You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
82 lines
3.0 KiB
82 lines
3.0 KiB
# 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
|