树在人机对弈中的应用代码实现
时间: 2023-08-31 21:04:57 浏览: 43
树在人机对弈中的应用代码实现可以分为以下几个步骤:
1. 定义棋盘的状态和落子规则
```python
class Board:
def __init__(self, size=15):
self.size = size
self.states = {} # 棋盘状态
self.player = 1 # 当前玩家
def move(self, pos):
self.states[pos] = self.player
self.player = -self.player # 玩家交替
```
2. 定义树节点类和树的构建方法
```python
class TreeNode:
def __init__(self, parent=None, pos=None):
self.parent = parent
self.pos = pos
self.children = []
self.n_visits = 0 # 节点访问次数
self.Q = 0 # 节点胜率
def expand(self, board):
"""
扩展节点
"""
for pos in board.get_legal_moves():
if pos not in [child.pos for child in self.children]:
self.children.append(TreeNode(parent=self, pos=pos))
def select(self):
"""
选择子节点
"""
return max(self.children, key=lambda c: c.Q / c.n_visits + sqrt(2 * log(self.n_visits) / c.n_visits))
def update(self, result):
"""
更新节点访问次数和胜率
"""
self.n_visits += 1
self.Q += result
def build_tree(board, root, n_iter):
"""
构建蒙特卡洛树
"""
for i in range(n_iter):
node = root
simulation_board = copy.deepcopy(board)
# 选择
while node.children:
node = node.select()
simulation_board.move(node.pos)
# 扩展
node.expand(simulation_board)
# 模拟
if node.children:
node = node.select()
simulation_board.move(node.pos)
while not simulation_board.is_game_over():
pos = simulation_board.random_move()
simulation_board.move(pos)
# 回溯
while node:
node.update(simulation_board.get_result(node.parent.player))
node = node.parent
```
3. 实现蒙特卡洛树搜索算法
```python
def mcts(board, root, n_iter):
build_tree(board, root, n_iter)
return max(root.children, key=lambda c: c.n_visits).pos
```
4. 在人机对弈中使用蒙特卡洛树算法
```python
board = Board()
root = TreeNode()
while not board.is_game_over():
if board.player == 1:
pos = mcts(board, root, n_iter=1000)
board.move(pos)
root = [child for child in root.children if child.pos == pos][0]
else:
pos = human_move(board)
board.move(pos)
root = [child for child in root.children if child.pos == pos][0]
```
以上是一个简单的树在人机对弈中的应用代码实现,可以根据具体需求进行修改和扩展。