#?multiAgents.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?util?import?manhattanDistance
from?game?import?Directions
import?random?util

from?game?import?Agent

class?ReflexAgent（Agent）:
????“““
??????A?reflex?agent?chooses?an?action?at?each?choice?point?by?examining
??????its?alternatives?via?a?state?evaluation?function.

??????The?code?below?is?provided?as?a?guide.??You?are?welcome?to?change
??????it?in?any?way?you?see?fit?so?long?as?you?don‘t?touch?our?method
??????headers.
????“““


????def?getAction（self?gameState）:
????????“““
????????You?do?not?need?to?change?this?method?but?you‘re?welcome?to.

????????getAction?chooses?among?the?best?options?according?to?the?evaluation?function.

????????Just?like?in?the?previous?project?getAction?takes?a?GameState?and?returns
????????some?Directions.X?for?some?X?in?the?set?{North?South?West?East?Stop}
????????“““
????????#?Collect?legal?moves?and?successor?states
????????legalMoves?=?gameState.getLegalActions（）

????????#?Choose?one?of?the?best?actions
????????scores?=?[self.evaluationFunction（gameState?action）?for?action?in?legalMoves]
????????bestScore?=?max（scores）
????????bestIndices?=?[index?for?index?in?range（len（scores））?if?scores[index]?==?bestScore]
????????chosenIndex?=?random.choice（bestIndices）?#?Pick?randomly?among?the?best

????????“Add?more?of?your?code?here?if?you?want?to“

????????return?legalMoves[chosenIndex]

????def?evaluationFunction（self?currentGameState?action）:
????????“““
????????Design?a?better?evaluation?function?here.

????????The?evaluation?function?takes?in?the?current?and?proposed?successor
????????GameStates?（pacman.py）?and?returns?a?number?where?higher?numbers?are?better.

????????The?code?below?extracts?some?useful?information?from?the?state?like?the
????????remaining?food?（newFood）?and?Pacman?position?after?moving?（newPos）.
????????newScaredTimes?holds?the?number?of?moves?that?each?ghost?will?remain
????????scared?because?of?Pacman?having?eaten?a?power?pellet.

????????Print?out?these?variables?to?see?what?you‘re?getting?then?combine?them
????????to?create?a?masterful?evaluation?function.
????????“““
????????#?Useful?information?you?can?extract?from?a?GameState?（pacman.py）
????????successorGameState?=?currentGameState.generatePacmanSuccessor（action）
????????newPos?=?successorGameState.getPacmanPosition（）
????????newFood?=?successorGameState.getFood（）
????????n

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----

?????文件??????22973??2017-10-22?13:12??searchAgents.py

?????文件??????12043??2017-10-22?13:13??multiAgents.py

?????文件???????5320??2017-10-22?13:12??search.py

-----------?---------??----------?-----??----

????????????????40336????????????????????3