# ghostAgents.py
# ————–
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
#
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created 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 (pabbeel@cs.berkeley.edu).
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