61 lines
2.6 KiB
Python
61 lines
2.6 KiB
Python
# bustersGhostAgents.py
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# ---------------------
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# Licensing Information: You are free to use or extend these projects for
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# educational purposes provided that (1) you do not distribute or publish
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# solutions, (2) you retain this notice, and (3) you provide clear
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# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
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#
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# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
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# The core projects and autograders were primarily created by John DeNero
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# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
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# Student side autograding was added by Brad Miller, Nick Hay, and
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# Pieter Abbeel (pabbeel@cs.berkeley.edu).
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import ghostAgents
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from game import Directions
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from game import Actions
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from util import manhattanDistance
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import util
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class StationaryGhost( ghostAgents.GhostAgent ):
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def getDistribution( self, state ):
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dist = util.Counter()
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dist[Directions.STOP] = 1.0
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return dist
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class DispersingGhost( ghostAgents.GhostAgent ):
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"Chooses an action that distances the ghost from the other ghosts with probability spreadProb."
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def __init__( self, index, spreadProb=0.5):
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self.index = index
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self.spreadProb = spreadProb
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def getDistribution( self, state ):
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ghostState = state.getGhostState( self.index )
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legalActions = state.getLegalActions( self.index )
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pos = state.getGhostPosition( self.index )
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isScared = ghostState.scaredTimer > 0
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speed = 1
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if isScared: speed = 0.5
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actionVectors = [Actions.directionToVector( a, speed ) for a in legalActions]
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newPositions = [( pos[0]+a[0], pos[1]+a[1] ) for a in actionVectors]
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# get other ghost positions
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others = [i for i in range(1,state.getNumAgents()) if i != self.index]
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for a in others: assert state.getGhostState(a) != None, "Ghost position unspecified in state!"
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otherGhostPositions = [state.getGhostPosition(a) for a in others if state.getGhostPosition(a)[1] > 1]
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# for each action, get the sum of inverse squared distances to the other ghosts
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sumOfDistances = []
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for pos in newPositions:
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sumOfDistances.append( sum([(1+manhattanDistance(pos, g))**(-2) for g in otherGhostPositions]) )
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bestDistance = min(sumOfDistances)
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numBest = [bestDistance == dist for dist in sumOfDistances].count(True)
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distribution = util.Counter()
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for action, distance in zip(legalActions, sumOfDistances):
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if distance == bestDistance: distribution[action] += self.spreadProb / numBest
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distribution[action] += (1 - self.spreadProb) / len(legalActions)
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return distribution
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