强化学习应用于游戏Tic-Tac-Toe

Tic-Tac-Toe游戏为3*3格子里轮流下棋，一方先有3子成直线的为赢家。

参考代码如下，我只删除了几个没用的地方：

#######################################################################
# Copyright (C)                                                       #
# 2016 - 2018 Shangtong Zhang(zhangshangtong.cpp@gmail.com)           #
# 2016 Jan Hakenberg(jan.hakenberg@gmail.com)                         #
# 2016 Tian Jun(tianjun.cpp@gmail.com)                                #
# 2016 Kenta Shimada(hyperkentakun@gmail.com)                         #
# Permission given to modify the code as long as you keep this        #
# declaration at the top                                              #
#######################################################################
##https://www.cnblogs.com/pinard/p/9385570.html ##
## 强化学习（一）模型基础 ##

import numpy as np
import pickle

BOARD_ROWS = 3
BOARD_COLS = 3
BOARD_SIZE = BOARD_ROWS * BOARD_COLS

State状态类
简要描述：每个状态用自定义hash值描述，主要方法为get_all_states（运行一次得到所有状态）和next_state（下一次棋，返回新的状态）

class State:
    def __init__(self):
        # the board is represented by an n * n array,
        # 1 represents a chessman of the player who moves first,
        # -1 represents a chessman of another player
        # 0 represents an empty position
        self.data = np.zeros((BOARD_ROWS, BOARD_COLS))
        self.winner = None
        self.hash_val = None
        self.end = None

    # compute the hash value for one state, it's unique
    def hash(self):
        if self.hash_val is None:
            self.hash_val = 0
            for i in self.data.reshape(BOARD_ROWS * BOARD_COLS):
                # 即原来取值-1,0,1，现在将-1设置为2，为了hash方便
                if i == -1:
                    i = 2
                self.hash_val = self.hash_val * 3 + i
        return int(self.hash_val)

    # check whether a player has won the game, or it's a tie
    def is_end(self):
        if self.end is not None:
            return self.end
        results = []
        # check row
        for i in range(0, BOARD_ROWS):
            results.append(np.sum(self.data[i, :]))
        # check columns
        for i in range(0, BOARD_COLS):
            results.append(np.sum(self.data[:, i]))

        # check diagonals
        results.append(0)
        for i in range(0, BOARD_ROWS):
            results[-1] += self.data[i, i]
        results.append(0)
        for i in range(0, BOARD_ROWS):
            results[-1] += self.data[i, BOARD_ROWS - 1 - i]

        for result in results:
            if result == 3:
                self.winner = 1
                self.end = True
                return self.end
            if result == -3:
                self.winner = -1
                self.end = True
                return self.end

        # whether it's a tie
        sum = np.sum(np.abs(self.data))
        if sum == BOARD_ROWS * BOARD_COLS:
            self.winner = 0
            self.end = True
            return self.end

        # game is still going on
        self.end = False
        return self.end

    # @symbol: 1 or -1
    # put chessman symbol in position (i, j)
    def next_state(self, i, j, symbol):
        new_state = State()
        new_state.data = np.copy(self.data)
        new_state.data[i, j] = symbol
        return new_state

    # print the board
    def print(self):
        for i in range(0, BOARD_ROWS):
            print('-------------')
            out = '| '
            for j in range(0, BOARD_COLS):
                if self.data[i, j] == 1:
                    token = '*'
                if self.data[i, j] == 0:
                    token = '0'
                if self.data[i, j] == -1:
                    token = 'x'
                out += token + ' | '
            print(out)
        print('-------------')

def get_all_states_impl(current_state, current_symbol, all_states):
    '''
    all_states：字典，以hash值为key，value为(state,is_End)
    '''
    for i in range(0, BOARD_ROWS):
        for j in range(0, BOARD_COLS):
            if current_state.data[i][j] == 0:
                newState = current_state.next_state(i, j, current_symbol)
                newHash = newState.hash()
                if newHash not in all_states.keys():
                    isEnd = newState.is_end()
                    all_states[newHash] = (newState, isEnd)
                    #如果没结束对局，下一个选手继续下
                    if not isEnd:
                        get_all_states_impl(newState, -current_symbol, all_states)

def get_all_states():
    current_symbol = 1
    current_state = State()
    all_states = dict()
    all_states[current_state.hash()] = (current_state, current_state.is_end())
    get_all_states_impl(current_state, current_symbol, all_states)
    return all_states

# all possible board configurations
all_states = get_all_states()

裁判：监督选手轮流下棋。主要方法为alternate（轮流选手），play（监督游戏执行，play里重要的为选手的act方法，后面讲）

class Judger:
    # @player1: the player who will move first, its chessman will be 1
    # @player2: another player with a chessman -1
    # @feedback: if True, both players will receive rewards when game is end
    def __init__(self, player1, player2):
        self.p1 = player1
        self.p2 = player2
        self.p1_symbol = 1
        self.p2_symbol = -1
        self.p1.set_symbol(self.p1_symbol)
        self.p2.set_symbol(self.p2_symbol)
        self.current_state = State()

    def reset(self):
        self.p1.reset()
        self.p2.reset()

    def alternate(self):
        while True:
            yield self.p1
            yield self.p2

    # @print: if True, print each board during the game
    def play(self, print=False):
        alternator = self.alternate()
        self.reset()
        current_state=self.current_state
        self.p1.set_state(current_state)
        self.p2.set_state(current_state)
        while True:
            player = next(alternator)
            if print:
                current_state.print()
            [i, j, symbol] = player.act()
            next_state_hash = current_state.next_state(i, j, symbol).hash()
            current_state, is_end = all_states[next_state_hash]
            self.p1.set_state(current_state)
            self.p2.set_state(current_state)
            if is_end:
                if print:
                    current_state.print()
                return current_state.winner

AI选手：estimations表示不同状态下的分值，用以进行下一状态的选择，greedy区分随机行为，即随机行为不参与更新状态的分值
主要方法为set_symbol（设置对于每个选手各状态分值的初始值），backup（更新状态分值，如果下一状态分值更高，那么当前状态的分值也要提高，即将长远的结果反作用到现在），act（获取下一步坐标）

class Player:
    # @step_size: the step size to update estimations
    # @epsilon: the probability to explore
    def __init__(self, step_size=0.1, epsilon=0.1):
        self.estimations = dict()
        self.step_size = step_size
        self.epsilon = epsilon
        self.states = []
        self.greedy = []

    def reset(self):
        self.states = []
        self.greedy = []

    def set_state(self, state):
        self.states.append(state)
        self.greedy.append(True)

    def set_symbol(self, symbol):
        self.symbol = symbol
#         对状态分值初始化，最终赢了得1分，输了不得分，平局0.5分，未到终局设置为0.5分
        for hash_val in all_states.keys():
            (state, is_end) = all_states[hash_val]
            if is_end:
                if state.winner == self.symbol:
                    self.estimations[hash_val] = 1.0
                elif state.winner == 0:
                    # we need to distinguish between a tie and a lose
                    self.estimations[hash_val] = 0.5
                else:
                    self.estimations[hash_val] = 0
            else:
                self.estimations[hash_val] = 0.5

    # update value estimation
    def backup(self):
        # for debug
        # print('player trajectory')
        # for state in self.states:
        #     state.print()

        self.states = [state.hash() for state in self.states]
#         顺序更新
        for i in reversed(range(len(self.states) - 1)):
            state = self.states[i]
            td_error = self.greedy[i] * (self.estimations[self.states[i + 1]] - self.estimations[state])
            self.estimations[state] += self.step_size * td_error

    # choose an action based on the state
    def act(self):
        #取出当前（最后一个）状态
        state = self.states[-1]
        #下一步可能的状态的hash
        next_states = []
        #下一步可能的坐标
        next_positions = []
        for i in range(BOARD_ROWS):
            for j in range(BOARD_COLS):
                if state.data[i, j] == 0:
                    next_positions.append([i, j])
                    next_states.append(state.next_state(i, j, self.symbol).hash())
        #小概率随机探索
        if np.random.rand() < self.epsilon:
            action = next_positions[np.random.randint(len(next_positions))]
            action.append(self.symbol)
#             表示随机行为不参与价值更新
            self.greedy[-1] = False
            return action
        #大概率按奖励最高行动
        values = []
        for hash, pos in zip(next_states, next_positions):
            values.append((self.estimations[hash], pos))
        values.sort(key=lambda x: x[0], reverse=True)
        action = values[0][1]
        action.append(self.symbol)
        return action

    def save_policy(self):
        with open('policy_%s.bin' % ('first' if self.symbol == 1 else 'second'), 'wb') as f:
            pickle.dump(self.estimations, f)

    def load_policy(self):
        with open('policy_%s.bin' % ('first' if self.symbol == 1 else 'second'), 'rb') as f:
            self.estimations = pickle.load(f)

人类选手：act方法为自己下棋

# human interface
# input a number to put a chessman
# | q | w | e |
# | a | s | d |
# | z | x | c |
class HumanPlayer:
    def __init__(self, **kwargs):
        self.symbol = None
        self.keys = ['q', 'w', 'e', 'a', 's', 'd', 'z', 'x', 'c']
        self.state = None
        return

    def reset(self):
        return

    def set_state(self, state):
        self.state = state

    def set_symbol(self, symbol):
        self.symbol = symbol
        return

    def backup(self, _):
        return

    def act(self):
        self.state.print()
        key = input("Input your position:")
        data = self.keys.index(key)
        i = data // int(BOARD_COLS)
        j = data % BOARD_COLS
        return (i, j, self.symbol)

训练：

def train(epochs):
    player1 = Player(epsilon=0.01)
    player2 = Player(epsilon=0.01)
    judger = Judger(player1, player2)
    player1_win = 0.0
    player2_win = 0.0
    for i in range(1, epochs + 1):
        winner = judger.play(print=False)
        if winner == 1:
            player1_win += 1
        if winner == -1:
            player2_win += 1
　　　　　　# 输出2个选手的获胜概率，到最后基本是平局
        if i%100==0:
            print('Epoch %d, player 1 win %.02f, player 2 win %.02f' % (i, player1_win / i, player2_win / i))
        player1.backup()
        player2.backup()
    player1.save_policy() # 保存状态价值，其实训练获取的就是各状态分别对每个选手的价值
    player2.save_policy()

AI自测：

def compete(turns):
    # 不允许随机行为
    player1 = Player(epsilon=0)
    player2 = Player(epsilon=0)
    judger = Judger(player1, player2)
    player1.load_policy()
    player2.load_policy()
    player1_win = 0.0
    player2_win = 0.0
    for i in range(0, turns):
        winner = judger.play()
        if winner == 1:
            player1_win += 1
        if winner == -1:
            player2_win += 1
        #judger.reset()
    print('%d turns, player 1 win %.02f, player 2 win %.02f' % (turns, player1_win / turns, player2_win / turns))

人机大战：

def play():
    while True:
        player1 = HumanPlayer()
        player2 = Player(epsilon=0)
        judger = Judger(player1, player2)
        player2.load_policy()
        winner = judger.play()
        if winner == player2.symbol:
            print("You lose!")
        elif winner == player1.symbol:
            print("You win!")
        else:
            print("It is a tie!")

开始！

if __name__ == '__main__':
    train(int(1e4))
    compete(int(1e3))
    play()

训练结束后，战绩为Epoch 10000, player 1 win 0.08, player 2 win 0.03
因为此时有一定随机行为（1%）
当AI自测时，去除了随机性，结果为1000 turns, player 1 win 0.00, player 2 win 0.00
可以看到，都是平局
后面就是人机大战了，根本赢不了这个AI的。