python实现翻转棋游戏（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

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