计算机博弈大赛点格棋的源代码

以下是一个使用 Monte Carlo Tree Search (MCTS) 算法的 Python 代码示例，用于在点格棋游戏中进行自动游戏。

import random

class Node:
    def __init__(self, state, parent=None):
        self.state = state
        self.parent = parent
        self.children = []
        self.visits = 0
        self.score = 0

    def add_child(self, child_state):
        child = Node(child_state, parent=self)
        self.children.append(child)
        return child

    def update(self, score):
        self.visits += 1
        self.score += score

    def fully_expanded(self):
        return len(self.children) == len(self.state.get_legal_moves())

    def best_child(self, c_param=1.4):
        choices_weights = [(c.score / c.visits) + c_param * math.sqrt((2 * math.log(self.visits) / c.visits)) for c in self.children]
        return self.children[choices_weights.index(max(choices_weights))]

    def __repr__(self):
        return f"<Node: {self.state}>"

class DotsAndBoxes:
    def __init__(self, size=3, players=("X", "O")):
        self.size = size
        self.board = [[None for _ in range(size+1)] for _ in range(size+1)]
        self.players = players
        self.current_player = players[0]

    def get_legal_moves(self):
        moves = []
        for i in range(self.size+1):
            for j in range(self.size+1):
                if self.board[i][j] is None:
                    moves.append((i, j))
        return moves

    def get_winner(self):
        x_count = 0
        o_count = 0
        for i in range(self.size):
            for j in range(self.size):
                if self.board[i][j] == self.players[0]:
                    x_count += 1
                elif self.board[i][j] == self.players[1]:
                    o_count += 1
        if x_count == o_count:
            return None
        elif x_count > o_count:
            return self.players[0]
        else:
            return self.players[1]

    def is_game_over(self):
        for i in range(self.size+1):
            for j in range(self.size+1):
                if self.board[i][j] is None:
                    return False
        return True

    def is_valid_move(self, move):
        if self.board[move[0]][move[1]] is not None:
            return False
        if move[0] == 0 or move[0] == self.size or move[1] == 0 or move[1] == self.size:
            return True
        sides = [(-1, 0), (0, -1), (1, 0), (0, 1)]
        for s in sides:
            if self.board[move[0]+s[0]][move[1]+s[1]] is None:
                return False
        return True

    def apply_move(self, move):
        self.board[move[0]][move[1]] = self.current_player
        if self.is_box(move):
            self.board[move[0]][move[1]] = self.current_player
            if self.current_player == self.players[0]:
                self.current_player = self.players[1]
            else:
                self.current_player = self.players[0]

    def is_box(self, move):
        sides = [(-1, 0), (0, -1), (1, 0), (0, 1)]
        for s in sides:
            if self.board[move[0]+s[0]][move[1]+s[1]] is None:
                return False
        return True

    def get_score(self):
        score = 0
        for i in range(self.size):
            for j in range(self.size):
                if self.is_box((i, j)):
                    if self.board[i][j] == self.players[0]:
                        score += 1
                    elif self.board[i][j] == self.players[1]:
                        score -= 1
        return score

    def __repr__(self):
        rows = []
        for i in range(self.size+1):
            row = []
            for j in range(self.size+1):
                if self.board[i][j] is None:
                    row.append(".")
                else:
                    row.append(self.board[i][j])
            rows.append(" ".join(row))
        return "\n".join(rows)

class DotsAndBoxesAI:
    def __init__(self, game, player, sims_per_turn=100):
        self.game = game
        self.player = player
        self.sims_per_turn = sims_per_turn

    def get_move(self):
        root = Node(self.game)
        for _ in range(self.sims_per_turn):
            node = root
            state = self.game
            while node.fully_expanded() and not node.state.is_game_over():
                node = node.best_child()
                state.apply_move(random.choice(node.state.get_legal_moves()))
            if not node.fully_expanded() and not node.state.is_game_over():
                legal_moves = node.state.get_legal_moves()
                move = random.choice([m for m in legal_moves if m not in [c.state for c in node.children]])
                state.apply_move(move)
                node = node.add_child(state)
            while not state.is_game_over():
                state.apply_move(random.choice(state.get_legal_moves()))
            while node is not None:
                node.update(state.get_score())
                node = node.parent
        best_move = None
        best_score = float("-inf")
        for child in root.children:
            if child.visits > best_score:
                best_move = child.state.last_move
                best_score = child.score
        return best_move

    def __repr__(self):
        return f"<DotsAndBoxesAI: {self.player}>"

这个代码实现了一个基于 MCTS 算法的自动点棋游戏玩家。玩家可以设置每个回合进行模拟的次数，以平衡计算时间和人类玩家的回合间隔。