3-2

"P3-2"
class MinimaxAgent(MultiAgentSearchAgent):
    """
      Your minimax agent
    """

    def getAction(self, gameState):
        """
          Returns the minimax action from the current gameState using self.depth
          and self.evaluationFunction.

          Here are some method calls that might be useful when implementing minimax.

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

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

          gameState.getNumAgents():
            Returns the total number of agents in the game
        
        # Choose one of the best actions
        scores = [self.evaluationFunction(gameState, action) for action in legalMoves]
        bestScore = max(scores)
        bestIndices = [index for index in range(len(scores)) if scores[index] == bestScore]
        chosenIndex = random.choice(bestIndices) # Pick randomly among the best

        return legalMoves[chosenIndex]        
        """
        
        "[Project 3] YOUR CODE HERE"
        

        """
        print gameState.getLegalActions(0)  
        print gameState.getLegalActions(1)
        print gameState.getLegalActions(2) 
        print gameState.getLegalActions(3)
        print "num::",gameState.getNumAgents()
        print "depth::",self.depth
        print range(0,self.depth)
        """
        x = input("fdfdfd")
        biggest = -10000
        actionANS = ''
        for action in gameState.getLegalActions(0):
            candidate = self.MIN_Value(1,gameState.generateSuccessor(0, action))
            print "candidata:::",candidate
            if biggest < candidate:
                biggest = candidate
                actionANS = action
                
        print actionANS
        return actionANS

        #util.raiseNotDefined()
    
    def MAX_Value(self,dep,gameState):
        biggest = -10000
        for action in gameState.getLegalActions(0):
            biggest = max(biggest,self.MIN_Value(dep,gameState.generateSuccessor(0, action)))
        return biggest
        
        
    def MIN_Value(self,dep,gameState):
        smallest = 10000
        for i in range(0,gameState.getNumAgents()-1):#012
            if i == self.depth-2:#2
                dep = dep + 1
                print gameState.getLegalActions(i+1)
                for action in gameState.getLegalActions(i+1):# i +1 == ghost 3
                    if dep == self.depth: #dep == 4
                        print self.evaluationFunction(gameState,action)
                        return self.evaluationFunction(gameState,action)
                    else:
                        smallest = min(smallest,self.MAX_Value(dep,gameState.generateSuccessor(i+1, action))) # i +1 == ghost 3           
            else:#01
                for action in gameState.getLegalActions(i+1):# i +1 == ghost 1/2
                    smallest = min(smallest,self.MIN_Value(dep,gameState.generateSuccessor(i+1, action)))# i +1 == ghost 1/2
        return smallest