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Model.py
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Model.py
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from World import World
from Solver import Solver
from TransitionFunction import TransitionFunction
from ObservationFunction import ObservationFunction
import Util
import time
import numpy.random
class Model:
# Policy options
POLICIES = ["Random", "Random checkpoint", "RTBSS checkpoint"]
RANDOM = 0
RANDOM_CHECKPOINT = 1
RTBSS_CHECKPOINT = 2
# Model representation
MODEL_REPRESENTATIONS = ["System", "Decentralized"]
SYSTEM = 0
DECENTRALIZED = 1
def __init__(self, grid = None, numAgents = None, numTargets = None, policyOption = None, modelRepr = None, initState = None, checkpoints = None, maxSteps = None, horizon = None, discount = None):
self.world = World(grid)
self.numAgents = 1 if numAgents == None else numAgents
self.numTargets = 1 if numTargets == None else numTargets
self.policyOption = Model.RTBSS_CHECKPOINT if policyOption == None else policyOption
self.modelRepr = Model.SYSTEM if modelRepr == None else modelRepr
self.state = self.__getDefaultInitState() if initState == None else self.getInitState(initState)
self.checkpoints = self.__getDefaultCheckpoints() if checkpoints == None else self.getCheckpoints(checkpoints)
self.checkpointIndeces = [0 for i in range(self.numAgents)]
self.DEFAULT_MAX_STEPS = [20 for i in range(self.numAgents)]
self.maxSteps = self.__getDefaultMaxSteps() if maxSteps == None else maxSteps
self.horizon = 1 if horizon == None else horizon
self.discount = 0.95 if discount == None else discount
self.reward = 0
# Initialize sets of all actions
self.agentActionLabels = ["stay", "go N", "go E", "go S", "go W"]
self.agentActions = [(0, 0), (-1, 0), (0, 1), (1, 0), (0, -1)]
self.targetActionLabels = ["stay", "go N", "go E", "go S", "go W"]
self.targetActions = [(0, 0), (-1, 0), (0, 1), (1, 0), (0, -1)]
#self.targetActions = [(0, 0), (-1, 0), (0, 1), (1, 0), (0, -1), (-2, 0), (0, 2), (2, 0), (0, -2)]
#self.targetActions = [(0, 0), (-1, 0), (0, 1), (1, 0), (0, -1), (-2, 0), (0, 2), (2, 0), (0, -2), (-3, 0), (0, 3), (3, 0), (0, -3)]
#self.targetActions = [(0, 0)]
self.actions = Util.cartesianPower(self.agentActions, self.numAgents) #Util.getOrderedCombinations(self.agentActions, self.numAgents)
self.targetCompoundActions = Util.cartesianPower(self.targetActions, self.numTargets) #Util.getOrderedCombinations(self.targetActions, self.numTargets)
# Initialize lists of all states
self.robotStates = self.world.robotStates
self.agentCompoundStates = []
self.targetCompoundStates = []
self.states = []
self.__initStates()
# Initialize transition, observation and reward functions
self.transitionFcn = TransitionFunction(self)
self.observationFcn = ObservationFunction(self)
self.agentObservations = [frozenset([robotState]) for robotState in self.robotStates]
self.observations = self.observationFcn.getObservations()
self.rewardFcn = self.observationFcn.getRewardFunction() # Reward function is generated upon observation function init
self.observation = frozenset() # Last set of observations
self.observationList = [] # Last observation as enumerated set
self.ambiguousObservation = [] # Last subset of observation that is ambiguous
if self.modelRepr == Model.DECENTRALIZED:
self.partialRewardFcn = dict()
self.__initPartialRewardFcn()
# Initialize belief
self.belief = dict()
self.__initBelief()
# Initialize policy solver
self.solver = Solver(self, self.discount, self.horizon)
# If results should be printed in terminal
self.doPrint = False
def getInitState(self, initStateInput):
"""Get the initial state of the system from input on initialization."""
numRobots = [self.numAgents, self.numTargets]
initState = tuple()
print "getInitState:"
for i in range(2):
initCompoundStateInput = initStateInput[i]
initCompoundState = tuple()
for j in range(len(initCompoundStateInput)):
initRobotStateInput = initCompoundStateInput[j]
if type(initRobotStateInput) is int:
keyword = initRobotStateInput
initCompoundState += (self.world.getState(keyword),)
else:
initCompoundState += (initRobotStateInput,)
initState += (initCompoundState,)
print initState
return initState
def getCheckpoints(self, checkpointsInput):
"""Returns correctly formatted checkpoints list from input."""
for i in range(len(checkpointsInput)):
for j in range(len(checkpointsInput[i])):
if type(checkpointsInput[i][j]) is int:
keyword = checkpointsInput[i][j]
checkpointsInput[i][j] = self.world.getState(keyword)
return checkpointsInput
def __getDefaultInitState(self):
"""Generates and returns a default initial system state."""
agentCompoundState = tuple((i, 0) for i in range(self.numAgents))
targetCompoundState = tuple((i, self.world.gridNumCols-1) for i in range(self.numTargets))
return (agentCompoundState, targetCompoundState)
def __getDefaultCheckpoints(self):
"""Generates and returns a default list of checkpoints"""
checkpoints = [[] for i in range(self.numAgents)]
for i in range(self.numAgents):
state1 = (self.world.gridNumRows - 1, self.world.gridNumCols - 1)
state2 = (0, 0)
checkpoints[i] = [state1, state2]
return checkpoints
def __getDefaultMaxSteps(self):
"""Returns default max steps between checkpoints."""
maxSteps = [None for i in range(self.numAgents)]
for i in range(self.numAgents):
maxSteps[i] = self.DEFAULT_MAX_STEPS[i]
return maxSteps
def __initStates(self):
"""Initializes all states system and sub-system states."""
start = time.time()
print "Initializing state space..."
# Enumerate agent and target compound states
self.agentCompoundStates = Util.cartesianPower(self.robotStates, self.numAgents) #Util.getOrderedCombinations(self.robotStates, self.numAgents)
self.targetCompoundStates = Util.cartesianPower(self.robotStates, self.numTargets) #Util.getOrderedCombinations(self.robotStates, self.numTargets)
# Enumerate system states
#for agentCompoundState in self.agentCompoundStates:
# for targetCompoundState in self.targetCompoundStates:
# self.states.append((agentCompoundState, targetCompoundState))
self.states = Util.cartesianProduct(self.agentCompoundStates, self.targetCompoundStates)
stop = time.time()
print "State space (" + str(len(self.states)) + " states) initialized successfully in " + str(stop - start) + " s."
def __getRobotEndState(self, state, action):
"""Return robot end state after taking action in initial state."""
endState = (state[0] + action[0], state[1] + action[1])
return endState if self.world.isValidRobotState(endState) else state
def __initBelief(self):
"""Initialize belief state."""
for targetCompoundState in self.targetCompoundStates:
self.belief[targetCompoundState] = 1.0/len(self.targetCompoundStates)
self.partialBeliefs = self.getPartialBeliefs()
def T(self, s1, a, s2 = None):
"""
Evaluates transition function for given initial state, action and end state,
i.e. returns probability of system ending up in s2 after starting in s1 and taking action a
"""
#if s2 == None:
# PT = self.transitionFcn.T[s1][a]
# if len(PT) == 1:
# return PT.keys()[0]
# return PT
return self.transitionFcn.eval(s1, a, s2)
def Ta(self, s1, a, s2 = None):
"""
Evaluates agent compound transition function for given initial state, action and end state,
i.e. returns probability of agents ending up in s2 after starting in s1 and taking action a
"""
if s2 == None:
PT = self.transitionFcn.Ta[s1][a]
if len(PT) == 1:
return PT.keys()[0]
return PT
return self.transitionFcn.evalTa(s1, a, s2)
def Tt(self, s1, s2 = None):
"""
Evaluates agent compound transition function for given initial state, action and end state,
i.e. returns probability of agents ending up in s2 after starting in s1 and taking action a
"""
if s2 == None:
PT = self.transitionFcn.Tt[s1]
if len(PT) == 1:
return PT.keys()[0]
return PT
return self.transitionFcn.evalTt(s1, s2)
def Ttl(self, stl1, stl2):
"""
Evaluates the partial target transition function for given initial and end target states.
i.e. returns probability of one target ending up in stl2 starting in stl1
"""
#if stl2 == None:
# PT = self.transitionFcn.Ttl[stl1]
# if len(PT) == 1:
# return PT.keys()[0]
# return PT
return self.transitionFcn.evalTtl(stl1, stl2)
def O(self, s, o):
"""
Evaluates observation function for given state and observation,
i.e. returns probability of system making observation o in state s
"""
if o == None:
PO = self.observationFcn.O[s]
if len(PO) == 1:
return PO.keys()[0]
return PO
return self.observationFcn.eval(s, o)
def Ol(self, sa, stl, o):
"""
Evaliates partial observation for given agent compound state, individual target state,
target index and observation, i.e. returns probability of system making observation o
given agent compound state sa etc.
"""
return self.observationFcn.evalOl(sa, stl, o)
def R(self, s):
"""Returns the reward associated with a system state."""
penalty = -0.5
if self.rewardFcn.get(s, None) != None:
return self.rewardFcn[s]
return penalty
def __initPartialRewardFcn(self):
"""Initializes partial reward function."""
print "Initializing partial reward function..."
start = time.time()
for stl in self.robotStates:
st = list(self.observationFcn.orderedCombinationsOfAllTargetsExceptOne) # Target compound states
for i in range(len(st)):
st[i] = st[i] + (stl,)
for sa in self.agentCompoundStates:
sl = (sa, stl)
if self.numTargets == 1:
s = (sa, (stl,))
self.partialRewardFcn[sl] = self.R(s)
continue
reward = 0.0
for sti in st:
s = (sa, sti)
reward += self.R(s)
self.partialRewardFcn[sl] = reward/len(st)
elapsedTime = time.time() - start
print "Partial reward function initialized in " + str(elapsedTime) + " s."
def Rl(self, sl):
"""Returns the partial reward associated with one of the targets being in state stl. sl = (sa, stl)."""
return self.partialRewardFcn[sl]
def updateCheckpoints(self, checkpoints = None, checkpointIndeces = None, maxSteps = None):
"""Updates checkpoint to next one for each agent if max allowed steps left is zero."""
checkpoints = self.checkpoints if checkpoints == None else checkpoints
checkpointIndeces = list(self.checkpointIndeces) if checkpointIndeces == None else list(checkpointIndeces)
maxSteps = list(self.maxSteps) if maxSteps == None else list(maxSteps)
for i in range(self.numAgents):
checkpointIndex = checkpointIndeces[i]
if maxSteps[i] == 0:
maxSteps[i] = self.DEFAULT_MAX_STEPS[i]
checkpointIndeces[i] = (checkpointIndex + 1)%len(checkpoints[i])
return checkpointIndeces, maxSteps
def __sampleState(self, state, action):
"""Samples a system end state given initial state and action."""
agentCompoundState, targetCompoundState = state[0], state[1]
agentCompoundEndState = self.Ta(agentCompoundState, action)
PTt = self.transitionFcn.Tt[targetCompoundState]
st = numpy.random.choice(range(len(PTt)), p = PTt.values())
targetCompoundEndState = PTt.keys()[st]
endState = (agentCompoundEndState, targetCompoundEndState)
return endState
def __sampleObservation(self, state):
"""Samples an observation given the state."""
PO = self.observationFcn.O[self.state]
o = numpy.random.choice(range(len(PO)), p = PO.values())
observation = PO.keys()[o]
return observation
def update(self, doPrint = False):
"""
Calculates optimal action for current belief, updates system state, makes observation
and updates belief state.
"""
self.doPrint = doPrint
self.printList = [] # List for simultaneous print later (to avoid twitchy print)
if doPrint:
print "\nPerforming system update..."
# Calculate optimal action
self.maxSteps = [self.maxSteps[i] - 1 for i in range(len(self.maxSteps))] # Decrement max allowed steps by 1 for all agents
if self.policyOption == Model.RANDOM:
action = self.solver.getRandomAction()
elif self.policyOption == Model.RANDOM_CHECKPOINT:
action = self.solver.getRandomAllowedAction()
elif self.policyOption == Model.RTBSS_CHECKPOINT:
if self.modelRepr == Model.SYSTEM:
if doPrint:
print "Performing RTBSS for system representation..."
action, value = self.solver.checkpointRTBSS()
elif self.modelRepr == Model.DECENTRALIZED:
if doPrint:
print "Performing RTBSS for decentralized representation..."
action, value = self.solver.checkpointDecentralizedRTBSS()
# Update checkpoints
self.checkpointIndeces, self.maxSteps = self.updateCheckpoints()
nextCheckpoints = [None for i in range(self.numAgents)]
agentActionLabel = [None for i in range(self.numAgents)]
for i in range(self.numAgents):
nextCheckpoints[i] = self.checkpoints[i][self.checkpointIndeces[i]]
agentActionLabel[i] = self.agentActionLabels[self.agentActions.index(action[i])]
self.printList.append("Action: " + str(agentActionLabel))
self.printList.append("Next checkpoint: " + str(nextCheckpoints))
self.printList.append("Max steps left: " + str(self.maxSteps))
# Update system state
agentCompoundState, targetCompoundState = self.state[0], self.state[1]
self.state = self.__sampleState(self.state, action)
self.printList.append("State: " + str(self.state))
self.reward = self.R(self.state)
self.printList.append("Reward: " + str(self.reward))
# Make observation
self.observation = self.__sampleObservation(self.state)
self.observationList = list(self.observation) # Enumarate all observations
self.printList.append("Made observation: " + str(self.observation))
# Update belief
agentCompoundEndState = self.state[0]
start = time.time()
if self.modelRepr == Model.SYSTEM:
self.belief = self.getNewBelief(self.belief, action, self.observation, agentCompoundState, agentCompoundEndState, self.printList)
self.partialBeliefs = self.getPartialBeliefs()
elif self.modelRepr == Model.DECENTRALIZED:
self.partialBeliefs = [self.getNewPartialBelief(self.partialBeliefs[i], self.observation, agentCompoundEndState, self.printList) for i in range(len(self.partialBeliefs))]
stop = time.time()
#self.printList.append("Exp. reward: " + str(self.solver.getExpectedReward()))
self.printList.append("Belief updated in " + str(stop - start) + " s.")
# Update partial belief
self.printList.append("Max belief: " + str(max(self.belief.values())))
self.printList.append("Number of states in belief: " + str(len(self.belief)))
self.printList.append("Individual beliefs:")
# Refine belief based on deduction that some observations can only correspond to certain targets
self.ambiguousObservation = [] # Subset of the observation that agent can't identify with specific targets
if len(self.observation) > 0:
observedTargets = self.deduceWhichTargets()
#observedTargets = [None for i in range(len(self.observationList))] # Targets observed in observation i
#for i in range(len(self.observationList)):
# observedState = self.observationList[i]
# targetIndex = self.deduceWhichTarget(observedState)
# canDeduce = targetIndex != None
# if canDeduce:
# observedTargets[i] = [targetIndex]
# else:
# self.ambiguousObservation.append(observedState)
# Refine belief if targets locations are deduced from observation
if observedTargets.count(None) != len(observedTargets):
self.refineBelief(observedTargets)
for i in range(3):
self.printList.pop()
self.printList.append("Max belief: " + str(max(self.belief.values())))
self.printList.append("Number of states in belief: " + str(len(self.belief)))
self.printList.append("Individual beliefs:")
for i in range(self.numTargets):
self.printList.append("\tTarget " + str(i+1) + ": " + str(len(self.partialBeliefs[i])))
#"""TEST BELIEF OBSERVATION PROBABILITY"""
#obsProb = str(self.solver.getBeliefObservationProbability(agentCompoundState, self.belief, action, self.observation))
#self.printList.append("Probability of making same observation next step: " + obsProb)
# Print results
if doPrint:
self.printUpdateResults()
def deduceWhichTargets(self):
"""
Deduces which targets correspond to which observations by considering possible states in belief.
If a state has non-zero probability only for one target, that target has to be in that state.
"""
observedStates = set(self.observationList)
possibleTargetStates = [set(self.partialBeliefs[targetId].keys()) for targetId in range(self.numTargets)]
possibleTargets = set(i for i in range(self.numTargets))
observedTargets = [None for i in range(len(self.observationList))]
canDeduce = True
#for i in range(len(self.partialBeliefs)):
# print "partialBelief " + str(i+1) + ":"
# print "\t" + str(self.partialBeliefs[i])
while canDeduce:
canDeduce = False
deductions = set()
for observedState in observedStates:
targetsPossiblyAtObservedState = []
for i in possibleTargets:
#print "target: " + str(i+1)
#print "possibleTargetStates[i]: " + str(possibleTargetStates[i])
#print "observedState: " + str(observedState)
if observedState in possibleTargetStates[i]:
targetsPossiblyAtObservedState.append(i)
if len(targetsPossiblyAtObservedState) == 1:
targetId = targetsPossiblyAtObservedState[0]
if self.doPrint:
print "Only target " + str(targetId+1) + " can be in " + str(observedState)
canDeduce = True
observedTargets[self.observationList.index(observedState)] = [targetId]
#print "possibleTargets: " + str(possibleTargets)
possibleTargets.remove(targetId)
#print "possibleTargets: " + str(possibleTargets) + " (after removal)"
#observedStates.remove(observedState)
deductions.add(observedState)
for observedState in deductions:
observedStates.remove(observedState)
for i in range(len(observedTargets)):
if observedTargets[i] == None:
self.ambiguousObservation.append(self.observationList[i])
return observedTargets
def deduceWhichTarget(self, observedState):
"""Returns target index if possible to deduce from belief which target was observed, else returns None."""
if self.doPrint:
print "From deduceWhichTarget:"
print "\tdeduceWhichTarget: " + str(observedState)
print "\tpartialBeliefs: "
targetsPossiblyAtObservedState = []
for i in range(len(self.partialBeliefs)):
if self.doPrint:
print str(self.partialBeliefs[i])
if self.partialBeliefs[i].get(observedState, None) != None:
targetsPossiblyAtObservedState.append(i)
if len(targetsPossiblyAtObservedState) == 1:
return targetsPossiblyAtObservedState[0]
return None
def printUpdateResults(self):
"""Print to terminal the results of most recent update."""
for i in range(self.numTargets):
print "Belief target " + str(i+1) + " (sum: " + str(sum(self.partialBeliefs[i].values())) + "):"
for targetState in self.partialBeliefs[i].keys():
print "\t" + str(targetState) + ": " + str(self.partialBeliefs[i][targetState])
#print "Belief (sum: " + str(sum(self.belief.values())) + "): "
#for key in self.belief.keys():
# print str(key) + ": " + str(self.belief[key])
self.printWorld()
print "\n".join(self.printList)
def getNewUnnormalizedPartialBelief(self, partialBelief, observation, agentCompoundEndState, printList = None):
"""Returns updated unnormalized partial belief."""
newPartialBelief = dict()
# Calculate new unnormalized belief based on tau (belief transition function)]
if self.observationFcn.targetStatesFromObservation[observation].get(agentCompoundEndState, None) == None:
return False
else:
possibleTargetEndStates = self.observationFcn.targetStatesFromObservation[observation][agentCompoundEndState]
count = 0
#for targetEndState in self.robotStates:
for targetEndState in possibleTargetEndStates:
p = 0
for targetState in partialBelief.keys():
p += self.Ttl(targetState, targetEndState)*partialBelief[targetState]
count += 1
newPartialBelief[targetEndState] = self.Ol(agentCompoundEndState, targetEndState, observation)*p
# If all calculated probabilities are 0, the new belief is invalid
if sum(newPartialBelief.values()) < 1e-10:#Util.EPSILON:
return False
# Add info to print list, if applicable
if printList != None:
printList.append("Iterations: " + str(count))
return newPartialBelief
def getNewPartialBelief(self, partialBelief, observation, agentCompoundEndState, printList = None):
"""Get a new belief based on previous belief, action and observation."""
newPartialBelief = self.getNewUnnormalizedPartialBelief(partialBelief, observation, agentCompoundEndState, printList)
# Normalize and remove entries with zero probability
if newPartialBelief == False:
return False
newPartialBelief = self.getNormalizedNonZeroBelief(newPartialBelief)
return newPartialBelief
def getNewUnnormalizedBelief(self, belief, action, observation, agentCompoundState, agentCompoundEndState, printList = None):
"""Returns updated unnormalized belief."""
newBelief = dict()
if agentCompoundEndState != self.Ta(agentCompoundState, action):
print "(s1, a, s2) IMPOSSIBLE!"
return False
# Calculate new unnormalized belief based on tau (belief transition function)
if self.observationFcn.targetCompoundStatesFromObservation[observation].get(agentCompoundEndState, None) != None:
possibleTargetCompoundEndStates = self.observationFcn.targetCompoundStatesFromObservation[observation][agentCompoundEndState]
else:
return False # If no targetCompoundStates are possible to end up in, all probabilities are zero (meaning impossible to make obervation given belief and action)
count = 0
for targetCompoundEndState in possibleTargetCompoundEndStates:
endState = (agentCompoundEndState, targetCompoundEndState)
p = 0.0
for targetCompoundState in belief.keys():
state = (agentCompoundState, targetCompoundState)
p += self.T(state, action, endState)*belief[targetCompoundState]
count += 1
newBelief[targetCompoundEndState] = self.O(endState, observation)*p
# If all calculated probabilities are 0, the new belief is invalid
if sum(newBelief.values()) < Util.EPSILON:
return False
# Add info to print list, if applicable
if printList != None:
printList.append("Observation corresponds to " + str(len(possibleTargetCompoundEndStates)) + " possible target compound states.")
printList.append("Iterations: " + str(count))
return newBelief
def getNewBelief(self, belief, action, observation, agentCompoundState, agentCompoundEndState, printList = None):
"""Get a new belief based on previous belief, action and observation."""
#print "from getNewBelief: belief: " + str(belief)
#print "from getNewBelief: observation: " + str(observation)
# Get new unnormalized belief, normalize and remove entries with zero probability
newBelief = self.getNewUnnormalizedBelief(belief, action, observation, agentCompoundState, agentCompoundEndState, printList)
if newBelief == False:
return False # Means that it is impossible to make observation given belief and action
newBelief = self.getNormalizedNonZeroBelief(newBelief)
return newBelief
def getNormalizedNonZeroBelief(self, belief = None):
"""Returns a normalized belief with zero-entries popped."""
belief = self.belief if belief == None else belief
normFactor = 1/sum(belief.values())
for targetCompoundState in belief.keys():
belief[targetCompoundState] *= normFactor
if abs(belief[targetCompoundState]) < Util.EPSILON:
belief.pop(targetCompoundState)
return belief
def refineBelief(self, observedTargets, observationList = None):
"""Refines the belief based on knowledge that observation i corresponds to specified targets."""
if self.doPrint:
print "From refineBelief:"
print "Beliefs before refinement:"
for i in range(len(self.partialBeliefs)):
print "Belief target " + str(i + 1) + ":"
partialBelief = self.partialBeliefs[i]
for targetState in partialBelief.keys():
print "\t" + str(targetState) + ": " + str(partialBelief[targetState])
#print "\tself.ambiguousObservation: " + str(self.ambiguousObservation)
#print "\tself.observationList: " + str(self.observationList)
if observationList == None:
if len(observedTargets) == len(self.observationList):
observationList = self.observationList
for i in range(len(observedTargets)):
if observedTargets[i] == None:
observedTargets[i] = list()
elif len(observedTargets) == len(self.ambiguousObservation):
observationList = self.ambiguousObservation
#print "\tobservedTargets: " + str(observedTargets)
#print "\tobservationList: " + str(observationList)
# Remove impossible beliefs based on observations
for i in range(len(observationList)):
observedState = observationList[i]
for targetId in range(self.numTargets):
if self.modelRepr == Model.SYSTEM:
for targetCompoundState in self.belief.keys():
targetState = targetCompoundState[targetId]
# Remove all beliefs corresponding to observed target not being in the observed state
# and all beliefs corresponding to non-observed targets being in the observed state
observedTargetNotInObservedState = targetId in observedTargets[i] and targetState != observedState
nonObservedTargetInObservedState = targetId not in observedTargets[i] and targetState == observedState
if observedTargetNotInObservedState or nonObservedTargetInObservedState:
#print "Refined belief, removed:"
#print "\ttargetId+1: " + str(targetId+1)
#print "\ttargetState: " + str(targetState)
#print "\tobservedState: " + str(observedState)
self.belief.pop(targetCompoundState)
elif self.modelRepr == Model.DECENTRALIZED:
for targetState in self.partialBeliefs[targetId].keys():
# Remove all beliefs corresponding to observed target not being in the observed state
# and all beliefs corresponding to non-observed targets being in the observed state
observedTargetNotInObservedState = targetId in observedTargets[i] and targetState != observedState
nonObservedTargetInObservedState = targetId not in observedTargets[i] and targetState == observedState
if observedTargetNotInObservedState or nonObservedTargetInObservedState:
#print "Refined belief, removed:"
#print "\ttargetId+1: " + str(targetId+1)
#print "\ttargetState: " + str(targetState)
#print "\tobservedState: " + str(observedState)
self.partialBeliefs[targetId].pop(targetState)
# Normalize belief
if self.modelRepr == Model.SYSTEM:
self.belief = self.getNormalizedNonZeroBelief()
self.partialBeliefs = self.getPartialBeliefs()
elif self.modelRepr == Model.DECENTRALIZED:
for targetId in range(self.numTargets):
self.partialBeliefs[targetId] = self.getNormalizedNonZeroBelief(self.partialBeliefs[targetId])
# Print results
if self.doPrint:
print "Beliefs after refinement:"
for i in range(len(self.partialBeliefs)):
print "Refined partial belief " + str(i + 1)
partialBelief = self.partialBeliefs[i]
for targetState in partialBelief.keys():
print "\t" + str(targetState) + ": " + str(partialBelief[targetState])
def getPartialBeliefs(self, belief = None):
"""Returns beliefs for each individual target."""
belief = self.belief if belief == None else belief
partialBeliefs = [dict() for i in range(self.numTargets)]
for targetCompoundState in belief.keys():
for i in range(self.numTargets):
targetState = targetCompoundState[i]
if partialBeliefs[i].get(targetState, None) == None:
partialBeliefs[i][targetState] = 0
partialBeliefs[i][targetState] += belief[targetCompoundState]
return partialBeliefs
def isHumanNeeded(self):
"""Returns whether human could be used to resolve belief ambiguity."""
return len(self.ambiguousObservation) > 0
def getHumanInput(self, observationList = None):
"""
Takes human input about observation-target correspondance.
Returns a list of either target ids or None at each index of list of observations. (This is what is returned from Unity application)
"""
observationList = self.ambiguousObservation if observationList == None else observationList
print "observationList: " + str(observationList)
self.printWorld()
observedTargets = [None for i in range(len(observationList))] # Targets observed in observation i
validInputs = {str(i + 1) for i in range(self.numTargets)}
for i in range(len(observationList)):
observedState = observationList[i]
humanInput = None
invalidInput = True
while invalidInput:
if humanInput != None:
print "Invalid input!"
humanInput = raw_input("Which targets are at " + str(observedState) + " (comma-separated ids)? ")
invalidInput = False
for el in humanInput.split(","):
if el not in validInputs:
invalidInput = True
break
observedTargets[i] = []
for targetIdStr in humanInput.split(","):
targetId = int(targetIdStr) - 1 # -1 since starts at 0
observedTargets[i].append(targetId)
print "Human info: " + str(observedTargets) + "\n"
print "\n==============================================================================\n"
return observedTargets
def getSimulatedHumanInput(self, observationList = None):
"""
Simulates perfect human input about observation-target correspondance.
Returns a list of either target ids or None at each index of list of observations. (This is what is returned from Unity application)
"""
observationList = self.ambiguousObservation if observationList == None else observationList
observedTargets = [None for i in range(len(observationList))] # Targets observed in observation i
targetCompoundState = self.state[1]
for i in range(len(observationList)):
observedState = observationList[i]
for targetId in range(self.numTargets):
targetState = targetCompoundState[targetId]
if targetState == observedState:
if observedTargets[i] == None:
observedTargets[i] = []
observedTargets[i].append(targetId)
if self.doPrint:
print "Simulated human gave input: " + str(observedTargets)
return observedTargets
def printWorld(self):
"""Print a 2D grid representation of current state in terminal."""
self.world.printWorld(self.state)