bustersAgents.py

# bustersAgents.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 util
from game import Agent
from game import Directions
from keyboardAgents import KeyboardAgent
import inference
import busters

class NullGraphics:
    "Placeholder for graphics"
    def initialize(self, state, isBlue = False):
        pass
    def update(self, state):
        pass
    def pause(self):
        pass
    def draw(self, state):
        pass
    def updateDistributions(self, dist):
        pass
    def finish(self):
        pass

class KeyboardInference(inference.InferenceModule):
    """
    Basic inference module for use with the keyboard.
    """
    def initializeUniformly(self, gameState):
        "Begin with a uniform distribution over ghost positions."
        self.beliefs = util.Counter()
        for p in self.legalPositions: self.beliefs[p] = 1.0
        self.beliefs.normalize()

    def observeUpdate(self, observation, gameState):
        noisyDistance = observation
        pacmanPosition = gameState.getPacmanPosition()
        allPossible = util.Counter()
        for p in self.legalPositions:
            trueDistance = util.manhattanDistance(p, pacmanPosition)
            if noisyDistance != None and \
                    busters.getObservationProbability(noisyDistance, trueDistance) > 0:
                allPossible[p] = 1.0
        allPossible.normalize()
        self.beliefs = allPossible

    def elapseTime(self, gameState):
        pass

    def getBeliefDistribution(self):
        return self.beliefs


class BustersAgent:
    "An agent that tracks and displays its beliefs about ghost positions."

    def __init__( self, index = 0, inference = "ExactInference", ghostAgents = None, observeEnable = True, elapseTimeEnable = True):
        try:
            inferenceType = util.lookup(inference, globals())
        except Exception:
            inferenceType = util.lookup('inference.' + inference, globals())
        self.inferenceModules = [inferenceType(a) for a in ghostAgents]
        self.observeEnable = observeEnable
        self.elapseTimeEnable = elapseTimeEnable

    def registerInitialState(self, gameState):
        "Initializes beliefs and inference modules"
        import __main__
        self.display = __main__._display
        for inference in self.inferenceModules:
            inference.initialize(gameState)
        self.ghostBeliefs = [inf.getBeliefDistribution() for inf in self.inferenceModules]
        self.firstMove = True

    def observationFunction(self, gameState):
        "Removes the ghost states from the gameState"
        agents = gameState.data.agentStates
        gameState.data.agentStates = [agents[0]] + [None for i in range(1, len(agents))]
        return gameState

    def getAction(self, gameState):
        "Updates beliefs, then chooses an action based on updated beliefs."
        for index, inf in enumerate(self.inferenceModules):
            if not self.firstMove and self.elapseTimeEnable:
                inf.elapseTime(gameState)
            self.firstMove = False
            if self.observeEnable:
                inf.observe(gameState)
            self.ghostBeliefs[index] = inf.getBeliefDistribution()
        self.display.updateDistributions(self.ghostBeliefs)
        return self.chooseAction(gameState)

    def chooseAction(self, gameState):
        "By default, a BustersAgent just stops.  This should be overridden."
        return Directions.STOP

class BustersKeyboardAgent(BustersAgent, KeyboardAgent):
    "An agent controlled by the keyboard that displays beliefs about ghost positions."

    def __init__(self, index = 0, inference = "KeyboardInference", ghostAgents = None):
        KeyboardAgent.__init__(self, index)
        BustersAgent.__init__(self, index, inference, ghostAgents)

    def getAction(self, gameState):
        return BustersAgent.getAction(self, gameState)

    def chooseAction(self, gameState):
        return KeyboardAgent.getAction(self, gameState)

from distanceCalculator import Distancer
from game import Actions
from game import Directions

class GreedyBustersAgent(BustersAgent):
    "An agent that charges the closest ghost."

    def registerInitialState(self, gameState):
        "Pre-computes the distance between every two points."
        BustersAgent.registerInitialState(self, gameState)
        self.distancer = Distancer(gameState.data.layout, False)

    def chooseAction(self, gameState):
        """
        First computes the most likely position of each ghost that has
        not yet been captured, then chooses an action that brings
        Pacman closest to the closest ghost (according to mazeDistance!).
        """
        pacmanPosition = gameState.getPacmanPosition()
        legal = [a for a in gameState.getLegalPacmanActions()]
        livingGhosts = gameState.getLivingGhosts()
        livingGhostPositionDistributions = \
            [beliefs for i, beliefs in enumerate(self.ghostBeliefs)
             if livingGhosts[i+1]]
        "*** CS5368 Fall 2023 YOUR CODE HERE ***"
        #Get most likely positions of uncaptured ghosts
        ghost_positions = [dist.argMax() for dist in livingGhostPositionDistributions]
        # Choose action that brings Pacman closest to closest ghost
        bestAction = None
        min_distance = float('inf')
        # Iterate over legal actions
        for action in legal:
            # Get successor position after taking action
            successorPosition = Actions.getSuccessor(pacmanPosition, action)
            # Find maze distance to closest ghost
            closest_ghostDistance = min(self.distancer.getDistance(successorPosition, ghostPosition)
                                         for ghostPosition in ghost_positions)
            # Update best action if distance is minimized
            if closest_ghostDistance < min_distance:
                min_distance = closest_ghostDistance
                bestAction = action

        return bestAction
        """
        procedure:
        1- the the location of all ghost (maximum belief in livingGhostPositionDistributions)
        2- choose the best action according to the above discription
        3- return the best action
        You may need to use self.distancer.getDistance(pacman next possible position, ghost position) at some point in the code
        """