class DemonExperiment(object):
Latency = 100 #s
def __init__(self):
command = CritterbotSimulator.startSimulator()
self.environment = CritterbotSimulator(command)
self.latencyTimer = Chrono()
self.rewards = self.createRewardFunction()
self.actions = XYThetaAction.sevenActions()
self.behaviourPolicy = RandomPolicy(Random(0), self.actions)
self.representation = TileCodersNoHashing(self.environment.legend().nbLabels(), -2000, 2000)
self.representation.includeActiveFeature()
self.demons = []
for rewardFunction in self.rewards:
demon = self.createOnPolicyPredictionDemon(rewardFunction)
self.demons.append(demon)
self.horde = Horde()
self.horde.demons().addAll(self.demons)
self.horde.beforeFunctions().addAll(self.rewards)
self.x_t = None
self.clock = zepy.clock("Nexting Clock")
def createRewardFunction(self):
legend = self.environment.legend()
return list(SensorRewardFunction(legend, label) for label in legend.getLabels())
def createOnPolicyPredictionDemon(self, rewardFunction):
gamma = .9
alpha = .1 / self.representation.vectorNorm()
nbFeatures = self.representation.vectorSize()
lambda_= .3
td = TDLambda(lambda_, gamma, alpha, nbFeatures)
return PredictionDemon(rewardFunction, td)
def learn(self, a_t, o_tp1):
x_tp1 = self.representation.project(o_tp1.doubleValues())
self.horde.update(o_tp1, self.x_t, a_t, x_tp1)
self.x_t = Vectors.bufferedCopy(x_tp1, self.x_t)
def run(self):
a_t = None
while self.clock.tick():
self.latencyTimer.start()
o_tp1 = self.environment.waitNewRawObs()
self.learn(a_t, o_tp1)
self.behaviourPolicy.update(None)
a_tp1 = self.behaviourPolicy.sampleAction()
self.environment.sendAction(a_tp1)
a_t = a_tp1
waitingTime = self.Latency - self.latencyTimer.getCurrentMillis()
if waitingTime > 0:
time.sleep(waitingTime / 1000.0)
self.environment.close()
def zephyrize(self):
zepy.advertise(self.clock, self.environment)
zepy.advertise(self.clock, self.horde)
for rewardFunction in self.rewards:
zepy.monattr(self.clock, rewardFunction, 'rewardValue', label = rewardFunction.label)
def __init__(self):
command = CritterbotSimulator.startSimulator()
self.environment = CritterbotSimulator(command)
self.latencyTimer = Chrono()
self.rewards = self.createRewardFunction()
self.actions = XYThetaAction.sevenActions()
self.behaviourPolicy = RandomPolicy(Random(0), self.actions)
self.representation = TileCodersNoHashing(self.environment.legend().nbLabels(), -2000, 2000)
self.representation.includeActiveFeature()
self.demons = []
for rewardFunction in self.rewards:
self.demons.append(self.createOffPolicyControlDemon(rewardFunction))
self.horde = Horde()
self.horde.demons().addAll(self.demons)
self.horde.beforeFunctions().addAll(self.rewards)
self.x_t = None
self.clock = zepy.clock("Horde Off-policy Control demons")
class DemonExperiment(object):
Latency = 100 #s
def __init__(self):
command = CritterbotSimulator.startSimulator()
self.environment = CritterbotSimulator(command)
self.latencyTimer = Chrono()
self.rewards = self.createRewardFunction()
self.actions = XYThetaAction.sevenActions()
self.behaviourPolicy = RandomPolicy(Random(0), self.actions)
self.representation = TileCodersNoHashing(self.environment.legend().nbLabels(), -2000, 2000)
self.representation.includeActiveFeature()
self.demons = []
for rewardFunction in self.rewards:
self.demons.append(self.createOffPolicyControlDemon(rewardFunction))
self.horde = Horde()
self.horde.demons().addAll(self.demons)
self.horde.beforeFunctions().addAll(self.rewards)
self.x_t = None
self.clock = zepy.clock("Horde Off-policy Control demons")
def createRewardFunction(self):
legend = self.environment.legend()
return [ SensorRewardFunction(legend, 'MotorCurrent0'),
SensorRewardFunction(legend, 'MotorCurrent1'),
SensorRewardFunction(legend, 'MotorCurrent2') ]
def createOffPolicyControlDemon(self, rewardFunction):
toStateAction = TabularAction(self.actions, self.representation.vectorNorm(), self.representation.vectorSize())
nbFeatures = toStateAction.vectorSize()
lambda_ = 0.1
beta = .1
alpha_v = .1 / toStateAction.vectorNorm()
alpha_w = .001 / toStateAction.vectorNorm()
gq = GQ(alpha_v, alpha_w, beta , lambda_, nbFeatures)
targetPolicy = Greedy(gq, self.actions, toStateAction)
controlGQ = GreedyGQ(gq, self.actions, toStateAction, targetPolicy, self.behaviourPolicy)
return ControlOffPolicyDemon(rewardFunction, controlGQ)
def learn(self, a_t, o_tp1):
x_tp1 = self.representation.project(o_tp1.doubleValues())
self.horde.update(o_tp1, self.x_t, a_t, x_tp1)
self.x_t = Vectors.bufferedCopy(x_tp1, self.x_t)
def run(self):
a_t = None
while self.clock.tick():
self.latencyTimer.start()
o_tp1 = self.environment.waitNewRawObs()
self.learn(a_t, o_tp1)
self.behaviourPolicy.update(None)
a_tp1 = self.behaviourPolicy.sampleAction()
self.environment.sendAction(a_tp1)
a_t = a_tp1
waitingTime = self.Latency - self.latencyTimer.getCurrentMillis()
if waitingTime > 0:
time.sleep(waitingTime / 1000.0)
self.environment.close()
def zephyrize(self):
zepy.advertise(self.clock, self.environment)
zepy.advertise(self.clock, self.horde)
for rewardFunction in self.rewards:
zepy.monattr(self.clock, rewardFunction, 'rewardValue', label = rewardFunction.label)