253 lines
8.5 KiB
Python
253 lines
8.5 KiB
Python
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 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, 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.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, 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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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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return self.score, nextState
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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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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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# 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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print(boardModel.summary())
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return boardModel
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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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# Get a random sample from the memory
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minibatch = random.sample(self.memory, int(self.maxSize))
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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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# 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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# 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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targets.append(target)
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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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# 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)
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continue
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if piece.colour == 1:
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num_row.append(1)
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continue
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num_row.append(2)
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num_board.append(num_row)
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return num_board
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def _encode(self, start: tuple, end: tuple) -> int:
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"""
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Encodes the move into an integer
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:param start: tuple of start position
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:param end: tuple of end position
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:return: encoded move
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"""
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start_row = start[0]
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start_col = end[0]
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end_row = start[-1]
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end_col = end[-1]
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# Concatenate into integer
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return int(str(start_row) + str(start_col) + str(end_row) + str(end_col))
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def _maxNextQ(self) -> float:
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colour = WHITE if self.colour == GREEN else GREEN
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encodedMoves = self._encodeMoves(colour, self.board)
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if len(encodedMoves) == 0:
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return -1
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paddedMoves = np.array(np.pad(encodedMoves, (0, self.maxSize - len(encodedMoves)), 'constant', constant_values=(1, 1)))
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# paddedMoves = np.reshape(paddedMoves, (32, 8, 8))
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# paddedMoves = paddedMoves / np.max(paddedMoved
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# paddedMoves = paddedMoves.reshape(32,)
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# pm = tf.convert_to_tensor(paddedMoves, dtype=tf.float32)
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# pm = tf.reshape(pm, [32])
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print(paddedMoves.shape)
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nextQValues = self.model.predict_on_batch(self._normalise(paddedMoves))
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return np.max(nextQValues)
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def _encodeMoves(self, colour: int, board: Board) -> list:
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"""
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Encodes the moves into a list encoded moves
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:param colour: colour of the player
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:param board: the board
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:return: list of encoded moves
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"""
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encodedMoves = []
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moves = board.getAllMoves(colour)
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for move in moves:
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where = self._boardDiff(board, move)
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encodedMoves.append(self._encode(where[0]+1, where[1]+1))
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return encodedMoves
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def _boardDiff(self, board, move):
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cnvState = np.array(self._convertState(board.board))
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cnvMove = np.array(self._convertState(move.board))
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diff = np.subtract(cnvMove, cnvState)
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diff = np.nonzero(diff)
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return diff
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def _normalise(self, data):
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"""
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Normalise the data
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"""
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for i in range(len(data)):
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data[i] = data[i] / np.linalg.norm(data[i])
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return data
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