bustersGhostAgents.py (original)


# bustersGhostAgents.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).
# They have also been modified for use at the University of Puget Sound by David 
# Akers (dakers@pugetsound.edu).
# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html


import ghostAgents
from game import Directions
from game import Actions
from util import manhattanDistance
import util

class StationaryGhost( ghostAgents.GhostAgent ):
  def getDistribution( self, state ):
    dist = util.Counter()
    dist[Directions.STOP] = 1.0
    return dist
  
class DispersingGhost( ghostAgents.GhostAgent ):
  "Chooses an action that distances the ghost from the other ghosts with probability spreadProb."
  def __init__( self, index, spreadProb=0.5):
    self.index = index
    self.spreadProb = spreadProb
      
  def getDistribution( self, 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]

    # get other ghost positions
    others = [i for i in range(1,state.getNumAgents()) if i != self.index]
    for a in others: assert state.getGhostState(a) != None, "Ghost position unspecified in state!"
    otherGhostPositions = [state.getGhostPosition(a) for a in others if state.getGhostPosition(a)[1] > 1]
    
    # for each action, get the sum of inverse squared distances to the other ghosts
    sumOfDistances = []
    for pos in newPositions:
      sumOfDistances.append( sum([(1+manhattanDistance(pos, g))**(-2) for g in otherGhostPositions]) )

    bestDistance = min(sumOfDistances)
    numBest = [bestDistance == dist for dist in sumOfDistances].count(True)
    distribution = util.Counter()
    for action, distance in zip(legalActions, sumOfDistances):
      if distance == bestDistance: distribution[action] += self.spreadProb / numBest
      distribution[action] += (1 - self.spreadProb) / len(legalActions)
    return distribution