SDK" />
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<component name="ProjectRootManager" version="2" project-jdk-name="$USER_HOME$/anaconda3" project-jdk-type="Python SDK" />
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</project>
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90
main.py
90
main.py
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import sys
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import pygame
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from matplotlib import pyplot as plt
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from reinforcementLearning.ReinforcementLearning import ReinforcementLearning
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from utilities.constants import WIDTH, HEIGHT, SQUARE_SIZE, WHITE, GREEN
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from utilities.gameManager import GameManager
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from minimax.minimaxAlgo import MiniMax
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@ -44,15 +46,15 @@ def drawMultiLineText(surface, text, pos, font, color=pygame.Color('black')):
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y += word_height # Start on new row.
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def main():
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def main(difficulty=0):
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pygame.init()
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screen = pygame.display.set_mode((WIDTH, HEIGHT))
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menuClock = pygame.time.Clock()
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click = False
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width = screen.get_width()
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font = pygame.font.SysFont(None, 25)
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difficulty = 0
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font = pygame.font.SysFont("", 25)
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if difficulty == 0:
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while True:
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# menu
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screen.fill((128, 128, 128))
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@ -107,7 +109,7 @@ def main():
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pygame.display.update()
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menuClock.tick(60)
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if difficulty != 0:
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game(difficulty)
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@ -116,8 +118,8 @@ def rulesGUI():
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menuClock = pygame.time.Clock()
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click = False
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width = screen.get_width()
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titleFont = pygame.font.SysFont(None, 48)
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font = pygame.font.SysFont(None, 21)
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titleFont = pygame.font.SysFont("", 48)
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font = pygame.font.SysFont("", 21)
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while True:
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screen.fill((128, 128, 128))
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drawText("Rules", titleFont, (255, 255, 255), screen, width / 2, 20)
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@ -175,39 +177,65 @@ def game(difficulty):
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run = True
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clock = pygame.time.Clock()
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gameManager = GameManager(WIN, GREEN)
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while run:
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rl = ReinforcementLearning(gameManager.board.getAllMoves(WHITE), gameManager.board, WHITE, gameManager)
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mm = MiniMax()
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totalReward = []
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for i in range(2000):
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score = 0
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for j in range(200):
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clock.tick(FPS)
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reward = 0
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if gameManager.turn == WHITE:
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mm = MiniMax()
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value, newBoard = mm.AI(gameManager.getBoard(), difficulty, WHITE, gameManager)
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value, newBoard = mm.AI(difficulty, WHITE, gameManager)
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# gameManager.aiMove(newBoard)
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# reward, newBoard = rl.AI(gameManager.board)
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if newBoard is None:
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print("Cannot make move")
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continue
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gameManager.aiMove(newBoard)
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# time.sleep(0.15)
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if gameManager.turn == GREEN:
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mm = MiniMax()
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value, newBoard = mm.AI(gameManager.getBoard(), difficulty, GREEN, gameManager)
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gameManager.aiMove(newBoard)
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# time.sleep(0.15)
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if gameManager.winner() != None:
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print(gameManager.winner())
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run = False
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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run = False
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if event.type == pygame.MOUSEBUTTONDOWN:
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pos = pygame.mouse.get_pos()
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row, col = getRowColFromMouse(pos)
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# if gameManager.turn == GREEN:
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gameManager.select(row, col)
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#
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gameManager.update()
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pygame.display.update()
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if gameManager.turn == GREEN:
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value, newBoard = mm.AI(difficulty, GREEN, gameManager)
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gameManager.aiMove(newBoard)
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score += reward
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if gameManager.winner() is not None:
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print(gameManager.winner())
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break
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# for event in pygame.event.get():
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# if event.type == pygame.QUIT:
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# break
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# if event.type == pygame.MOUSEBUTTONDOWN:
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# pos = pygame.mouse.get_pos()
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# row, col = getRowColFromMouse(pos)
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# # if gameManager.turn == GREEN:
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# gameManager.select(row, col)
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gameManager.update()
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pygame.display.update()
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gameManager.reset()
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rl.resetScore()
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print("Game: ", i, " Reward: ", score)
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totalReward.append(score)
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# save model weights every 25 games
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if i % 250 == 0 and i != 0:
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rl.model.save("./modelWeights/model_" + str(i) + ".h5")
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# pygame.quit()
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rl.model.save("./modelWeights/model_final.h5")
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main()
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plt.plot([i for i in range(len(totalReward))], totalReward)
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plt.xlabel("Games")
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plt.ylabel("Reward")
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plt.show()
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main(3)
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@ -1,56 +1,38 @@
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import random
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from copy import deepcopy
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from math import inf
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from utilities.constants import GREEN, WHITE
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class MiniMax():
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class MiniMax:
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def AI(self, board, depth, maxPlayer, gameManager):
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if depth == 0 or board.winner() is not None:
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return board.scoreOfTheBoard(), board
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def AI(self, depth, maxPlayer, gameManager):
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if depth == 0 or gameManager.board.winner() is not None:
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return gameManager.board.scoreOfTheBoard(), gameManager.board
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if maxPlayer:
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if type(maxPlayer) == int:
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maxEval = -inf
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bestMove = None
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for move in self.getAllMoves(board, maxPlayer):
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evaluation = self.AI(move, depth - 1, False, gameManager)[0]
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for move in gameManager.board.getAllMoves(maxPlayer):
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evaluation = self.AI(depth - 1, False, gameManager)[0]
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maxEval = max(maxEval, evaluation)
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if maxEval > evaluation:
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bestMove = move
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if maxEval == evaluation:
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# bestMove = move
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bestMove = bestMove if random.choice([True, False]) else move
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return maxEval, bestMove
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else:
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minEval = inf
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bestMove = None
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colour = WHITE if gameManager.turn == GREEN else GREEN
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for move in self.getAllMoves(board, colour):
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evaluation = self.AI(move, depth - 1, True, gameManager)[0]
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for move in gameManager.board.getAllMoves(colour):
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evaluation = self.AI(depth - 1, True, gameManager)[0]
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minEval = min(minEval, evaluation)
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if minEval < evaluation:
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bestMove = move
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if minEval == evaluation:
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# bestMove = move
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bestMove = bestMove if random.choice([True, False]) else move
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return minEval, bestMove
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def _simulateMove(self, piece, move, board, skip):
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board.move(piece, move[0], move[1])
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if skip:
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board.remove(skip)
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return board
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def getAllMoves(self, board, colour):
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moves = []
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for piece in board.getAllPieces(colour):
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validMoves = board.getValidMoves(piece)
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for move, skip in validMoves.items():
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tempBoard = deepcopy(board)
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tempPiece = tempBoard.getPiece(piece.row, piece.col)
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newBoard = self._simulateMove(tempPiece, move, tempBoard, skip)
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moves.append(newBoard)
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return moves
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import random
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from collections import deque
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from typing import Any
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from copy import deepcopy
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import numpy as np
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import tensorflow as tf
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from tensorflow.python.keras import Sequential, regularizers
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from tensorflow.python.keras.layers import Dense
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from keras.engine.input_layer import InputLayer
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from keras.layers import BatchNormalization
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from tensorflow.python.keras import Sequential, regularizers, Input
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from tensorflow.python.keras.layers import Dense, Lambda, Dropout
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from tensorflow.python.keras.optimizer_v2.adam import Adam
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from minimax.minimaxAlgo import MiniMax
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from utilities import Board
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from utilities.constants import WHITE, GREEN
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from utilities.gameManager import GameManager
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class ReinforcementLearning():
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def __init__(self, action_space, state_space, env):
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self.action_space = action_space
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self.state_space = state_space
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self.env = env
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def __init__(self, actionSpace: list, board: Board, colour: int, gameManager: GameManager) -> None:
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"""
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Constructor for the ReinforcementLearning class
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:param actionSpace: the number of possible actions
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:param board: the game board
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"""
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self.gameManager = gameManager
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self.actionSpace = actionSpace
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self.board = board
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self.state = self.board.board
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self.colour = colour
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self.score = 0
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self.epsilon = 1
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self.gamma = .95
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self.batch_size = 64
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self.epsilon_min = .01
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self.epsilon_decay = .995
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self.learning_rate = 0.001
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self.memory = deque(maxlen=100000)
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self.model = self._buildModel()
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self.batchSize = 256
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self.maxSize = 32
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self.epsilonMin = .01
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self.epsilonDecay = .995
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self.learningRate = 0.001
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self.memory = deque(maxlen=10000000)
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self.model = self._buildMainModel()
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def AI(self, episode):
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loss = []
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def AI(self, board: Board) -> tuple:
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"""
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Learns to play the draughts game
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:return: the loss
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"""
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self.board = board
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self.state = self._convertState(self.board.board)
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self.actionSpace = self._encodeMoves(self.colour, self.board)
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if len(self.actionSpace) == 0:
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return self.score, None
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max_steps = 1000
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action = self._act()
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reward, nextState, done = self.board.step(action, self.colour)
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self.score += reward
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self.state = self._convertState(nextState.board)
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self._remember(deepcopy(self.board), action, reward, self.state, done)
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self._replay()
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for e in range(episode):
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state = self.env.reset()
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state = np.reshape(state, (1, self.state_space))
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score = 0
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for i in range(max_steps):
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action = self.act(state)
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reward, next_state, done = self.env.step(action)
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score += reward
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next_state = np.reshape(next_state, (1, self.state_space))
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self.remember(state, action, reward, next_state, done)
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state = next_state
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self.replay()
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if done:
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print("episode: {}/{}, score: {}".format(e, episode, score))
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break
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loss.append(score)
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return self.score, nextState
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def _buildModel(self):
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def _buildMainModel(self) -> Sequential:
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"""
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Build the model for the AI
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:return: the model
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"""
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# Board model
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board_model = Sequential()
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modelLayers = [
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Lambda(lambda x: tf.reshape(x, [-1, 32])),
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Dense(256, activation='relu'),
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Dropout(0.2),
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Dense(128, activation='relu'),
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Dropout(0.2),
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Dense(64, activation='relu'),
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Dropout(0.2),
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Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)),
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Dropout(0.2),
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Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.01)),
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Dropout(0.2),
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Dense(1, activation='linear', kernel_regularizer=regularizers.l2(0.01))
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]
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boardModel = Sequential(modelLayers)
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# input dimensions is 32 board position values
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board_model.add(Dense(64, activation='relu', input_dim=32))
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# boardModel.add(BatchNormalization())
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boardModel.compile(optimizer=Adam(learning_rate=0.0001), loss='mean_squared_error')
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boardModel.build(input_shape=(None, None))
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# use regularizers, to prevent fitting noisy labels
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board_model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
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board_model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.01))) # 16
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board_model.add(Dense(8, activation='relu', kernel_regularizer=regularizers.l2(0.01))) # 8
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print(boardModel.summary())
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# output isn't squashed, because it might lose information
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board_model.add(Dense(1, activation='linear', kernel_regularizer=regularizers.l2(0.01)))
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board_model.compile(optimizer='nadam', loss='binary_crossentropy')
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return boardModel
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return board_model
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def remember(self, state, action, reward, next_state, done):
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self.memory.append((state, action, reward, next_state, done))
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def replay(self):
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if len(self.memory) < self.batch_size:
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def _replay(self) -> None:
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"""
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trains the model
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:return: None (void)
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"""
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if len(self.memory) < self.batchSize:
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# Not enough data to replay and test the model
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return
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minibatch = random.sample(self.memory, self.batch_size)
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states = np.array([i[0] for i in minibatch])
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actions = np.array([i[1] for i in minibatch])
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rewards = np.array([i[2] for i in minibatch])
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next_states = np.array([i[3] for i in minibatch])
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dones = np.array([i[4] for i in minibatch])
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# Get a random sample from the memory
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minibatch = random.sample(self.memory, int(self.maxSize))
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states = np.squeeze(states)
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next_states = np.squeeze(next_states)
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# Extract states, rewards, dones
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states = [m[0] for m in minibatch]
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rewards = [m[2] for m in minibatch]
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dones = [m[4] for m in minibatch]
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targets = rewards + self.gamma * (np.amax(self.model.predict_on_batch(next_states), axis=1)) * (1 - dones)
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targets_full = self.model.predict_on_batch(states)
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# Encoded moves
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encodedMoves = []
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for state in states:
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encodedMoves.append(self._encodeMoves(self.colour, state))
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ind = np.array([i for i in range(self.batch_size)])
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targets_full[[ind], [actions]] = targets
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# Calculate targets
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targets = []
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for i, moves in enumerate(encodedMoves):
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if dones[i]:
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target = rewards[i]
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else:
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target = rewards[i] + self.gamma * self._maxNextQ()
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self.model.fit(states, targets_full, epochs=1, verbose=0)
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if self.epsilon > self.epsilon_min:
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self.epsilon *= self.epsilon_decay
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targets.append(target)
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def act(self, state):
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encodedMoves = np.array([np.pad(m, (0, self.maxSize - len(m)), 'constant', constant_values=(1, 1))
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for m in encodedMoves])
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targets = np.array(targets)
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self.model.fit(self._normalise(encodedMoves), self._normalise(targets), epochs=20)
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if self.epsilon > self.epsilonMin:
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self.epsilon *= self.epsilonDecay
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def _remember(self, state: np.array, action: int, reward: float, nextState: np.array, done: bool) -> None:
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"""
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Remembers what it has learnt
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:param state: the current state
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:param action: the action taken
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:param reward: the reward for the action
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:param nextState: the next state
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:param done: whether the game is finished
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:return: None (void)
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"""
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self.memory.append((state, action, reward, nextState, done))
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def _act(self) -> Any:
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"""
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Chooses an action based on the available moves
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:return: the action
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"""
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if np.random.rand() <= self.epsilon:
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return random.randrange(self.action_space)
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act_values = self.model.predict(state)
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return np.argmax(act_values[0])
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# choose a random action from the action spaces list
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mm = MiniMax()
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value, newBoard = mm.AI(3, self.colour, self.gameManager)
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if newBoard is None:
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return random.choice(self.actionSpace)
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where = self._boardDiff(self.board, newBoard)
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return self._encode(where[0]+1, where[1]+1)
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if len(self.actionSpace) == 1:
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return self.actionSpace[0]
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encodedMoves = np.squeeze(self.actionSpace)
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encodedMoves = np.pad(encodedMoves, (0, self.maxSize - len(encodedMoves)), 'constant', constant_values=(1, 1))
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act_values = self.model.predict(self._normalise(encodedMoves))
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return self.actionSpace[np.argmax(act_values[0])]
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def resetScore(self):
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self.score = 0
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def _convertState(self, board: list) -> list:
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"""
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Converts the board into a 2D list of numbers
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:param board: 2D list of pieces
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:return: new 2D list of numbers
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"""
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num_board = []
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for row in board:
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num_row = []
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for piece in row:
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if piece == 0:
|
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num_row.append(0)
|
||||
continue
|
||||
|
||||
if piece.colour == 1:
|
||||
num_row.append(1)
|
||||
continue
|
||||
|
||||
num_row.append(2)
|
||||
|
||||
num_board.append(num_row)
|
||||
|
||||
return num_board
|
||||
|
||||
def _encode(self, start: tuple, end: tuple) -> int:
|
||||
"""
|
||||
Encodes the move into an integer
|
||||
:param start: tuple of start position
|
||||
:param end: tuple of end position
|
||||
:return: encoded move
|
||||
"""
|
||||
start_row = start[0]
|
||||
start_col = end[0]
|
||||
|
||||
end_row = start[-1]
|
||||
end_col = end[-1]
|
||||
|
||||
# Concatenate into integer
|
||||
return int(str(start_row) + str(start_col) + str(end_row) + str(end_col))
|
||||
|
||||
def _maxNextQ(self) -> float:
|
||||
colour = WHITE if self.colour == GREEN else GREEN
|
||||
encodedMoves = self._encodeMoves(colour, self.board)
|
||||
if len(encodedMoves) == 0:
|
||||
return -1
|
||||
paddedMoves = np.array(np.pad(encodedMoves, (0, self.maxSize - len(encodedMoves)), 'constant', constant_values=(1, 1)))
|
||||
# paddedMoves = np.reshape(paddedMoves, (32, 8, 8))
|
||||
# paddedMoves = paddedMoves / np.max(paddedMoved
|
||||
# paddedMoves = paddedMoves.reshape(32,)
|
||||
# pm = tf.convert_to_tensor(paddedMoves, dtype=tf.float32)
|
||||
# pm = tf.reshape(pm, [32])
|
||||
print(paddedMoves.shape)
|
||||
nextQValues = self.model.predict_on_batch(self._normalise(paddedMoves))
|
||||
return np.max(nextQValues)
|
||||
|
||||
def _encodeMoves(self, colour: int, board: Board) -> list:
|
||||
"""
|
||||
Encodes the moves into a list encoded moves
|
||||
:param colour: colour of the player
|
||||
:param board: the board
|
||||
:return: list of encoded moves
|
||||
"""
|
||||
encodedMoves = []
|
||||
moves = board.getAllMoves(colour)
|
||||
for move in moves:
|
||||
where = self._boardDiff(board, move)
|
||||
encodedMoves.append(self._encode(where[0]+1, where[1]+1))
|
||||
return encodedMoves
|
||||
|
||||
def _boardDiff(self, board, move):
|
||||
cnvState = np.array(self._convertState(board.board))
|
||||
cnvMove = np.array(self._convertState(move.board))
|
||||
diff = np.subtract(cnvMove, cnvState)
|
||||
diff = np.nonzero(diff)
|
||||
return diff
|
||||
|
||||
def _normalise(self, data):
|
||||
"""
|
||||
Normalise the data
|
||||
"""
|
||||
for i in range(len(data)):
|
||||
data[i] = data[i] / np.linalg.norm(data[i])
|
||||
return data
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
import pygame
|
||||
|
||||
from copy import deepcopy
|
||||
from .constants import BLACK, ROWS, GREEN, SQUARE_SIZE, COLS, WHITE
|
||||
from .piece import Piece
|
||||
|
||||
@ -9,34 +9,39 @@ class Board:
|
||||
self.board = []
|
||||
self.greenLeft = self.whiteLeft = 12
|
||||
self.greenKings = self.whiteKings = 0
|
||||
self.createBoard()
|
||||
self.green = (144, 184, 59)
|
||||
self._createBoard()
|
||||
|
||||
def drawSquares(self, win):
|
||||
def _drawSquares(self, win):
|
||||
win.fill(BLACK)
|
||||
for row in range(ROWS):
|
||||
for col in range(row % 2, ROWS, 2):
|
||||
pygame.draw.rect(win, GREEN, (row * SQUARE_SIZE, col * SQUARE_SIZE, SQUARE_SIZE, SQUARE_SIZE))
|
||||
pygame.draw.rect(win, self.green, (row * SQUARE_SIZE, col * SQUARE_SIZE, SQUARE_SIZE, SQUARE_SIZE))
|
||||
|
||||
def createBoard(self):
|
||||
def _createBoard(self):
|
||||
for row in range(ROWS):
|
||||
self.board.append([])
|
||||
for col in range(COLS):
|
||||
if col % 2 == ((row + 1) % 2):
|
||||
if row < 3:
|
||||
self.board[row].append(Piece(row, col, WHITE))
|
||||
elif row > 4:
|
||||
continue
|
||||
|
||||
if row > 4:
|
||||
self.board[row].append(Piece(row, col, GREEN))
|
||||
else:
|
||||
self.board[row].append(None)
|
||||
else:
|
||||
self.board[row].append(None)
|
||||
continue
|
||||
|
||||
self.board[row].append(0)
|
||||
continue
|
||||
|
||||
self.board[row].append(0)
|
||||
|
||||
def draw(self, win):
|
||||
self.drawSquares(win)
|
||||
self._drawSquares(win)
|
||||
for row in range(ROWS):
|
||||
for col in range(COLS):
|
||||
piece = self.board[row][col]
|
||||
if piece is not None:
|
||||
if piece != 0:
|
||||
piece.draw(win)
|
||||
|
||||
def move(self, piece, row, col):
|
||||
@ -45,27 +50,49 @@ class Board:
|
||||
|
||||
if row == ROWS - 1 or row == 0:
|
||||
piece.makeKing()
|
||||
|
||||
if piece.colour == WHITE:
|
||||
self.whiteKings += 1
|
||||
else:
|
||||
|
||||
if piece.colour == GREEN:
|
||||
self.greenKings += 1
|
||||
|
||||
def remove(self, skipped):
|
||||
for piece in skipped:
|
||||
self.board[piece.row][piece.col] = None
|
||||
if piece is not None:
|
||||
self.board[piece.row][piece.col] = 0
|
||||
if piece != 0:
|
||||
if piece.colour == GREEN:
|
||||
self.greenLeft -= 1
|
||||
else:
|
||||
return
|
||||
self.whiteLeft -= 1
|
||||
|
||||
def getAllMoves(self, colour):
|
||||
moves = []
|
||||
|
||||
for piece in self.getAllPieces(colour):
|
||||
validMoves = self.getValidMoves(piece)
|
||||
for move, skip in validMoves.items():
|
||||
tempBoard = deepcopy(self)
|
||||
tempPiece = tempBoard.getPiece(piece.row, piece.col)
|
||||
newBoard = self._simulateMove(tempPiece, move, tempBoard, skip)
|
||||
moves.append(newBoard)
|
||||
return moves
|
||||
|
||||
def _simulateMove(self, piece, move, board, skip):
|
||||
board.move(piece, move[0], move[1])
|
||||
if skip:
|
||||
board.remove(skip)
|
||||
|
||||
return board
|
||||
|
||||
def getPiece(self, row, col):
|
||||
return self.board[row][col]
|
||||
|
||||
def winner(self):
|
||||
if self.greenLeft <= 0:
|
||||
return WHITE
|
||||
elif self.whiteLeft <= 0:
|
||||
|
||||
if self.whiteLeft <= 0:
|
||||
return GREEN
|
||||
|
||||
return None
|
||||
@ -76,16 +103,10 @@ class Board:
|
||||
left = piece.col - 1
|
||||
right = piece.col + 1
|
||||
row = piece.row
|
||||
if piece.colour == GREEN:
|
||||
moves.update(self._traverseLeft(row - 1, max(row - 3, -1), -1, piece.colour, left))
|
||||
moves.update(self._traverseRight(row - 1, max(row - 3, -1), -1, piece.colour, right))
|
||||
if piece.colour == WHITE:
|
||||
moves.update(self._traverseLeft(row + 1, min(row + 3, ROWS), 1, piece.colour, left))
|
||||
moves.update(self._traverseRight(row + 1, min(row + 3, ROWS), 1, piece.colour, right))
|
||||
|
||||
if piece.king:
|
||||
if piece.colour == GREEN or piece.king:
|
||||
moves.update(self._traverseLeft(row - 1, max(row - 3, -1), -1, piece.colour, left))
|
||||
moves.update(self._traverseRight(row - 1, max(row - 3, -1), -1, piece.colour, right))
|
||||
if piece.colour == WHITE or piece.king:
|
||||
moves.update(self._traverseLeft(row + 1, min(row + 3, ROWS), 1, piece.colour, left))
|
||||
moves.update(self._traverseRight(row + 1, min(row + 3, ROWS), 1, piece.colour, right))
|
||||
|
||||
@ -122,7 +143,7 @@ class Board:
|
||||
pieces = []
|
||||
for row in self.board:
|
||||
for piece in row:
|
||||
if piece is not None and piece.colour == colour:
|
||||
if piece != 0 and piece.colour == colour:
|
||||
pieces.append(piece)
|
||||
return pieces
|
||||
|
||||
@ -162,7 +183,7 @@ class Board:
|
||||
|
||||
def _traverse(self, row, col, skipped, moves, step, last, colour):
|
||||
current = self.board[row][col]
|
||||
if current is None:
|
||||
if current == 0:
|
||||
if skipped and not last:
|
||||
return None
|
||||
elif skipped:
|
||||
@ -183,3 +204,56 @@ class Board:
|
||||
else:
|
||||
last = [current]
|
||||
return last
|
||||
|
||||
def step(self, move, colour):
|
||||
start, end = self._decode(move)
|
||||
start[0] = start[0] - 1
|
||||
start[1] = start[1] - 1
|
||||
end[0] = end[0] - 1
|
||||
end[1] = end[1] - 1
|
||||
reward = 0
|
||||
done = False
|
||||
piece = self.getPiece(start[0], start[1])
|
||||
if piece == 0:
|
||||
newStart = end
|
||||
end = start
|
||||
start = newStart
|
||||
piece = self.getPiece(start[0], start[1])
|
||||
moves = self.getValidMoves(piece)
|
||||
for move, skip in moves.items():
|
||||
if tuple(end) == move:
|
||||
self._simulateMove(piece, move, self, skip)
|
||||
if len(skip) == 1:
|
||||
reward = 2
|
||||
break
|
||||
if len(skip) > 1:
|
||||
reward = 3 + len(skip) * 0.2
|
||||
break
|
||||
reward = -0.5
|
||||
break
|
||||
|
||||
if self.winner() == colour:
|
||||
done = True
|
||||
reward = 10
|
||||
return reward, self, done
|
||||
|
||||
|
||||
def _decode(self, move):
|
||||
# Split digits back out
|
||||
str_code = str(move)
|
||||
print(str_code)
|
||||
start_row = int(str_code[0])
|
||||
start_col = int(str_code[1])
|
||||
end_row = int(str_code[2])
|
||||
end_col = int(str_code[3])
|
||||
# Reconstruct positions
|
||||
start = [start_row, start_col]
|
||||
end = [end_row, end_col]
|
||||
return start, end
|
||||
|
||||
# def reset(self):
|
||||
# self.board = []
|
||||
# self.whiteLeft = self.greenLeft = 12
|
||||
# self.whiteKings = self.greenKings = 0
|
||||
# self._createBoard()
|
||||
# return self.board
|
||||
@ -6,8 +6,8 @@ SQUARE_SIZE = WIDTH // COLS
|
||||
|
||||
# RGB color
|
||||
|
||||
GREEN = (144, 184, 59)
|
||||
WHITE = (255, 255, 255)
|
||||
GREEN = 1
|
||||
WHITE = 2
|
||||
BLACK = (0, 0, 0)
|
||||
BLUE = (0, 0, 255)
|
||||
GREY = (128, 128, 128)
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
import pygame
|
||||
from utilities.board import Board
|
||||
from utilities.Board import Board
|
||||
from utilities.constants import GREEN, WHITE, BLUE, SQUARE_SIZE
|
||||
|
||||
|
||||
class GameManager:
|
||||
def __init__(self, win, colour):
|
||||
self._init(colour)
|
||||
@ -29,14 +30,14 @@ class GameManager:
|
||||
self.selected = None
|
||||
self.select(row, col)
|
||||
piece = self.board.getPiece(row, col)
|
||||
if piece is not None and piece.colour == self.turn:
|
||||
if piece != 0 and piece.colour == self.turn:
|
||||
self.selected = piece
|
||||
self.validMoves = self.board.getValidMoves(piece)
|
||||
return True
|
||||
|
||||
def _move(self, row, col):
|
||||
piece = self.board.getPiece(row, col)
|
||||
if self.selected and piece is None and (row, col) in self.validMoves:
|
||||
if self.selected and piece == 0 and (row, col) in self.validMoves:
|
||||
self.board.move(self.selected, row, col)
|
||||
skipped = self.validMoves[row, col]
|
||||
if self.validMoves[list(self.validMoves.keys())[0]]:
|
||||
@ -58,7 +59,7 @@ class GameManager:
|
||||
self.validMoves = {}
|
||||
if self.turn == GREEN:
|
||||
self.turn = WHITE
|
||||
else:
|
||||
return
|
||||
self.turn = GREEN
|
||||
|
||||
def drawValidMoves(self, moves):
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
import pygame.draw
|
||||
|
||||
from utilities.constants import SQUARE_SIZE, GREY, CROWN
|
||||
from utilities.constants import SQUARE_SIZE, GREY, CROWN, GREEN
|
||||
|
||||
|
||||
class Piece:
|
||||
@ -14,6 +14,8 @@ class Piece:
|
||||
self.calcPosition()
|
||||
self.padding = 20
|
||||
self.border = 2
|
||||
self.green = (144, 184, 59)
|
||||
self.white = (255, 255, 255)
|
||||
|
||||
def calcPosition(self):
|
||||
self.x = SQUARE_SIZE * self.col + SQUARE_SIZE // 2
|
||||
@ -25,7 +27,7 @@ class Piece:
|
||||
def draw(self, win):
|
||||
radius = SQUARE_SIZE // 2 - self.padding
|
||||
pygame.draw.circle(win, GREY, (self.x, self.y), radius + self.border)
|
||||
pygame.draw.circle(win, self.colour, (self.x, self.y), radius)
|
||||
pygame.draw.circle(win, self.green if self.colour == GREEN else self.white, (self.x, self.y), radius)
|
||||
if self.king:
|
||||
win.blit(CROWN, (self.x - CROWN.get_width() // 2, self.y - CROWN.get_height() // 2))
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user