为什么我的Minimax无法扩展并正确移动？

Question

我正在Pacman的基本游戏中的Python 2.7.11中实现minimax。Pacman是最大化代理，而一个或多个幽灵（取决于测试布局）是最小化代理。

我必须实现minimax，以便可能有多个以上的最小化代理，并且它可以创建n层（深度）的树。例如，第1层将是每个幽灵转弯以最小化终端状态实用程序的可能移动，以及pacman进行转弯以最大化幽灵已经最小化的东西。在图形上，层1如下所示：

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

Pacman应该返回-4，然后朝该方向移动，根据该决定创建一个全新的minimax树。首先，实现我的实现所需要的变量和函数的列表：

# 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()

这是我的实现：

""" 
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)

有谁知道我的代码为什么要这样做？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。

更新：通过实例化而不是的self.myDepth值，我已经解决了引发异常的问题。但是，我的实现的整体错误仍然存​​在。01

Answer 1

我终于找到了解决我的问题的方法。主要问题是我没有depth正确引用来跟踪层数。不应增加maxValue方法内的深度，而应将其作为参数传递给每个函数，并且仅在传递到maxValue. 还有其他几个逻辑错误，例如未正确引用，以及我的方法没有返回操作numAgents这一事实。miniMax这是我的解决方案，结果是有效的：

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)

很快！我们最终的多智能体极小极大实现。