53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
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# pacmanAgents.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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from pacman import Directions
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from game import Agent
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import random
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import game
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import util
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class LeftTurnAgent(game.Agent):
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"An agent that turns left at every opportunity"
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def getAction(self, state):
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legal = state.getLegalPacmanActions()
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current = state.getPacmanState().configuration.direction
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if current == Directions.STOP: current = Directions.NORTH
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left = Directions.LEFT[current]
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if left in legal: return left
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if current in legal: return current
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if Directions.RIGHT[current] in legal: return Directions.RIGHT[current]
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if Directions.LEFT[left] in legal: return Directions.LEFT[left]
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return Directions.STOP
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class GreedyAgent(Agent):
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def __init__(self, evalFn="scoreEvaluation"):
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self.evaluationFunction = util.lookup(evalFn, globals())
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assert self.evaluationFunction != None
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def getAction(self, state):
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# Generate candidate actions
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legal = state.getLegalPacmanActions()
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if Directions.STOP in legal: legal.remove(Directions.STOP)
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successors = [(state.generateSuccessor(0, action), action) for action in legal]
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scored = [(self.evaluationFunction(state), action) for state, action in successors]
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bestScore = max(scored)[0]
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bestActions = [pair[1] for pair in scored if pair[0] == bestScore]
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return random.choice(bestActions)
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def scoreEvaluation(state):
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return state.getScore()
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