.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (draughts)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="$USER_HOME$/anaconda3" project-jdk-type="Python SDK" />
 </project>

main.py

@@ -1,7 +1,9 @@
 import sys
 
 import pygame
+from matplotlib import pyplot as plt
 
+from reinforcementLearning.ReinforcementLearning import ReinforcementLearning
 from utilities.constants import WIDTH, HEIGHT, SQUARE_SIZE, WHITE, GREEN
 from utilities.gameManager import GameManager
 from minimax.minimaxAlgo import MiniMax
@@ -44,15 +46,15 @@ def drawMultiLineText(surface, text, pos, font, color=pygame.Color('black')):
         y += word_height  # Start on new row.
 
 
-def main():
+def main(difficulty=0):
     pygame.init()
     screen = pygame.display.set_mode((WIDTH, HEIGHT))
     menuClock = pygame.time.Clock()
     click = False
     width = screen.get_width()
-    font = pygame.font.SysFont(None, 25)
+    font = pygame.font.SysFont("", 25)
-    difficulty = 0
 
+    if difficulty == 0:
         while True:
             # menu
             screen.fill((128, 128, 128))
@@ -107,7 +109,7 @@ def main():
 
             pygame.display.update()
             menuClock.tick(60)
-    if difficulty != 0:
+
     game(difficulty)
 
 
@@ -116,8 +118,8 @@ def rulesGUI():
     menuClock = pygame.time.Clock()
     click = False
     width = screen.get_width()
-    titleFont = pygame.font.SysFont(None, 48)
+    titleFont = pygame.font.SysFont("", 48)
-    font = pygame.font.SysFont(None, 21)
+    font = pygame.font.SysFont("", 21)
     while True:
         screen.fill((128, 128, 128))
         drawText("Rules", titleFont, (255, 255, 255), screen, width / 2, 20)
@@ -175,39 +177,65 @@ def game(difficulty):
     run = True
     clock = pygame.time.Clock()
     gameManager = GameManager(WIN, GREEN)
-
+    rl = ReinforcementLearning(gameManager.board.getAllMoves(WHITE), gameManager.board, WHITE, gameManager)
-    while run:
+    mm = MiniMax()
+    totalReward = []
+    for i in range(2000):
+        score = 0
+        for j in range(200):
             clock.tick(FPS)
-
+            reward = 0
             if gameManager.turn == WHITE:
                 mm = MiniMax()
-            value, newBoard = mm.AI(gameManager.getBoard(), difficulty, WHITE, gameManager)
+                value, newBoard = mm.AI(difficulty, WHITE, gameManager)
+                # gameManager.aiMove(newBoard)
+                # reward, newBoard = rl.AI(gameManager.board)
+                if newBoard is None:
+                    print("Cannot make move")
+                    continue
                 gameManager.aiMove(newBoard)
-            # time.sleep(0.15)
+                #
-
-        if gameManager.turn == GREEN:
-            mm = MiniMax()
-            value, newBoard = mm.AI(gameManager.getBoard(), difficulty, GREEN, gameManager)
-            gameManager.aiMove(newBoard)
-            # time.sleep(0.15)
-
-        if gameManager.winner() != None:
-            print(gameManager.winner())
-            run = False
-
-        for event in pygame.event.get():
-            if event.type == pygame.QUIT:
-                run = False
-            if event.type == pygame.MOUSEBUTTONDOWN:
-                pos = pygame.mouse.get_pos()
-                row, col = getRowColFromMouse(pos)
-                # if gameManager.turn == GREEN:
-                gameManager.select(row, col)
 
             gameManager.update()
             pygame.display.update()
 
+            if gameManager.turn == GREEN:
+                value, newBoard = mm.AI(difficulty, GREEN, gameManager)
+                gameManager.aiMove(newBoard)
+
+            score += reward
+
+            if gameManager.winner() is not None:
+                print(gameManager.winner())
+                break
+
+            # for event in pygame.event.get():
+            #     if event.type == pygame.QUIT:
+            #         break
+            #     if event.type == pygame.MOUSEBUTTONDOWN:
+            #         pos = pygame.mouse.get_pos()
+            #         row, col = getRowColFromMouse(pos)
+            #         # if gameManager.turn == GREEN:
+            #         gameManager.select(row, col)
+
+            gameManager.update()
+            pygame.display.update()
+
+        gameManager.reset()
+        rl.resetScore()
+        print("Game: ", i, " Reward: ", score)
+        totalReward.append(score)
+        # save model weights every 25 games
+        if i % 250 == 0 and i != 0:
+            rl.model.save("./modelWeights/model_" + str(i) + ".h5")
     # pygame.quit()
 
+    rl.model.save("./modelWeights/model_final.h5")
 
-main()
+    plt.plot([i for i in range(len(totalReward))], totalReward)
+    plt.xlabel("Games")
+    plt.ylabel("Reward")
+    plt.show()
+
+
+main(3)

minimax/minimaxAlgo.py

@@ -1,56 +1,38 @@
 import random
-from copy import deepcopy
 from math import inf
 
 from utilities.constants import GREEN, WHITE
 
 
-class MiniMax():
+class MiniMax:
 
-    def AI(self, board, depth, maxPlayer, gameManager):
+    def AI(self, depth, maxPlayer, gameManager):
-        if depth == 0 or board.winner() is not None:
+        if depth == 0 or gameManager.board.winner() is not None:
-            return board.scoreOfTheBoard(), board
+            return gameManager.board.scoreOfTheBoard(), gameManager.board
 
-        if maxPlayer:
+        if type(maxPlayer) == int:
             maxEval = -inf
             bestMove = None
-            for move in self.getAllMoves(board, maxPlayer):
+            for move in gameManager.board.getAllMoves(maxPlayer):
-                evaluation = self.AI(move, depth - 1, False, gameManager)[0]
+                evaluation = self.AI(depth - 1, False, gameManager)[0]
                 maxEval = max(maxEval, evaluation)
                 if maxEval > evaluation:
                     bestMove = move
                 if maxEval == evaluation:
+                    # bestMove = move
                     bestMove = bestMove if random.choice([True, False]) else move
             return maxEval, bestMove
         else:
             minEval = inf
             bestMove = None
             colour = WHITE if gameManager.turn == GREEN else GREEN
-            for move in self.getAllMoves(board, colour):
+            for move in gameManager.board.getAllMoves(colour):
-                evaluation = self.AI(move, depth - 1, True, gameManager)[0]
+                evaluation = self.AI(depth - 1, True, gameManager)[0]
                 minEval = min(minEval, evaluation)
                 if minEval < evaluation:
                     bestMove = move
                 if minEval == evaluation:
+                    # bestMove = move
                     bestMove = bestMove if random.choice([True, False]) else move
 
             return minEval, bestMove
-
-    def _simulateMove(self, piece, move, board, skip):
-        board.move(piece, move[0], move[1])
-        if skip:
-            board.remove(skip)
-
-        return board
-
-    def getAllMoves(self, board, colour):
-        moves = []
-
-        for piece in board.getAllPieces(colour):
-            validMoves = board.getValidMoves(piece)
-            for move, skip in validMoves.items():
-                tempBoard = deepcopy(board)
-                tempPiece = tempBoard.getPiece(piece.row, piece.col)
-                newBoard = self._simulateMove(tempPiece, move, tempBoard, skip)
-                moves.append(newBoard)
-        return moves

reinforcementLearning/ReinforcementLearning.py

@@ -1,96 +1,252 @@
 import random
 from collections import deque
+from typing import Any
+from copy import deepcopy
 
 import numpy as np
 import tensorflow as tf
-from tensorflow.python.keras import Sequential, regularizers
+from keras.engine.input_layer import InputLayer
-from tensorflow.python.keras.layers import Dense
+from keras.layers import BatchNormalization
+from tensorflow.python.keras import Sequential, regularizers, Input
+from tensorflow.python.keras.layers import Dense, Lambda, Dropout
+from tensorflow.python.keras.optimizer_v2.adam import Adam
+
+from minimax.minimaxAlgo import MiniMax
+from utilities import Board
+from utilities.constants import WHITE, GREEN
+from utilities.gameManager import GameManager
 
 
 class ReinforcementLearning():
 
-    def __init__(self, action_space, state_space, env):
+    def __init__(self, actionSpace: list, board: Board, colour: int, gameManager: GameManager) -> None:
-        self.action_space = action_space
+        """
-        self.state_space = state_space
+        Constructor for the ReinforcementLearning class
-        self.env = env
+        :param actionSpace: the number of possible actions
+        :param board: the game board
+        """
+        self.gameManager = gameManager
+        self.actionSpace = actionSpace
+        self.board = board
+        self.state = self.board.board
+        self.colour = colour
+        self.score = 0
         self.epsilon = 1
         self.gamma = .95
-        self.batch_size = 64
+        self.batchSize = 256
-        self.epsilon_min = .01
+        self.maxSize = 32
-        self.epsilon_decay = .995
+        self.epsilonMin = .01
-        self.learning_rate = 0.001
+        self.epsilonDecay = .995
-        self.memory = deque(maxlen=100000)
+        self.learningRate = 0.001
-        self.model = self._buildModel()
+        self.memory = deque(maxlen=10000000)
+        self.model = self._buildMainModel()
 
-    def AI(self, episode):
+    def AI(self, board: Board) -> tuple:
-        loss = []
+        """
+        Learns to play the draughts game
+        :return: the loss
+        """
+        self.board = board
+        self.state = self._convertState(self.board.board)
+        self.actionSpace = self._encodeMoves(self.colour, self.board)
+        if len(self.actionSpace) == 0:
+            return self.score, None
 
-        max_steps = 1000
+        action = self._act()
+        reward, nextState, done = self.board.step(action, self.colour)
+        self.score += reward
+        self.state = self._convertState(nextState.board)
+        self._remember(deepcopy(self.board), action, reward, self.state, done)
+        self._replay()
 
-        for e in range(episode):
+        return self.score, nextState
-            state = self.env.reset()
-            state = np.reshape(state, (1, self.state_space))
-            score = 0
-            for i in range(max_steps):
-                action = self.act(state)
-                reward, next_state, done = self.env.step(action)
-                score += reward
-                next_state = np.reshape(next_state, (1, self.state_space))
-                self.remember(state, action, reward, next_state, done)
-                state = next_state
-                self.replay()
-                if done:
-                    print("episode: {}/{}, score: {}".format(e, episode, score))
-                    break
-            loss.append(score)
 
-    def _buildModel(self):
+    def _buildMainModel(self) -> Sequential:
+        """
+        Build the model for the AI
+        :return: the model
+        """
         # Board model
-        board_model = Sequential()
+        modelLayers = [
+            Lambda(lambda x: tf.reshape(x, [-1, 32])),
+            Dense(256, activation='relu'),
+            Dropout(0.2),
+            Dense(128, activation='relu'),
+            Dropout(0.2),
+            Dense(64, activation='relu'),
+            Dropout(0.2),
+            Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)),
+            Dropout(0.2),
+            Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.01)),
+            Dropout(0.2),
+            Dense(1, activation='linear', kernel_regularizer=regularizers.l2(0.01))
+        ]
+        boardModel = Sequential(modelLayers)
 
-        # input dimensions is 32 board position values
+        # boardModel.add(BatchNormalization())
-        board_model.add(Dense(64, activation='relu', input_dim=32))
+        boardModel.compile(optimizer=Adam(learning_rate=0.0001), loss='mean_squared_error')
+        boardModel.build(input_shape=(None, None))
 
-        # use regularizers, to prevent fitting noisy labels
+        print(boardModel.summary())
-        board_model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
-        board_model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.01)))  # 16
-        board_model.add(Dense(8, activation='relu', kernel_regularizer=regularizers.l2(0.01)))  # 8
 
-        # output isn't squashed, because it might lose information
+        return boardModel
-        board_model.add(Dense(1, activation='linear', kernel_regularizer=regularizers.l2(0.01)))
-        board_model.compile(optimizer='nadam', loss='binary_crossentropy')
 
-        return board_model
+    def _replay(self) -> None:
-
+        """
-    def remember(self, state, action, reward, next_state, done):
+        trains the model
-        self.memory.append((state, action, reward, next_state, done))
+        :return: None (void)
-
+        """
-    def replay(self):
+        if len(self.memory) < self.batchSize:
-        if len(self.memory) < self.batch_size:
+            # Not enough data to replay and test the model
             return
 
-        minibatch = random.sample(self.memory, self.batch_size)
+        # Get a random sample from the memory
-        states = np.array([i[0] for i in minibatch])
+        minibatch = random.sample(self.memory, int(self.maxSize))
-        actions = np.array([i[1] for i in minibatch])
-        rewards = np.array([i[2] for i in minibatch])
-        next_states = np.array([i[3] for i in minibatch])
-        dones = np.array([i[4] for i in minibatch])
 
-        states = np.squeeze(states)
+        # Extract states, rewards, dones
-        next_states = np.squeeze(next_states)
+        states = [m[0] for m in minibatch]
+        rewards = [m[2] for m in minibatch]
+        dones = [m[4] for m in minibatch]
 
-        targets = rewards + self.gamma * (np.amax(self.model.predict_on_batch(next_states), axis=1)) * (1 - dones)
+        # Encoded moves
-        targets_full = self.model.predict_on_batch(states)
+        encodedMoves = []
+        for state in states:
+            encodedMoves.append(self._encodeMoves(self.colour, state))
 
-        ind = np.array([i for i in range(self.batch_size)])
+        # Calculate targets
-        targets_full[[ind], [actions]] = targets
+        targets = []
+        for i, moves in enumerate(encodedMoves):
+            if dones[i]:
+                target = rewards[i]
+            else:
+                target = rewards[i] + self.gamma * self._maxNextQ()
 
-        self.model.fit(states, targets_full, epochs=1, verbose=0)
+            targets.append(target)
-        if self.epsilon > self.epsilon_min:
-            self.epsilon *= self.epsilon_decay
 
-    def act(self, state):
+        encodedMoves = np.array([np.pad(m, (0, self.maxSize - len(m)), 'constant', constant_values=(1, 1))
+                                 for m in encodedMoves])
+        targets = np.array(targets)
+        self.model.fit(self._normalise(encodedMoves), self._normalise(targets), epochs=20)
+        if self.epsilon > self.epsilonMin:
+            self.epsilon *= self.epsilonDecay
+
+    def _remember(self, state: np.array, action: int, reward: float, nextState: np.array, done: bool) -> None:
+        """
+        Remembers what it has learnt
+        :param state: the current state
+        :param action: the action taken
+        :param reward: the reward for the action
+        :param nextState: the next state
+        :param done: whether the game is finished
+        :return: None (void)
+        """
+        self.memory.append((state, action, reward, nextState, done))
+
+    def _act(self) -> Any:
+        """
+        Chooses an action based on the available moves
+        :return: the action
+        """
         if np.random.rand() <= self.epsilon:
-            return random.randrange(self.action_space)
+            # choose a random action from the action spaces list
-        act_values = self.model.predict(state)
+            mm = MiniMax()
-        return np.argmax(act_values[0])
+            value, newBoard = mm.AI(3, self.colour, self.gameManager)
+            if newBoard is None:
+                return random.choice(self.actionSpace)
+            where = self._boardDiff(self.board, newBoard)
+            return self._encode(where[0]+1, where[1]+1)
+
+        if len(self.actionSpace) == 1:
+            return self.actionSpace[0]
+        encodedMoves = np.squeeze(self.actionSpace)
+        encodedMoves = np.pad(encodedMoves, (0, self.maxSize - len(encodedMoves)), 'constant', constant_values=(1, 1))
+        act_values = self.model.predict(self._normalise(encodedMoves))
+        return self.actionSpace[np.argmax(act_values[0])]
+
+    def resetScore(self):
+        self.score = 0
+
+    def _convertState(self, board: list) -> list:
+        """
+        Converts the board into a 2D list of numbers
+        :param board: 2D list of pieces
+        :return: new 2D list of numbers
+        """
+        num_board = []
+
+        for row in board:
+            num_row = []
+            for piece in row:
+                if piece == 0:
+                    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

run.sh

@@ -0,0 +1,2 @@
+conda activate
+python main.py

utilities/Board.py

@@ -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:
+                        continue
-                        self.board[row].append(None)
+
-                else:
+                    self.board[row].append(0)
-                    self.board[row].append(None)
+                    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
+            self.board[piece.row][piece.col] = 0
-            if piece is not None:
+            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:
+        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:
-            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:
             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

utilities/constants.py

@@ -6,8 +6,8 @@ SQUARE_SIZE = WIDTH // COLS
 
 # RGB color
 
-GREEN = (144, 184, 59)
+GREEN = 1
-WHITE = (255, 255, 255)
+WHITE = 2
 BLACK = (0, 0, 0)
 BLUE = (0, 0, 255)
 GREY = (128, 128, 128)

utilities/gameManager.py

@@ -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):

utilities/piece.py

@@ -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))