fortran蒙特卡洛

好的，以下是一个基于

实现的基于

和UCT的五子棋游戏AI的代码示例：

 python import random import math  class TreeNode: def __init__(self, state, parent=None): self.state = state self.parent = parent self.children = [] self.visits = 0 self.score = 0  def UCT(node): C = 1.4 if node.visits == 0: return float('inf') return (node.score / node.visits) + C * math.sqrt(math.log(node.parent.visits) / node.visits)  def MCTS (state, iterations): root = TreeNode(state) for i in range(iterations): node = root # selection while node.children: node = max(node.children, key=UCT) # expansion if node.visits > 0: moves = node.state.get_moves() for move in moves: if move not in [c.state.last_move for c in node.children]: child_state = node.state.apply_move(move) child_node = TreeNode(child_state, node) node.children.append(child_node) # simulation sim_node = node while sim_node.children: sim_node = random.choice(sim_node.children) score = simulate(sim_node.state) # backpropagation while node: node.visits += 1 node.score += score node = node.parent return max(root.children, key=lambda c: c.visits).state.last_move  def simulate(state): player = state.get_current_player() while not state.is_terminal(): move = random.choice(state.get_moves()) state = state.apply_move(move) player = state.get_current_player() if state.get_winner() == player: return 1 elif state.get_winner() == None: return 0.5 else: return 0  class Board: def __init__(self, width=15, height=15, win_length=5): self.width = width self.height = height self.win_length = win_length self.board = [[None for y in range(height)] for x in range(width)] self.last_move = None  def get_moves(self): moves = [] for x in range(self.width): for y in range(self.height): if self.board[x][y] == None: moves.append((x, y)) return moves  def apply_move(self, move): x, y = move player = self.get_current_player() new_board = Board(self.width, self.height, self.win_length) new_board.board = [row[:] for row in self.board] new_board.board[x][y] = player new_board.last_move = move return new_board  def get_current_player(self): if sum(row.count(None) for row in self.board) % 2 == 0: return "X" else: return "O"  def is_terminal(self): if self.get_winner() != None: return True for x in range(self.width): for y in range(self.height): if self.board[x][y] == None: return False return True  def get_winner(self): for x in range(self.width): for y in range(self.height): if self.board[x][y] == None: continue if x + self.win_length <= self.width: if all(self.board[x+i][y] == self.board[x][y] for i in range(self.win_length)): return self.board[x][y] if y + self.win_length <= self.height: if all(self.board[x][y+i] == self.board[x][y] for i in range(self.win_length)): return self.board[x][y] if x + self.win_length <= self.width and y + self.win_length <= self.height: if all(self.board[x+i][y+i] == self.board[x][y] for i in range(self.win_length)): return self.board[x][y] if x + self.win_length <= self.width and y - self.win_length >= -1: if all(self.board[x+i][y-i] == self.board[x][y] for i in range(self.win_length)): return self.board[x][y] return None  def __str__(self): return " ".join(" ".join(self.board[x][y] or "-" for x in range(self.width)) for y in range(self.height))  if __name__ == "__main__": board = Board() while not board.is_terminal(): if board.get_current_player() == "X": x, y = map(int, input("Enter move (x y): ").split()) board = board.apply_move((x, y)) else: move = MCTS (board, 1000) print("AI move:", move) board = board.apply_move(move) print(board) print("Winner:", board.get_winner()) 

该代码定义了一个 `TreeNode` 类来保存节点的状态和统计信息，实现了基于UCB公式的UCT算法和基于

和UCT的五子棋AI。同时，代码还定义了一个 `Board` 类来表示五子棋游戏的状态和规则，并实现了判断胜负、获取可行落子位置等方法。在 `__main__` 函数中，代码通过交替输入玩家落子位置和调用AI选择落子位置的方式，实现了人机对战的功能。

希望这个代码对你有所帮助！

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