intro2ai/p4_tracking/bustersAgents.py

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2021-12-12 20:08:34 +01:00
# 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 observe(self, observation, gameState):
noisyDistance = observation
emissionModel = busters.getObservationDistribution(noisyDistance)
pacmanPosition = gameState.getPacmanPosition()
allPossible = util.Counter()
for p in self.legalPositions:
trueDistance = util.manhattanDistance(p, pacmanPosition)
if emissionModel[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):
inferenceType = util.lookup(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.observeState(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 closer to the closest ghost (according to mazeDistance!).
To find the mazeDistance between any two positions, use:
self.distancer.getDistance(pos1, pos2)
To find the successor position of a position after an action:
successorPosition = Actions.getSuccessor(position, action)
livingGhostPositionDistributions, defined below, is a list of
util.Counter objects equal to the position belief
distributions for each of the ghosts that are still alive. It
is defined based on (these are implementation details about
which you need not be concerned):
1) gameState.getLivingGhosts(), a list of booleans, one for each
agent, indicating whether or not the agent is alive. Note
that pacman is always agent 0, so the ghosts are agents 1,
onwards (just as before).
2) self.ghostBeliefs, the list of belief distributions for each
of the ghosts (including ghosts that are not alive). The
indices into this list should be 1 less than indices into the
gameState.getLivingGhosts() list.
"""
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]]
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def getMaxProbPos(distribution):
return max([(prob, pos) for pos, prob in distribution.items()])[1]
closestGhostPosition = None
closestGhostDist = float("inf")
for distribution in livingGhostPositionDistributions:
ghostPosition = getMaxProbPos(distribution)
dist = self.distancer.getDistance(pacmanPosition, ghostPosition)
if dist < closestGhostDist:
closestGhostPosition = ghostPosition
closestGhostDist = dist
if closestGhostPosition is None:
return 'Stop'
distanceActionTuples = [
(self.distancer.getDistance(
Actions.getSuccessor(pacmanPosition, action),
closestGhostPosition),
action)
for action in legal]
return min(distanceActionTuples)[1]