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ai_player.py
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ai_player.py
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# !/usr/bin/python
# -*- coding: utf-8 -*-
from copy import deepcopy
import time
class AIPlayer:
"""
AI 玩家
"""
def __init__(self, color):
"""
玩家初始化
:param color: 下棋方，'X' - 黑棋，'O' - 白棋
"""
self.color = color
if self.color == 'O':
self.op_color = 'X'
else:
self.op_color = 'O'
self.second_weight = [[45, 2, 9, 9, 9, 9, 2, 45],
[2, 0, 3, 3, 3, 3, 0, 2],
[9, 3, 5, 5, 5, 5, 3, 9],
[9, 3, 5, 1, 1, 5, 3, 9],
[9, 3, 5, 1, 1, 5, 3, 9],
[9, 3, 5, 5, 5, 5, 3, 9],
[2, 0, 3, 3, 3, 3, 0, 2],
[45, 2, 9, 9, 9, 9, 2, 45]]
self.start = 0
self.base_stable_score = 10
self.corner_score = 80
self.s_corner_score = 40
self.s_line_score = 20
def get_move(self, board):
"""
根据当前棋盘状态获取最佳落子位置
:param board: 棋盘
:return: action 最佳落子位置, e.g. 'A1'
"""
if self.color == 'X':
player_name = '黑棋'
else:
player_name = '白棋'
print("请等一会，对方 {}-{} 正在思考中...".format(player_name, self.color))
virtual_board = deepcopy(board)
self.start = time.clock()
action, value = self.max_value(virtual_board, -65, 65, 0)
return action
def heuristic(self, virtual_board, flexibility, op_flexibility):
board = virtual_board._board
value = 0
s1, s2 = virtual_board.count(self.op_color), virtual_board.count(self.color)
s_sum = s1 + s2
s_diff = s1 - s2
if board[0][0] == self.color:
value += self.corner_score
for i in range(1, 8):
if board[0][i] == self.color:
value += self.base_stable_score
else:
break
for i in range(1, 8):
if board[i][0] == self.color:
value += self.base_stable_score
else:
break
else:
if board[1][1] == self.color:
value -= self.s_corner_score
if board[0][1] == self.color:
value -= self.s_line_score
if board[1][0] == self.color:
value -= self.s_line_score
if board[7][0] == self.color:
value += self.corner_score
for i in range(1, 8):
if board[7][i] == self.color:
value += self.base_stable_score
else:
break
for i in range(6, -1, -1):
if board[i][0] == self.color:
value += self.base_stable_score
else:
break
else:
if board[6][1] == self.color:
value -= self.s_corner_score
if board[7][1] == self.color:
value -= self.s_line_score
if board[6][0] == self.color:
value -= self.s_line_score
if board[0][7] == self.color:
value += self.corner_score
for i in range(6, -1, -1):
if board[0][i] == self.color:
value += self.base_stable_score
else:
break
for i in range(1, 8):
if board[i][7] == self.color:
value += self.base_stable_score
else:
break
else:
if board[1][6] == self.color:
value -= self.s_corner_score
if board[0][6] == self.color:
value -= self.s_line_score
if board[1][7] == self.color:
value -= self.s_line_score
if board[7][7] == self.color:
value += self.corner_score
for i in range(6, -1, -1):
if board[7][i] == self.color:
value -= self.base_stable_score
else:
break
for i in range(6, -1, -1):
if board[i][7] == self.color:
value += self.base_stable_score
else:
break
else:
if board[6][6] == self.color:
value -= self.s_corner_score
if board[7][6] == self.color:
value -= self.s_line_score
if board[6][7] == self.color:
value -= self.s_line_score
if board[0][0] == self.op_color:
value -= self.corner_score
for i in range(1, 8):
if board[0][i] == self.op_color:
value -= self.base_stable_score
else:
break
for i in range(1, 8):
if board[i][0] == self.op_color:
value -= self.base_stable_score
else:
break
else:
if board[1][1] == self.op_color:
value += self.s_corner_score
if board[0][1] == self.op_color:
value += self.s_line_score
if board[1][0] == self.op_color:
value += self.s_line_score
if board[7][0] == self.op_color:
value -= self.corner_score
for i in range(1, 8):
if board[7][i] == self.op_color:
value -= self.base_stable_score
else:
break
for i in range(6, -1, -1):
if board[i][0] == self.op_color:
value -= self.base_stable_score
else:
break
else:
if board[6][1] == self.op_color:
value += self.s_corner_score
if board[7][1] == self.op_color:
value += self.s_line_score
if board[6][0] == self.op_color:
value += self.s_line_score
if board[0][7] == self.op_color:
value -= self.corner_score
for i in range(6, -1, -1):
if board[0][i] == self.op_color:
value -= self.base_stable_score
else:
break
for i in range(1, 8):
if board[i][7] == self.op_color:
value -= self.base_stable_score
else:
break
else:
if board[1][6] == self.op_color:
value += self.s_corner_score
if board[0][6] == self.op_color:
value += self.s_line_score
if board[1][7] == self.op_color:
value += self.s_line_score
if board[7][7] == self.op_color:
value -= self.corner_score
for i in range(6, -1, -1):
if board[7][i] == self.op_color:
value -= self.base_stable_score
else:
break
for i in range(6, -1, -1):
if board[i][7] == self.op_color:
value -= self.base_stable_score
else:
break
else:
if board[6][6] == self.op_color:
value += self.s_corner_score
if board[7][6] == self.op_color:
value += self.s_line_score
if board[6][7] == self.op_color:
value += self.s_line_score
if flexibility == 0:
flexibility = -100
if s_sum < 46:
value += int(2.3 * flexibility) - op_flexibility
if s_sum < 30:
value += s_diff
else:
for i in range(8):
for j in range(8):
if board[i][j] == self.color:
value += self.second_weight[i][j]
elif board[i][j] == self.op_color:
value -= self.second_weight[i][j]
return value / 3000
def max_value(self, virtual_board, alpha, beta, depth):
list_x = list(virtual_board.get_legal_actions('X'))
list_o = list(virtual_board.get_legal_actions('O'))
if len(list_x) == 0 and len(list_o) == 0:
return None, virtual_board.count(self.color) - virtual_board.count(self.op_color)
v = -65
final_action = None
if self.color == 'X':
current_color = 'X'
legal_list = list_x
op_legal_list = list_o
else:
current_color = 'O'
legal_list = list_o
op_legal_list = list_x
if len(legal_list) == 0:
return self.min_value(virtual_board, alpha, beta, depth + 1)
s_sum = virtual_board.count('X') + virtual_board.count('O')
if time.clock() - self.start >= 57.5 or (depth >= 6 and s_sum < 57):
return None, self.heuristic(virtual_board, len(legal_list), len(op_legal_list))
self.list_sort(virtual_board, legal_list)
for action in legal_list:
flipped = virtual_board._move(action, current_color)
temp_min_value = self.min_value(virtual_board, alpha, beta, depth + 1)[1]
virtual_board.backpropagation(action, flipped, current_color)
if temp_min_value > v:
final_action = action
v = temp_min_value
if v >= beta:
return None, v
if v > alpha:
alpha = v
return final_action, v
def min_value(self, virtual_board, alpha, beta, depth):
list_x = list(virtual_board.get_legal_actions('X'))
list_o = list(virtual_board.get_legal_actions('O'))
if len(list_x) == 0 and len(list_o) == 0:
return None, virtual_board.count(self.color) - virtual_board.count(self.op_color)
v = 65
final_action = None
if self.color == 'X':
current_color = 'O'
legal_list = list_o
else:
current_color = 'X'
legal_list = list_x
if len(legal_list) == 0:
return self.max_value(virtual_board, alpha, beta, depth + 1)
self.list_sort(virtual_board, legal_list)
for action in legal_list:
flipped = virtual_board._move(action, current_color)
temp_max_value = self.max_value(virtual_board, alpha, beta, depth + 1)[1]
virtual_board.backpropagation(action, flipped, current_color)
if temp_max_value < v:
final_action = action
v = temp_max_value
if v <= alpha:
return None, v
if v < beta:
beta = v
return final_action, v
def list_sort(self, virtual_board, legal_list):
for i in range(len(legal_list)):
for j in range(len(legal_list) - i - 1):
x1, y1 = virtual_board.board_num(legal_list[j])
x2, y2 = virtual_board.board_num(legal_list[j + 1])
if self.second_weight[x1][y1] < self.second_weight[x2][y2]:
tmp = legal_list[j]
legal_list[j] = legal_list[j + 1]
legal_list[j + 1] = tmp