[SOLVED] 代写 game html AI pacmanAgents.py (original)

pacmanAgents.py (original)

# pacmanAgents.py
# —————
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero ([email protected]) and Dan Klein ([email protected]).
# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html

from pacman import Directions
from game import Agent
import random
import game
import util

class LeftTurnAgent(game.Agent):
“An agent that turns left at every opportunity”

def getAction(self, state):
legal = state.getLegalPacmanActions()
current = state.getPacmanState().configuration.direction
if current == Directions.STOP: current = Directions.NORTH
left = Directions.LEFT[current]
if left in legal: return left
if current in legal: return current
if Directions.RIGHT[current] in legal: return Directions.RIGHT[current]
if Directions.LEFT[left] in legal: return Directions.LEFT[left]
return Directions.STOP

class GreedyAgent(Agent):
def __init__(self, evalFn=”scoreEvaluation”):
self.evaluationFunction = util.lookup(evalFn, globals())
assert self.evaluationFunction != None

def getAction(self, state):
# Generate candidate actions
legal = state.getLegalPacmanActions()
if Directions.STOP in legal: legal.remove(Directions.STOP)

successors = [(state.generateSuccessor(0, action), action) for action in legal]
scored = [(self.evaluationFunction(state), action) for state, action in successors]
bestScore = max(scored)[0]
bestActions = [pair[1] for pair in scored if pair[0] == bestScore]
return random.choice(bestActions)

def scoreEvaluation(state):
return state.getScore()