为什么我的Minimax没有扩展并且正确地进行移动？-Java 学习之路

我在Pacman的基本游戏中在Python 2.7.11中实现了minimax . Pacman是最大化剂，并且一个或多个鬼（取决于测试布局）是最小化剂 .

我必须实现minimax，以便可能有最小化代理，以便它可以创建 n plies （深度）的树 . 例如，Ply 1将是每个幽灵轮流最小化其可能移动的终端状态效用，以及pacman轮流最大化鬼魂已经最小化的内容 . 从图形上看，ply 1看起来像这样：

Ply 1 depth of minimax

如果我们将以下任意实用程序分配给绿色终端状态（从左到右）：

-10, 5, 8, 4, -4, 20, -7, 17

吃 beans 子应该返回 -4 然后向那个方向移动，根据该决定创建一个全新的极小极大树 . 首先，我的实现所需的变量和函数列表是有意义的：

# Stores everything about the current state of the game
gameState

# A globally defined depth that varies depending on the test cases.
#     It could be as little as 1 or arbitrarily large
self.depth

# A locally defined depth that keeps track of how many plies deep I've gone in the tree
self.myDepth

# A function that assigns a numeric value as a utility for the current state
#     How this is calculated is moot
self.evaluationFunction(gameState)

# Returns a list of legal actions for an agent
#     agentIndex = 0 means Pacman, ghosts are >= 1
gameState.getLegalActions(agentIndex)

# Returns the successor game state after an agent takes an action
gameState.generateSuccessor(agentIndex, action)

# Returns the total number of agents in the game
gameState.getNumAgents()

# Returns whether or not the game state is a winning (terminal) state
gameState.isWin()

# Returns whether or not the game state is a losing (terminal) state
gameState.isLose()

This is my implementation:

""" 
getAction takes a gameState and returns the optimal move for pacman,
assuming that the ghosts are optimal at minimizing his possibilities
"""
def getAction(self, gameState):
    self.myDepth = 0

    def miniMax(gameState):
        if gameState.isWin() or gameState.isLose() or self.myDepth == self.depth:
            return self.evaluationFunction(gameState)

        numAgents = gameState.getNumAgents()
        for i in range(0, numAgents, 1):
            legalMoves = gameState.getLegalActions(i)
            successors = [gameState.generateSuccessor(j, legalMoves[j]) for j, move 
                                                           in enumerate(legalMoves)]
            for successor in successors:
                if i == 0:
                    return maxValue(successor, i)
                else:
                    return minValue(successor, i)

    def minValue(gameState, agentIndex):
        minUtility = float('inf')
        legalMoves = gameState.getLegalActions(agentIndex)
        succesors = [gameState.generateSuccessor(i, legalMoves[i]) for i, move 
                                                      in enumerate(legalMoves)]
        for successor in successors:
            minUtility = min(minUtility, miniMax(successor))

        return minUtility

    def maxValue(gameState, agentIndex)
        self.myDepth += 1
        maxUtility = float('-inf')
        legalMoves = gameState.getLegalActions(agentIndex)
        successors = [gameState.generateSuccessor(i, legalMoves[i]) for i, move
                                                       in enumerate(legalMoves)]
        for successor in successors:
            maxUtility = max(maxUtility, miniMax(successor))

        return maxUtility

    return miniMax(gameState)

有没有人有任何想法为什么我的代码这样做？我希望有一些Minimax /人工智能专家可以识别我的问题 . 提前致谢 .

UPDATE: 通过将我的 self.myDepth 值实例化为 0 而不是 1 ，我已经照射了异常抛出问题 . 但是，我的实现的整体不正确性仍然存在 .

1 回答

我终于找到了解决问题的方法 . 主要问题是我没有正确引用 depth 以跟踪层 . 它应该作为参数传递给每个函数，而不是在 maxValue 方法中递增深度，而只是在传递给 maxValue 时递增 . 还有其他一些逻辑错误，例如未正确引用 numAgents ，以及我的 miniMax 方法未返回操作的事实 . 这是我的解决方案，结果证明是有效的：

def getAction(self, gameState):

    self.numAgents = gameState.getNumAgents()
    self.myDepth = 0
    self.action = Direction.STOP # Imported from a class that defines 5 directions

    def miniMax(gameState, index, depth, action):
        maxU = float('-inf')
        legalMoves = gameState.getLegalActions(index)
        for move in legalMoves:
            tempU = maxU
            successor = gameState.generateSuccessor(index, move)
            maxU = minValue(successor, index + 1, depth)
            if maxU > tempU:
                action = move
        return action

    def maxValue(gameState, index, depth):
        if gameState.isWin() or gameState.isLose() or depth == self.depth:
            return self.evaluationFunction(gameState)

        index %= (self.numAgents - 1)
        maxU = float('-inf')
        legalMoves = gameState.getLegalActions(index)
        for move in legalMoves:
            successor = gameState.generateSuccessor(index, move)
            maxU = max(maxU, minValue(successor, index + 1, depth)
        return maxU

    def minValue(gameState, index, depth):
        if gameState.isWin() or gameState.isLose() or depth == self.depth:
            return self.evaluationFunction(gameState)

        minU = float('inf')
        legalMoves = gameState.getLegalActions(index)
        if index + 1 == self.numAgents:
            for move in legalMoves:
                successor = gameState.generateSuccessor(index, move)
                # Where depth is increased
                minU = min(minU, maxValue(successor, index, depth + 1)
        else:
            for move in legalMoves:
                successor = gameState.generateSuccessor(index, move)
                minU = min(minU, minValue(successor, index + 1, depth)
        return minU

    return miniMax(gameState, self.index, self.myDepth, self.action)

并且presto！我们的最终工作多代理minimax实现 .

回复于 2024-04-20T16:16:21+08:00

为什么我的Minimax没有扩展并且正确地进行移动？

1 回答

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