python 翻转棋（othello）

利用上一篇的框架，再写了个翻转棋的程序，为了调试minimax算法，花了两天的时间。

几点改进说明：

• 拆分成四个文件：board.py，player.py，ai.py，othello.py。使得整个结构更清晰，更通用，更易于维护。

• AI 的水平跟 minimax 的递归深度，以及评价函数有关。基于此，我把 minimax 和评价函数都放到 AI 类里面

• AIPlayer 使用了多重继承。继承了 Player 与 AI 两个类

• Game 类中把原run函数里的生成两个玩家的部分提出来，写成一个函数make_two_players，使得 run函数结构更清晰

• AI 玩家等级不要选择 0：beginer。会报错，还没调试好

board.py

'''
作者：hhh5460
时间：2017年7月1日
'''

class Board(object):
    def __init__(self):
        self.empty = '.'
        self._board = [[self.empty for _ in range(8)] for _ in range(8)] # 规格：8*8
        self._board[3][4], self._board[4][3] = 'X', 'X'
        self._board[3][3], self._board[4][4] = 'O', 'O'

    # 增加 Board[][] 索引语法
    def __getitem__(self, index):
        return self._board[index]

    # 打印棋盘
    def print_b(self):
        board = self._board
        print(' ', ' '.join(list('ABCDEFGH')))
        for i in range(8):
            print(str(i+1),' '.join(board[i]))

    # 棋局终止
    def teminate(self):
        list1 = list(self.get_legal_actions('X'))
        list2 = list(self.get_legal_actions('O'))
        return [False, True][len(list1) == 0 and len(list2) == 0]

    # 判断赢家
    def get_winner(self):
        s1, s2 = 0, 0
        for i in range(8):
            for j in range(8):
                if self._board[i][j] == 'X':
                    s1 += 1
                if self._board[i][j] == 'O':
                    s2 += 1
        if s1 > s2:
            return 0 # 黑胜
        elif s1 < s2:
            return 1 # 白胜
        elif s1 == s2:
            return 2 # 平局
    # 落子
    def _move(self, action, color):
        x,y = action
        self._board[x][y] = color

        return self._flip(action, color)

    # 翻子（返回list）
    def _flip(self, action, color):
        flipped_pos = []

        for line in self._get_lines(action):
            for i,p in enumerate(line):
                if self._board[p[0]][p[1]] == self.empty:
                    break
                elif self._board[p[0]][p[1]] == color:
                    flipped_pos.extend(line[:i])
                    break

        for p in flipped_pos:
            self._board[p[0]][p[1]] = color

        return flipped_pos

    # 撤销
    def _unmove(self, action, flipped_pos, color):
        self._board[action[0]][action[1]] = self.empty

        uncolor = ['X', 'O'][color=='X']
        for p in flipped_pos:
            self._board[p[0]][p[1]] = uncolor

    # 生成8个方向的下标数组，方便后续操作
    def _get_lines(self, action):
        '''说明：刚开始我是用一维棋盘来考虑的，后来改为二维棋盘。偷懒，不想推倒重来，简单地修改了一下'''
        board_coord = [(i,j) for i in range(8) for j in range(8)] # 棋盘坐标

        r,c = action
        ix = r*8 + c
        r, c = ix//8, ix%8
        left = board_coord[r*8:ix] # 要反转
        right = board_coord[ix+1:(r+1)*8]
        top = board_coord[c:ix:8] # 要反转
        bottom = board_coord[ix+8:8*8:8]

        if r <= c:
            lefttop = board_coord[c-r:ix:9] # 要反转
            rightbottom = board_coord[ix+9:(7-(c-r))*8+7+1:9]
        else:
            lefttop = board_coord[(r-c)*8:ix:9] # 要反转
            rightbottom = board_coord[ix+9:7*8+(7-(c-r))+1:9]

        if r+c<=7:
            leftbottom = board_coord[ix+7:(r+c)*8:7]
            righttop = board_coord[r+c:ix:7] # 要反转
        else:
            leftbottom = board_coord[ix+7:7*8+(r+c)-7+1:7]
            righttop = board_coord[((r+c)-7)*8+7:ix:7] # 要反转

        # 有四个要反转，方便判断
        left.reverse()
        top.reverse()
        lefttop.reverse()
        righttop.reverse()
        lines = [left, top, lefttop, righttop, right, bottom, leftbottom, rightbottom]
        return lines

    # 检测，位置是否有子可翻
    def _can_fliped(self, action, color):
        flipped_pos = []

        for line in self._get_lines(action):
            for i,p in enumerate(line):
                if self._board[p[0]][p[1]] == self.empty:
                    break
                elif self._board[p[0]][p[1]] == color:
                    flipped_pos.extend(line[:i])
                    break
        return [False, True][len(flipped_pos) > 0]

    # 合法走法
    def get_legal_actions(self, color):
        uncolor = ['X', 'O'][color=='X']
        uncolor_near_points = [] # 反色邻近的空位

        board = self._board
        for i in range(8):
            for j in range(8):
                if board[i][j] == uncolor:
                    for dx,dy in [(-1,0),(-1,1),(0,1),(1,1),(1,0),(1,-1),(0,-1)]:
                        x, y = i+dx, j+dy
                        if 0 <= x <=7 and 0 <= y <=7 and board[x][y] == self.empty and (x, y) not in uncolor_near_points:
                            uncolor_near_points.append((x, y))
        for p in uncolor_near_points:
            if self._can_fliped(p, color):
                yield p

# 测试
if __name__ == '__main__':
    board = Board()
    board.print_b()
    print(list(board.get_legal_actions('X')))

player.py

from ai import AI

'''
作者：hhh5460
时间：2017年7月1日
'''

# 玩家
class Player(object):
    def __init__(self, color):
        self.color = color

    # 思考
    def think(self, board):
        pass

    # 落子
    def move(self, board, action):
        flipped_pos = board._move(action, self.color)
        return flipped_pos

    # 悔子
    def unmove(self, board, action, flipped_pos):
        board._unmove(action, flipped_pos, self.color)

# 人类玩家
class HumanPlayer(Player):
    def __init__(self, color):
        super().__init__(color)

    def think(self, board):
        while True:
            action = input("Turn to '{}'. \nPlease input a point.(such as 'A1'): ".format(self.color)) # A1~H8
            r, c = action[1], action[0].upper()
            if  r in '12345678' and c in 'ABCDEFGH': # 合法性检查1
                x, y = '12345678'.index(r), 'ABCDEFGH'.index(c)
                if (x,y) in board.get_legal_actions(self.color):  # 合法性检查2
                    return x, y

# 电脑玩家（多重继承）
class AIPlayer(Player, AI):

    def __init__(self, color, level_ix=0):
        super().__init__(color)              # init Player
        super(Player, self).__init__(level_ix)  # init AI

    def think(self, board):
        print("Turn to '{}'. \nPlease wait a moment. AI is thinking...".format(self.color))
        uncolor = ['X','O'][self.color=='X']
        opfor = AIPlayer(uncolor) # 假想敌，陪练
        action = self.brain(board, opfor, 4)
        return action

ai.py

import random

'''
作者：hhh5460
时间：2017年7月1日
'''

class AI(object):
    '''
    三个水平等级：初级（beginner）、中级（intermediate）、高级（advanced）
    '''
    def __init__(self, level_ix =0):
        # 玩家等级
        self.level = ['beginner','intermediate','advanced'][level_ix]
        # 棋盘位置权重，参考：https://github.com/k-time/ai-minimax-agent/blob/master/ksx2101.py
        self.board_weights = [
            [120, -20,  20,   5,   5,  20, -20, 120],
            [-20, -40,  -5,  -5,  -5,  -5, -40, -20],
            [ 20,  -5,  15,   3,   3,  15,  -5,  20],
            [  5,  -5,   3,   3,   3,   3,  -5,   5],
            [  5,  -5,   3,   3,   3,   3,  -5,   5],
            [ 20,  -5,  15,   3,   3,  15,  -5,  20],
            [-20, -40,  -5,  -5,  -5,  -5, -40, -20],
            [120, -20,  20,   5,   5,  20, -20, 120]
        ]

    # 评估函数（仅根据棋盘位置权重）
    def evaluate(self, board, color):
        uncolor = ['X','O'][color=='X']
        score = 0
        for i in range(8):
            for j in range(8):
                if board[i][j] == color:
                    score += self.board_weights[i][j]
                elif board[i][j] == uncolor:
                    score -= self.board_weights[i][j]
        return score

    # AI的大脑
    def brain(self, board, opponent, depth):
        if self.level == 'beginer':         # 初级水平
            _, action = self.randomchoice(board)
        elif self.level == 'intermediate':  # 中级水平
            _, action = self.minimax(board, opponent, depth)
        elif self.level == 'advanced':      # 高级水平
            _, action = self.minimax_alpha_beta(board, opponent, depth)
        assert action is not None, 'action is None'
        return action

    # 随机选（从合法走法列表中随机选）
    def randomchoice(self, board):
        color = self.color
        action_list = list(board.get_legal_actions(color))
        return None, random.choice(action_list)

    # 极大极小算法，限制深度
    def minimax(self, board, opfor, depth=4): # 其中 opfor 是假想敌、陪练
        '''参考：https://github.com/k-time/ai-minimax-agent/blob/master/ksx2101.py'''
        color = self.color

        if depth == 0:
            return self.evaluate(board, color), None

        action_list = list(board.get_legal_actions(color))
        if not action_list:
            return self.evaluate(board, color), None

        best_score = -100000
        best_action = None

        for action in action_list:
            flipped_pos = self.move(board, action) # 落子
            score, _ = opfor.minimax(board, self, depth-1) # 深度优先，轮到陪练
            self.unmove(board, action, flipped_pos) # 回溯

            score = -score
            if score > best_score:
                best_score = score
                best_action = action

        return best_score, best_action

    # 极大极小算法，带alpha-beta剪枝
    def minimax_alpha_beta(self, board, opfor, depth=8, my_best=-float('inf'), opp_best=float('inf')):
        '''参考：https://github.com/k-time/ai-minimax-agent/blob/master/ksx2101.py'''
        color = self.color

        if depth == 0:
            return self.evaluate(board, color), None

        action_list = list(board.get_legal_actions(color))
        if not action_list:
            return self.evaluate(board, color), None

        best_score = my_best
        best_action = None

        for action in action_list:
            flipped_pos = self.move(board, action)  # 落子
            score, _ = opfor.minimax_alpha_beta(board, self, depth-1, -opp_best, -best_score) # 深度优先，轮到陪练
            self.unmove(board, action, flipped_pos) # 回溯

            score = -score
            if score > best_score:
                best_score = score
                best_action = action

            if best_score > opp_best:
                break

        return best_score, best_action

othello.py

from board import Board
from player import HumanPlayer, AIPlayer

'''
作者：hhh5460
时间：2017年7月1日
'''

# 游戏
class Game(object):
    def __init__(self):
        self.board = Board()
        self.current_player = None

    # 生成两个玩家
    def make_two_players(self):
        ps = input("Please select two player's type:\n\t0.Human\n\t1.AI\nSuch as:0 0\n:")
        p1, p2 = [int(p) for p in ps.split(' ')]
        if p1 == 1 or p2 == 1: # 至少有一个AI玩家
            level_ix = int(input("Please select the level of AI player.\n\t0: beginner\n\t1: intermediate\n\t2: advanced\n:"))
            if p1 == 0:
                player1 = HumanPlayer('X')
                player2 = AIPlayer('O', level_ix)
            elif p2 == 0:
                player1 = AIPlayer('X', level_ix)
                player2 = HumanPlayer('O')
            else:
                player1 = AIPlayer('X', level_ix)
                player2 = AIPlayer('O', level_ix)
        else:
            player1, player2 = HumanPlayer('X'), HumanPlayer('O') # 先手执X，后手执O

        return player1, player2

    # 切换玩家（游戏过程中）
    def switch_player(self, player1, player2):
        if self.current_player is None:
            return player1
        else:
            return [player1, player2][self.current_player == player1]

    # 打印赢家
    def print_winner(self, winner): # winner in [0,1,2]
        print(['Winner is player1','Winner is player2','Draw'][winner])

    # 运行游戏
    def run(self):
        # 生成两个玩家
        player1, player2 = self.make_two_players()

        # 游戏开始
        print('\nGame start!\n')
        self.board.print_b() # 显示棋盘
        while True:
            self.current_player = self.switch_player(player1, player2) # 切换当前玩家

            action = self.current_player.think(self.board) # 当前玩家对棋盘进行思考后，得到招法

            if action is not None:
                self.current_player.move(self.board, action)   # 当前玩家执行招法，改变棋盘

            self.board.print_b() # 显示当前棋盘

            if self.board.teminate(): # 根据当前棋盘，判断棋局是否终止
                winner = self.board.get_winner() # 得到赢家 0,1,2
                break

        self.print_winner(winner)
        print('Game over!')

        self.board.print_history()

if __name__ == '__main__':
    Game().run()

效果图