完成剪枝

multiagent/multiagent/multiAgents.py

@@ -74,6 +74,7 @@ class ReflexAgent(Agent):
         newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]
         #many ghosts
         "*** YOUR CODE HERE ***"
+
         GhostPos = successorGameState.getGhostPositions()
         x_pacman,y_pacman = newPos
         failedDist = min([(abs(each[0]- x_pacman) + abs(each[1]-y_pacman)) for each in GhostPos])
@@ -112,6 +113,7 @@ class ReflexAgent(Agent):
             foodScore = 0
         return successorGameState.getScore() + foodScore + ghostScore
 
+
 def scoreEvaluationFunction(currentGameState):
     """
       This default evaluation function just returns the score of the state.
@@ -146,30 +148,6 @@ class MinimaxAgent(MultiAgentSearchAgent):
     """
     Your minimax agent (question 2)
     """
-    def minimax(self,agentIndex,gameState,Depth):
-        if Depth >= self.depth * 2 or gameState.isWin() or gameState.isLose():
-            return self.evaluationFunction(gameState)
-        if agentIndex == 0:
-            MAX = float("-inf")
-            pacmanActions = gameState.getLegalActions(agentIndex)
-            for each_action in pacmanActions:
-                successor_state = gameState.generateSuccessor(agentIndex, each_action)
-                value = self.minimax(1, successor_state, Depth + 1)
-                MAX = max(MAX, value)
-            return MAX
-        else:
-            values = []
-            MIN = float("inf")
-            ghostActions = gameState.getLegalActions(agentIndex)
-            for each_action in ghostActions:
-                successor_state = gameState.generateSuccessor(agentIndex, each_action)
-                if agentIndex >= gameState.getNumAgents() - 1:
-                    values.append(self.minimax(0, successor_state, Depth + 1))
-                else:
-                    values.append(self.minimax(agentIndex + 1, successor_state, Depth))
-                value = min(values)
-                MIN = min(MIN, value)
-            return MIN
     def getAction(self, gameState):
         """
         Returns the minimax action from the current gameState using self.depth
@@ -194,7 +172,6 @@ class MinimaxAgent(MultiAgentSearchAgent):
         Returns whether or not the game state is a losing state
         """
         "*** YOUR CODE HERE ***"
-        '''
         GhostIndex = [i for i in range(1, gameState.getNumAgents())]
 
         def gameOver(state, d):
@@ -217,23 +194,14 @@ class MinimaxAgent(MultiAgentSearchAgent):
                 return self.evaluationFunction(state)
             Alpha = -10000000000000000
             for action in state.getLegalActions(0):
+                if action == 'Stop':
+                    continue
                 Alpha = max(Alpha, min_value(state.generateSuccessor(0, action), d, 1))
             return Alpha
         res = [(action, min_value(gameState.generateSuccessor(0, action), 0, 1)) for action in
                gameState.getLegalActions(0)]
         res.sort(key=lambda k: k[1])
         return res[-1][0]
-        '''
-
-        bestaction = "stop"
-        maxvalue = float('-inf')
-        for each_action in gameState.getLegalActions(0):
-            successor_state = gameState.generateSuccessor(0, each_action)
-            value = self.minimax(agentIndex=0, gameState=successor_state, Depth=0)
-            if value is not None and maxvalue < value:
-                maxvalue = value
-                bestaction = each_action
-        return bestaction
         util.raiseNotDefined()
 
 class AlphaBetaAgent(MultiAgentSearchAgent):
@@ -246,6 +214,51 @@ class AlphaBetaAgent(MultiAgentSearchAgent):
           Returns the minimax action using self.depth and self.evaluationFunction
         """
         "*** YOUR CODE HERE ***"
+        ghostIndex = [i for i in range(1,gameState.getNumAgents())]
+        def gameOver(state,depth):
+            return state.isWin() or state.isLose() or depth == self.depth
+        def min_value(state,depth,ghost,alpha,beta):
+            if gameOver(state,depth):
+                return self.evaluationFunction(state)
+            value = float('inf')
+            for action in state.getLegalActions(ghost):
+                if ghost == ghostIndex[-1]:
+                    value = min(value,max_value(state.generateSuccessor(ghost,action),depth+1,alpha,beta))
+                else:
+                    value = min(value,min_value(state.generateSuccessor(ghost,action),depth,ghost+1,alpha,beta))
+                if value < alpha:
+                    return value
+                beta = min(beta,value)
+            return value
+        def max_value(state,depth,alpha,beta):
+            if gameOver(state,depth):
+                return self.evaluationFunction(state)
+            value = float('-inf')
+            for action in state.getLegalActions(0):
+                if action == 'Stop':
+                    continue
+                value = max(value,min_value(state.generateSuccessor(0,action),depth,1,alpha,beta))
+                if value > beta:
+                    return value
+                alpha = max(value,alpha)
+            return value
+        def function(state):
+            value = float('-inf')
+            actions = None
+            alpha = float('-inf')
+            beta = float('inf')
+            for action in state.getLegalActions(0):
+                if action == 'Stop':
+                    continue
+                tmpValue = min_value(state.generateSuccessor(0,action),0,1,alpha,beta)
+                if value < tmpValue:
+                    value = tmpValue
+                    actions = action
+                if value > beta:
+                    return value
+                alpha = max(value,alpha)
+            return actions
+        return function(gameState)
         util.raiseNotDefined()
 
 class ExpectimaxAgent(MultiAgentSearchAgent):