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This commit is contained in:
Arjun Patel
2019-04-11 21:46:37 -07:00
parent edbcead48d
commit be5521d1c4
3 changed files with 198 additions and 24 deletions
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bustersAgents.py
+21
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@@ -144,3 +144,24 @@ class GreedyBustersAgent(BustersAgent):
[beliefs for i, beliefs in enumerate(self.ghostBeliefs)
if livingGhosts[i+1]]
"*** YOUR CODE HERE ***"
max_level = []
for belief in livingGhostPositionDistributions:
max_level.append(belief.argMax())
goalProbability, goalCoordinate = 0, None
for index, coordinate in enumerate(max_level):
# checking goal
if livingGhostPositionDistributions[index][coordinate] >= goalProbability:
goalCoordinate = coordinate
goalProbability = livingGhostPositionDistributions[index][coordinate]
prs = []
# checking all acitons
for action in legal:
nextLocation = Actions.getSuccessor(pacmanPosition, action)
prs.append((self.distancer.getDistance(nextLocation, goalCoordinate), action))
return min(prs)[1]
inference.py
+172 -20
View File
@@ -25,6 +25,7 @@ class DiscreteDistribution(dict):
A DiscreteDistribution models belief distributions and weight distributions
over a finite set of discrete keys.
"""
def __getitem__(self, key):
self.setdefault(key, 0)
return dict.__getitem__(self, key)
@@ -75,7 +76,14 @@ class DiscreteDistribution(dict):
{}
"""
"*** YOUR CODE HERE ***"
raiseNotDefined()
total_calc = float(self.total())
# need to stop and return func since landed on ghost
if total_calc == 0:
return
for key in self.keys():
self[key] = self[key] / total_calc
def sample(self):
"""
@@ -99,7 +107,23 @@ class DiscreteDistribution(dict):
0.0
"""
"*** YOUR CODE HERE ***"
raiseNotDefined()
# already normalized?
if self.total() != 1:
self.normalize()
items_sorted = sorted(self.items())
dist = [i[1] for i in items_sorted]
values = [i[0] for i in items_sorted]
# incorporating the random as in spec
choice = random.random()
total, i = dist[0], 0
while choice > total:
i += 1
total += dist[i]
return values[i]
class InferenceModule:
@@ -129,12 +153,13 @@ class InferenceModule:
jail = self.getJailPosition(index)
gameState = self.setGhostPositions(gameState, pos)
pacmanPosition = gameState.getPacmanPosition()
ghostPosition = gameState.getGhostPosition(index + 1) # The position you set
ghostPosition = gameState.getGhostPosition(
index + 1) # The position you set
dist = DiscreteDistribution()
if pacmanPosition == ghostPosition: # The ghost has been caught!
dist[jail] = 1.0
return dist
pacmanSuccessorStates = game.Actions.getLegalNeighbors(pacmanPosition, \
pacmanSuccessorStates = game.Actions.getLegalNeighbors(pacmanPosition,
gameState.getWalls()) # Positions Pacman can move to
if ghostPosition in pacmanSuccessorStates: # Ghost could get caught
mult = 1.0 / float(len(pacmanSuccessorStates))
@@ -143,11 +168,13 @@ class InferenceModule:
mult = 0.0
actionDist = agent.getDistribution(gameState)
for action, prob in actionDist.items():
successorPosition = game.Actions.getSuccessor(ghostPosition, action)
successorPosition = game.Actions.getSuccessor(
ghostPosition, action)
if successorPosition in pacmanSuccessorStates: # Ghost could get caught
denom = float(len(actionDist))
dist[jail] += prob * (1.0 / denom) * (1.0 - mult)
dist[successorPosition] = prob * ((denom - 1.0) / denom) * (1.0 - mult)
dist[successorPosition] = prob * \
((denom - 1.0) / denom) * (1.0 - mult)
else:
dist[successorPosition] = prob * (1.0 - mult)
return dist
@@ -169,7 +196,20 @@ class InferenceModule:
Return the probability P(noisyDistance | pacmanPosition, ghostPosition).
"""
"*** YOUR CODE HERE ***"
raiseNotDefined()
# check if ghost in jail currently
if jailPosition == ghostPosition:
# remember need floats now
if noisyDistance == None:
return 1.0
else:
return 0.0
if noisyDistance == None:
return 0.0
actual = manhattanDistance(pacmanPosition, ghostPosition)
return busters.getObservationProbability(noisyDistance, actual)
def setGhostPosition(self, gameState, ghostPosition, index):
"""
@@ -195,7 +235,8 @@ class InferenceModule:
"""
for index, pos in enumerate(ghostPositions):
conf = game.Configuration(pos, game.Directions.STOP)
gameState.data.agentStates[index + 1] = game.AgentState(conf, False)
gameState.data.agentStates[index +
1] = game.AgentState(conf, False)
return gameState
def observe(self, gameState):
@@ -212,7 +253,8 @@ class InferenceModule:
"""
Initialize beliefs to a uniform distribution over all legal positions.
"""
self.legalPositions = [p for p in gameState.getWalls().asList(False) if p[1] > 1]
self.legalPositions = [
p for p in gameState.getWalls().asList(False) if p[1] > 1]
self.allPositions = self.legalPositions + [self.getJailPosition()]
self.initializeUniformly(gameState)
@@ -251,6 +293,7 @@ class ExactInference(InferenceModule):
The exact dynamic inference module should use forward algorithm updates to
compute the exact belief function at each time step.
"""
def initializeUniformly(self, gameState):
"""
Begin with a uniform distribution over legal ghost positions (i.e., not
@@ -277,9 +320,20 @@ class ExactInference(InferenceModule):
position is known.
"""
"*** YOUR CODE HERE ***"
raiseNotDefined()
distribution = DiscreteDistribution()
# from input vars
pacmanPosition = gameState.getPacmanPosition()
self.beliefs.normalize()
jailPosition = self.getJailPosition()
for pos in self.allPositions:
prob = self.getObservationProb(
observation, pacmanPosition, pos, jailPosition)
distribution[pos] = prob * self.beliefs[pos]
distribution.normalize()
self.beliefs = distribution
def elapseTime(self, gameState):
"""
@@ -291,7 +345,20 @@ class ExactInference(InferenceModule):
current position is known.
"""
"*** YOUR CODE HERE ***"
raiseNotDefined()
import util
distribution = DiscreteDistribution()
# iterate all over pos
for old_pos in self.allPositions:
new_pos = self.getPositionDistribution(gameState, old_pos)
# update keys
old_prob = self.beliefs[old_pos]
for newPos in new_pos.keys():
distribution[newPos] += old_prob * new_pos[newPos]
self.beliefs = distribution
def getBeliefDistribution(self):
return self.beliefs
@@ -301,6 +368,7 @@ class ParticleFilter(InferenceModule):
"""
A particle filter for approximately tracking a single ghost.
"""
def __init__(self, ghostAgent, numParticles=300):
InferenceModule.__init__(self, ghostAgent)
self.setNumParticles(numParticles)
@@ -318,7 +386,14 @@ class ParticleFilter(InferenceModule):
"""
self.particles = []
"*** YOUR CODE HERE ***"
raiseNotDefined()
count = 0
self.particles = []
# going through all particles
while count < self.numParticles:
for position in self.legalPositions:
if count < self.numParticles:
self.particles.append(position)
count += 1
def observeUpdate(self, observation, gameState):
"""
@@ -333,7 +408,25 @@ class ParticleFilter(InferenceModule):
the DiscreteDistribution may be useful.
"""
"*** YOUR CODE HERE ***"
raiseNotDefined()
# get from given input
pacmanPosition, jailPosition = gameState.getPacmanPosition(), self.getJailPosition()
distribution = DiscreteDistribution()
for par in self.particles:
prob = self.getObservationProb(
observation, pacmanPosition, par, jailPosition)
distribution[par] += prob
# check if not vialbe
if distribution.total() == 0:
self.initializeUniformly(gameState)
else:
distribution.normalize()
self.beliefs = distribution
for x in range(self.numParticles):
new_sample = distribution.sample()
self.particles[x] = new_sample
def elapseTime(self, gameState):
"""
@@ -341,7 +434,20 @@ class ParticleFilter(InferenceModule):
gameState.
"""
"*** YOUR CODE HERE ***"
raiseNotDefined()
import util
cache = {}
# maybe use counter?
for i in range(self.numParticles):
particle = self.particles[i]
# check cases in spec
if particle in cache:
self.particles[i] = cache[particle].sample()
else:
dist = self.getPositionDistribution(gameState, particle)
cache[particle] = dist
self.particles[i] = dist.sample()
def getBeliefDistribution(self):
"""
@@ -352,7 +458,14 @@ class ParticleFilter(InferenceModule):
This function should return a normalized distribution.
"""
"*** YOUR CODE HERE ***"
raiseNotDefined()
import util
distribution = util.Counter()
for element in self.particles:
distribution[element] += 1
distribution.normalize()
return distribution
class JointParticleFilter(ParticleFilter):
@@ -360,6 +473,7 @@ class JointParticleFilter(ParticleFilter):
JointParticleFilter tracks a joint distribution over tuples of all ghost
positions.
"""
def __init__(self, numParticles=600):
self.setNumParticles(numParticles)
@@ -380,7 +494,16 @@ class JointParticleFilter(ParticleFilter):
"""
self.particles = []
"*** YOUR CODE HERE ***"
raiseNotDefined()
perm = list(itertools.product(self.legalPositions, repeat = self.numGhosts))
random.shuffle(perm)
size, n = len(perm), self.numParticles
while size < n:
n -= size
self.particles += perm
self.particles = self.particles + perm[:n]
def addGhostAgent(self, agent):
"""
@@ -413,20 +536,48 @@ class JointParticleFilter(ParticleFilter):
the DiscreteDistribution may be useful.
"""
"*** YOUR CODE HERE ***"
raiseNotDefined()
pacmanPosition, distribution = gameState.getPacmanPosition(), DiscreteDistribution()
for pros in self.particles:
curr_p = 1
for i in range(self.numGhosts):
noisy_dist = observation[i]
curr_p *= self.getObservationProb(noisy_dist, pacmanPosition, pros[i], self.getJailPosition(i))
distribution[pros] += curr_p
self.beliefs = distribution
# check for total/norm
if self.beliefs.total() == 0:
self.initializeUniformly(gameState)
else:
self.beliefs.normalize()
# ok through all particles and assign
for x in range(self.numParticles):
newPos = self.beliefs.sample()
self.particles[x] = newPos
def elapseTime(self, gameState):
"""
Sample each particle's next state based on its current state and the
gameState.
"""
newParticles = []
newParticles, cache = [], {}
for oldParticle in self.particles:
newParticle = list(oldParticle) # A list of ghost positions
# now loop through and update each entry in newParticle...
"*** YOUR CODE HERE ***"
raiseNotDefined()
prevPos = list(oldParticle)
# through all ghosts
for i in range(self.numGhosts):
# check if seen before
if (oldParticle, i) in cache:
newParticle[i] = cache[(oldParticle, i)].sample()
else:
newPosDist = self.getPositionDistribution(gameState, prevPos, i, self.ghostAgents[i])
cache[(oldParticle, i)] = newPosDist
# assign new ones to each particle
newParticle[i] = newPosDist.sample()
"""*** END YOUR CODE HERE ***"""
newParticles.append(tuple(newParticle))
@@ -442,6 +593,7 @@ class MarginalInference(InferenceModule):
A wrapper around the JointInference module that returns marginal beliefs
about ghosts.
"""
def initializeUniformly(self, gameState):
"""
Set the belief state to an initial, prior value.
tracking.token
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