迷宫寻宝ai竞技python最优代码

迷宫寻宝AI竞技通常涉及到路径搜索算法，如深度优先搜索(DFS)、广度优先搜索(BFS)、A*搜索等。Python因其简洁的语法和丰富的库，非常适合编写这类AI程序。这里提供一个简化版的A*搜索算法用于解决迷宫寻路问题：

import heapq

def heuristic(a, b):  # 贪心启发式函数，计算曼哈顿距离或其他合适的估算
    return abs(a - b) + abs(a - b)

def astar(maze, start, end):
    frontier = [(0, start)]  # 开放列表，开始位置的F值为0
    came_from = {start: None}  # 记录每个节点的前驱
    g_score = {start: 0}      # 从起始点到当前节点的实际代价
    f_score = {start: heuristic(start, end)}  # F值，结合实际成本和启发式估计

    while frontier:
        current = heapq.heappop(frontier)  # 获取F值最小的节点

        if current == end:  # 找到目标，返回路径
            path = []
            while current is not None:
                path.append(current)
                current = came_from[current]
            return list(reversed(path))

        for neighbor in maze.get_neighbors(current):
            tentative_g_score = g_score[current] + 1  # 到邻居节点的成本加一
            if neighbor not in g_score or tentative_g_score < g_score[neighbor]:
                came_from[neighbor] = current
                g_score[neighbor] = tentative_g_score
                f_score[neighbor] = tentative_g_score + heuristic(neighbor, end)
                heapq.heappush(frontier, (f_score[neighbor], neighbor))

    return None  # 如果没有找到路径，返回None

# 使用示例：
maze = [[0, 1, 0, 0],
        [0, 0, 0, 1],
        [1, 0, 0, 0]]

path = astar(maze, (0, 0), (2, 3))  # 寻找起点(0,0)到终点(2,3)的路径