python实现翻转棋游戏（othello）

Python  /  管理员 发布于 7年前   191

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 Boardfrom 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

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。