为什么我的 Minimax 不能正确展开和移动?
Why is My Minimax Not Expanding and Making Moves Correctly?
我正在 Python 2.7.11 的吃豆人基本游戏中实现极小化。 Pacman 是最大化代理,一个或多个幽灵(取决于测试布局)is/are 是最小化代理。
我必须实现 minimax 以便可能 多个 最小化代理,这样它就可以创建一棵树n 层(深度)。例如,第 1 层是每个幽灵轮流将其可能移动的最终状态效用最小化,以及吃豆人轮流最大化幽灵已经最小化的东西。从图形上看,第 1 层看起来像这样:
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 return -4
然后朝那个方向移动,根据那个决定创建一个全新的极小极大树。
首先,我的实现需要一个变量和函数列表:
# 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)
有人知道为什么我的代码会这样吗?我希望有一些 Minimax/Artificial 情报专家可以找出我的问题。
提前致谢。
更新: 通过将我的 self.myDepth
值实例化为 0
而不是 1
,我已经解决了异常抛出问题。但是,我的实现的整体错误仍然存在。
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
来跟踪层数。与其在 maxValue
方法中递增深度,不如将其作为参数传递给每个函数,并且仅在传递到 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。这是我的解决方案,结果证明有效:
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!我们最终的工作多智能体 minimax 实现。
我正在 Python 2.7.11 的吃豆人基本游戏中实现极小化。 Pacman 是最大化代理,一个或多个幽灵(取决于测试布局)is/are 是最小化代理。
我必须实现 minimax 以便可能 多个 最小化代理,这样它就可以创建一棵树n 层(深度)。例如,第 1 层是每个幽灵轮流将其可能移动的最终状态效用最小化,以及吃豆人轮流最大化幽灵已经最小化的东西。从图形上看,第 1 层看起来像这样:
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 return -4
然后朝那个方向移动,根据那个决定创建一个全新的极小极大树。
首先,我的实现需要一个变量和函数列表:
# 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)
有人知道为什么我的代码会这样吗?我希望有一些 Minimax/Artificial 情报专家可以找出我的问题。 提前致谢。
更新: 通过将我的 self.myDepth
值实例化为 0
而不是 1
,我已经解决了异常抛出问题。但是,我的实现的整体错误仍然存在。
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
来跟踪层数。与其在 maxValue
方法中递增深度,不如将其作为参数传递给每个函数,并且仅在传递到 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。这是我的解决方案,结果证明有效:
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!我们最终的工作多智能体 minimax 实现。