python实现翻转棋游戏（othello）

更新时间：2020-07-18 23:00:01 作者：startmvc

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

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