61 lines
2.6 KiB
Python
61 lines
2.6 KiB
Python
|
# bustersGhostAgents.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).
|
||
|
|
|
||
|
|
|
||
|
|
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
|