示例一个高级五子棋AI代码
时间: 2024-02-05 19:39:17 浏览: 38
以下是一个基于AlphaZero算法的高级五子棋AI代码,使用Python语言编写:
```
import numpy as np
from collections import defaultdict
class AlphaZero:
def __init__(self, board_size=15, n_in_row=5, n_playout=1000, c_puct=5, lbd=0.5):
self.board_size = board_size
self.n_in_row = n_in_row
self.n_playout = n_playout
self.c_puct = c_puct
self.lbd = lbd
self.Qsa = defaultdict(lambda: 0) # Q value of state-action pairs
self.Nsa = defaultdict(lambda: 0) # visit count of state-action pairs
self.Ns = defaultdict(lambda: 0) # visit count of states
self.Ps = {} # prior probability of actions
self.Es = {} # end status of states (win / lose / tie)
def get_action(self, board, temp=1e-3):
for n in range(self.n_playout):
board_copy = np.copy(board)
self.simulate(board_copy)
s = self.get_state(board)
Ns = [self.Nsa[(s, a)] for a in self.get_legal_actions(board)]
if temp == 0:
a = np.argmax(Ns)
return self.get_move(a)
else:
Ns = [n**(1/temp) for n in Ns]
Ns /= sum(Ns)
a = np.random.choice(len(Ns), p=Ns)
return self.get_move(a)
def simulate(self, board):
actions = self.get_legal_actions(board)
if not actions:
return -1
s = self.get_state(board)
if s not in self.Es:
self.Es[s] = self.get_end_status(board)
if self.Es[s] != 0:
return -self.Es[s]
max_u, best_a = -1e9, -1
for a in actions:
u = self.get_value(board, a)
if u > max_u:
max_u = u
best_a = a
next_board = self.get_next_board(board, best_a)
v = self.simulate(next_board)
u = -v
self.Nsa[(s, best_a)] += 1
self.Ns[s] += 1
self.Qsa[(s, best_a)] += (u - self.Qsa[(s, best_a)]) / self.Nsa[(s, best_a)]
return v
def get_legal_actions(self, board):
return [(i, j) for i in range(self.board_size) for j in range(self.board_size) if board[i][j] == 0]
def get_state(self, board):
return str(board.reshape(self.board_size**2))
def get_end_status(self, board):
for i in range(self.board_size):
for j in range(self.board_size):
if board[i][j] == 0:
continue
for di, dj in [(0, 1), (1, 0), (1, 1), (1, -1)]:
if self.check_win(board, i, j, di, dj):
if board[i][j] == 1:
return 1
else:
return -1
if len(self.get_legal_actions(board)) == 0:
return 0
else:
return None
def check_win(self, board, i, j, di, dj):
for k in range(self.n_in_row - 1):
if i + (k+1)*di < 0 or i + (k+1)*di >= self.board_size or j + (k+1)*dj < 0 or j + (k+1)*dj >= self.board_size or board[i+di*(k+1)][j+dj*(k+1)] != board[i][j]:
return False
return True
def get_value(self, board, action):
s = self.get_state(board)
if s not in self.Ps:
self.Ps[s], v = self.policy_value(board)
return v
if (s, action) in self.Qsa:
u = self.Qsa[(s, action)] + self.c_puct * self.Ps[s][self.get_index(action)]
return u
else:
return self.c_puct * self.Ps[s][self.get_index(action)]
def policy_value(self, board):
state = self.get_state(board)
legal_actions = self.get_legal_actions(board)
policy = np.zeros(self.board_size**2)
for a in legal_actions:
policy[self.get_index(a)] = 1
policy /= sum(policy)
v = self.lbd * self.get_net_value(board)
return policy, v
def get_net_value(self, board):
pass # 基于深度学习的神经网络估值
def get_next_board(self, board, action):
next_board = np.copy(board)
next_board[action[0]][action[1]] = 1
return next_board
def get_move(self, index):
return (index // self.board_size, index % self.board_size)
def get_index(self, action):
return action[0] * self.board_size + action[1]
```
这个AlphaZero实现包括以下功能:
- `get_action(board, temp=1e-3)`:返回在当前棋盘状态下最优的落子位置
- `simulate(board)`:模拟一次随机搜索过程,并更新Q值和N值
- `get_legal_actions(board)`:返回当前状态下所有合法的落子位置
- `get_state(board)`:返回当前状态的哈希值,用于查找访问次数、Q值、P值等
- `get_end_status(board)`:返回当前状态的胜负情况(1表示先手胜利,-1表示后手胜利,0表示平局,None表示未结束)
- `get_value(board, action)`:返回在当前状态下执行指定动作的价值估计
- `policy_value(board)`:返回当前状态的策略分布和价值估计,用于训练神经网络
- `get_net_value(board)`:基于深度学习的神经网络估值,需要自己实现
- `get_next_board(board, action)`:返回在当前状态下执行指定动作后的新状态
- `get_move(index)`:将状态哈希值中的索引转换为棋盘坐标
- `get_index(action)`:将棋盘坐标转换为状态哈希值中的索引