ghostAgents.py

# ghostAgents.py
# --------------
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and Pieter 
# Abbeel in Spring 2013.
# For more info, see http://inst.eecs.berkeley.edu/~cs188/pacman/pacman.html

from game import Agent
from game import Actions
from game import Directions
import random
from util import manhattanDistance
import util

class GhostAgent( Agent ):
    def __init__( self, index ):
        self.index = index

    def getAction( self, state ):
        dist = self.getDistribution(state)
        if len(dist) == 0:
            return Directions.STOP
        else:
            return util.chooseFromDistribution( dist )

    def getDistribution(self, state):
        "Returns a Counter encoding a distribution over actions from the provided state."
        util.raiseNotDefined()

class RandomGhost( GhostAgent ):
    "A ghost that chooses a legal action uniformly at random."
    def getDistribution( self, state ):
        dist = util.Counter()
        for a in state.getLegalActions( self.index ): dist[a] = 1.0
        dist.normalize()
        return dist

class DirectionalGhost( GhostAgent ):
    "A ghost that prefers to rush Pacman, or flee when scared."
    def __init__( self, index, prob_attack=0.8, prob_scaredFlee=0.8 ):
        self.index = index
        self.prob_attack = prob_attack
        self.prob_scaredFlee = prob_scaredFlee

    def getDistribution( self, state ):
        # Read variables from state
        ghostState = state.getGhostState( self.index )
        legalActions = state.getLegalActions( self.index )
        pos = state.getGhostPosition( self.index )
        isScared = ghostState.scaredTimer > 0

        speed = 1
        if isScared: speed = 0.5

        actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
        newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
        pacmanPosition = state.getPacmanPosition()

        # Select best actions given the state
        distancesToPacman = [manhattanDistance( pos, pacmanPosition ) for pos in newPositions]
        if isScared:
            bestScore = max( distancesToPacman )
            bestProb = self.prob_scaredFlee
        else:
            bestScore = min( distancesToPacman )
            bestProb = self.prob_attack
        bestActions = [action for action, distance in zip( legalActions, distancesToPacman ) if distance == bestScore]

        # Construct distribution
        dist = util.Counter()
        for a in bestActions: dist[a] = bestProb / len(bestActions)
        for a in legalActions: dist[a] += ( 1-bestProb ) / len(legalActions)
        dist.normalize()
        return dist