Algorithm A*算法伪码

我从维基百科找到了伪代码

function A*(start, goal)
    // The set of nodes already evaluated.
    closedSet := {}
    // The set of currently discovered nodes still to be evaluated.
    // Initially, only the start node is known.
    openSet := {start}
    // For each node, which node it can most efficiently be reached from.
    // If a node can be reached from many nodes, cameFrom will eventually contain the
    // most efficient previous step.
    cameFrom := the empty map

    // For each node, the cost of getting from the start node to that node.
    gScore := map with default value of Infinity
    // The cost of going from start to start is zero.
    gScore[start] := 0 
    // For each node, the total cost of getting from the start node to the goal
    // by passing by that node. That value is partly known, partly heuristic.
    fScore := map with default value of Infinity
    // For the first node, that value is completely heuristic.
    fScore[start] := heuristic_cost_estimate(start, goal)

    while openSet is not empty
        current := the node in openSet having the lowest fScore[] value
        if current = goal
            return reconstruct_path(cameFrom, current)

        openSet.Remove(current)
        closedSet.Add(current)
        for each neighbor of current
            if neighbor in closedSet
                continue        // Ignore the neighbor which is already evaluated.
            // The distance from start to a neighbor
            tentative_gScore := gScore[current] + dist_between(current, neighbor)
            if neighbor not in openSet  // Discover a new node
                openSet.Add(neighbor)
            else if tentative_gScore >= gScore[neighbor]
                continue        // This is not a better path.

            // This path is the best until now. Record it!
            cameFrom[neighbor] := current
            gScore[neighbor] := tentative_gScore
            fScore[neighbor] := gScore[neighbor] + heuristic_cost_estimate(neighbor, goal)

    return failure

function reconstruct_path(cameFrom, current)
    ....

但我仍然不明白什么是启发式成本估算（？
伪代码没有显示函数是什么。

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在我看来，这是另一种类似dijkstra的算法，对吗？

该函数将返回一个启发式值，用于做出决策。在A*中，它通常是当前节点和最终节点之间的最短直线距离，因此函数似乎只是简单地计算两个给定节点之间的距离（直线，不使用路径）。

启发式必须给出实际成本的下限。返回值必须小于或等于实际最小成本，否则算法无法正常工作

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满足此要求的任何估算都将有效。即使是始终返回0的最简单的选择也能起作用。但是，估计值越高，算法的性能就越好。

所以dist_between（）也是一样的？是的，是这样。正如Henry所说，它应该是低于实际值的任何值（使用路径），但是为了获得良好的性能，您应该使用节点之间的最短距离。这不是一个“错误”的结果，因为您不知道最短路径，所以您可以使用此估计来做出决策。如果没有估计（或者像Henry说的返回0），您将是盲目的，并尝试随机节点。