def setSARSA(self, type=None):
if type is None:
type = self.options['SARSA']['type']
if type == "discrete":
self.Sarsa = SARSADiscrete(
sensorObj=self.Sensor,
actionSet=self.Controller.actionSet,
collisionThreshold=self.collisionThreshold,
alphaStepSize=self.options['SARSA']['alphaStepSize'],
useQLearningUpdate=self.options['SARSA']['useQLearningUpdate'],
lam=self.options['SARSA']['lam'],
numInnerBins=self.options['SARSA']['numInnerBins'],
numOuterBins=self.options['SARSA']['numOuterBins'],
binCutoff=self.options['SARSA']['binCutoff'],
burnInTime=self.options['SARSA']['burnInTime'],
epsilonGreedy=self.options['SARSA']['epsilonGreedy'],
epsilonGreedyExponent=self.options['SARSA']
['epsilonGreedyExponent'],
forceDriveStraight=self.options['SARSA']['forceDriveStraight'])
elif type == "continuous":
self.Sarsa = SARSAContinuous(
sensorObj=self.Sensor,
actionSet=self.Controller.actionSet,
lam=self.options['SARSA']['lam'],
alphaStepSize=self.options['SARSA']['alphaStepSize'],
collisionThreshold=self.collisionThreshold,
burnInTime=self.options['SARSA']['burnInTime'],
epsilonGreedy=self.options['SARSA']['epsilonGreedy'],
epsilonGreedyExponent=self.options['SARSA']
['epsilonGreedyExponent'])
else:
raise ValueError(
"sarsa type must be either discrete or continuous")
def setSARSA(self, type=None):
if type is None:
type = self.options["SARSA"]["type"]
if type == "discrete":
self.Sarsa = SARSADiscrete(
sensorObj=self.Sensor,
actionSet=self.Controller.actionSet,
collisionThreshold=self.collisionThreshold,
alphaStepSize=self.options["SARSA"]["alphaStepSize"],
useQLearningUpdate=self.options["SARSA"]["useQLearningUpdate"],
lam=self.options["SARSA"]["lam"],
numInnerBins=self.options["SARSA"]["numInnerBins"],
numOuterBins=self.options["SARSA"]["numOuterBins"],
binCutoff=self.options["SARSA"]["binCutoff"],
burnInTime=self.options["SARSA"]["burnInTime"],
epsilonGreedy=self.options["SARSA"]["epsilonGreedy"],
epsilonGreedyExponent=self.options["SARSA"]["epsilonGreedyExponent"],
forceDriveStraight=self.options["SARSA"]["forceDriveStraight"],
)
elif type == "continuous":
self.Sarsa = SARSAContinuous(
sensorObj=self.Sensor,
actionSet=self.Controller.actionSet,
lam=self.options["SARSA"]["lam"],
alphaStepSize=self.options["SARSA"]["alphaStepSize"],
collisionThreshold=self.collisionThreshold,
burnInTime=self.options["SARSA"]["burnInTime"],
epsilonGreedy=self.options["SARSA"]["epsilonGreedy"],
epsilonGreedyExponent=self.options["SARSA"]["epsilonGreedyExponent"],
)
else:
raise ValueError("sarsa type must be either discrete or continuous")
def setSARSA(self, type=None):
if type is None:
type = self.options['SARSA']['type']
if type=="discrete":
self.Sarsa = SARSADiscrete(sensorObj=self.Sensor, actionSet=self.Controller.actionSet,
collisionThreshold=self.collisionThreshold,
alphaStepSize= self.options['SARSA']['alphaStepSize'],
useQLearningUpdate=self.options['SARSA']['useQLearningUpdate'],
lam=self.options['SARSA']['lam'],
numInnerBins = self.options['SARSA']['numInnerBins'],
numOuterBins = self.options['SARSA']['numOuterBins'],
binCutoff=self.options['SARSA']['binCutoff'],
burnInTime = self.options['SARSA']['burnInTime'],
epsilonGreedy=self.options['SARSA']['epsilonGreedy'],
epsilonGreedyExponent=self.options['SARSA']['epsilonGreedyExponent'])
elif type=="continuous":
self.Sarsa = SARSAContinuous(sensorObj=self.Sensor, actionSet=self.Controller.actionSet,
lam=self.options['SARSA']['lam'],
alphaStepSize= self.options['SARSA']['alphaStepSize'],
collisionThreshold=self.collisionThreshold,
burnInTime = self.options['SARSA']['burnInTime'],
epsilonGreedy=self.options['SARSA']['epsilonGreedy'],
epsilonGreedyExponent=self.options['SARSA']['epsilonGreedyExponent'])
else:
raise ValueError("sarsa type must be either discrete or continuous")
class Simulator(object):
def __init__(self,
percentObsDensity=20,
endTime=40,
randomizeControl=False,
nonRandomWorld=False,
circleRadius=0.7,
worldScale=1.0,
supervisedTrainingTime=500,
autoInitialize=True,
verbose=True,
sarsaType="discrete"):
self.verbose = verbose
self.randomizeControl = randomizeControl
self.startSimTime = time.time()
self.collisionThreshold = 1.3
self.randomSeed = 5
self.Sarsa_numInnerBins = 4
self.Sarsa_numOuterBins = 4
self.Sensor_rayLength = 8
self.sarsaType = sarsaType
self.percentObsDensity = percentObsDensity
self.supervisedTrainingTime = 10
self.learningRandomTime = 10
self.learningEvalTime = 10
self.defaultControllerTime = 10
self.nonRandomWorld = nonRandomWorld
self.circleRadius = circleRadius
self.worldScale = worldScale
# create the visualizer object
self.app = ConsoleApp()
# view = app.createView(useGrid=False)
self.view = self.app.createView(useGrid=False)
self.initializeOptions()
self.initializeColorMap()
if autoInitialize:
self.initialize()
def initializeOptions(self):
self.options = dict()
self.options['Reward'] = dict()
self.options['Reward']['actionCost'] = 0.1
self.options['Reward']['collisionPenalty'] = 100.0
self.options['Reward']['raycastCost'] = 20.0
self.options['SARSA'] = dict()
self.options['SARSA']['type'] = "discrete"
self.options['SARSA']['lam'] = 0.7
self.options['SARSA']['alphaStepSize'] = 0.2
self.options['SARSA']['useQLearningUpdate'] = False
self.options['SARSA']['epsilonGreedy'] = 0.2
self.options['SARSA']['burnInTime'] = 500
self.options['SARSA']['epsilonGreedyExponent'] = 0.3
self.options['SARSA'][
'exponentialDiscountFactor'] = 0.05 #so gamma = e^(-rho*dt)
self.options['SARSA']['numInnerBins'] = 5
self.options['SARSA']['numOuterBins'] = 4
self.options['SARSA']['binCutoff'] = 0.5
self.options['World'] = dict()
self.options['World']['obstaclesInnerFraction'] = 0.85
self.options['World']['randomSeed'] = 40
self.options['World']['percentObsDensity'] = 7.5
self.options['World']['nonRandomWorld'] = True
self.options['World']['circleRadius'] = 1.75
self.options['World']['scale'] = 1.0
self.options['Sensor'] = dict()
self.options['Sensor']['rayLength'] = 10
self.options['Sensor']['numRays'] = 20
self.options['Car'] = dict()
self.options['Car']['velocity'] = 12
self.options['dt'] = 0.05
self.options['runTime'] = dict()
self.options['runTime']['supervisedTrainingTime'] = 10
self.options['runTime']['learningRandomTime'] = 10
self.options['runTime']['learningEvalTime'] = 10
self.options['runTime']['defaultControllerTime'] = 10
def setDefaultOptions(self):
defaultOptions = dict()
defaultOptions['Reward'] = dict()
defaultOptions['Reward']['actionCost'] = 0.1
defaultOptions['Reward']['collisionPenalty'] = 100.0
defaultOptions['Reward']['raycastCost'] = 20.0
defaultOptions['SARSA'] = dict()
defaultOptions['SARSA']['type'] = "discrete"
defaultOptions['SARSA']['lam'] = 0.7
defaultOptions['SARSA']['alphaStepSize'] = 0.2
defaultOptions['SARSA']['useQLearningUpdate'] = False
defaultOptions['SARSA']['useSupervisedTraining'] = True
defaultOptions['SARSA']['epsilonGreedy'] = 0.2
defaultOptions['SARSA']['burnInTime'] = 500
defaultOptions['SARSA']['epsilonGreedyExponent'] = 0.3
defaultOptions['SARSA'][
'exponentialDiscountFactor'] = 0.05 #so gamma = e^(-rho*dt)
defaultOptions['SARSA']['numInnerBins'] = 5
defaultOptions['SARSA']['numOuterBins'] = 4
defaultOptions['SARSA']['binCutoff'] = 0.5
defaultOptions['SARSA']['forceDriveStraight'] = True
defaultOptions['World'] = dict()
defaultOptions['World']['obstaclesInnerFraction'] = 0.85
defaultOptions['World']['randomSeed'] = 40
defaultOptions['World']['percentObsDensity'] = 7.5
defaultOptions['World']['nonRandomWorld'] = True
defaultOptions['World']['circleRadius'] = 1.75
defaultOptions['World']['scale'] = 1.0
defaultOptions['Sensor'] = dict()
defaultOptions['Sensor']['rayLength'] = 10
defaultOptions['Sensor']['numRays'] = 20
defaultOptions['Car'] = dict()
defaultOptions['Car']['velocity'] = 12
defaultOptions['dt'] = 0.05
defaultOptions['runTime'] = dict()
defaultOptions['runTime']['supervisedTrainingTime'] = 10
defaultOptions['runTime']['learningRandomTime'] = 10
defaultOptions['runTime']['learningEvalTime'] = 10
defaultOptions['runTime']['defaultControllerTime'] = 10
for k in defaultOptions:
self.options.setdefault(k, defaultOptions[k])
for k in defaultOptions:
if not isinstance(defaultOptions[k], dict):
continue
for j in defaultOptions[k]:
self.options[k].setdefault(j, defaultOptions[k][j])
def initializeColorMap(self):
self.colorMap = dict()
self.colorMap['defaultRandom'] = [0, 0, 1]
self.colorMap['learnedRandom'] = [1.0, 0.54901961,
0.] # this is orange
self.colorMap['learned'] = [0.58039216, 0.0,
0.82745098] # this is yellow
self.colorMap['default'] = [0, 1, 0]
def initialize(self):
self.dt = self.options['dt']
self.controllerTypeOrder = [
'defaultRandom', 'learnedRandom', 'learned', 'default'
]
self.setDefaultOptions()
self.Sensor = SensorObj(rayLength=self.options['Sensor']['rayLength'],
numRays=self.options['Sensor']['numRays'])
self.Controller = ControllerObj(self.Sensor)
self.Car = CarPlant(controller=self.Controller,
velocity=self.options['Car']['velocity'])
self.Reward = Reward(
self.Sensor,
collisionThreshold=self.collisionThreshold,
actionCost=self.options['Reward']['actionCost'],
collisionPenalty=self.options['Reward']['collisionPenalty'],
raycastCost=self.options['Reward']['raycastCost'])
self.setSARSA()
# create the things needed for simulation
om.removeFromObjectModel(om.findObjectByName('world'))
self.world = World.buildCircleWorld(
percentObsDensity=self.options['World']['percentObsDensity'],
circleRadius=self.options['World']['circleRadius'],
nonRandom=self.options['World']['nonRandomWorld'],
scale=self.options['World']['scale'],
randomSeed=self.options['World']['randomSeed'],
obstaclesInnerFraction=self.options['World']
['obstaclesInnerFraction'])
om.removeFromObjectModel(om.findObjectByName('robot'))
self.robot, self.frame = World.buildRobot()
self.locator = World.buildCellLocator(self.world.visObj.polyData)
self.Sensor.setLocator(self.locator)
self.frame = self.robot.getChildFrame()
self.frame.setProperty('Scale', 3)
self.frame.setProperty('Edit', True)
self.frame.widget.HandleRotationEnabledOff()
rep = self.frame.widget.GetRepresentation()
rep.SetTranslateAxisEnabled(2, False)
rep.SetRotateAxisEnabled(0, False)
rep.SetRotateAxisEnabled(1, False)
self.supervisedTrainingTime = self.options['runTime'][
'supervisedTrainingTime']
self.learningRandomTime = self.options['runTime']['learningRandomTime']
self.learningEvalTime = self.options['runTime']['learningEvalTime']
self.defaultControllerTime = self.options['runTime'][
'defaultControllerTime']
self.Car.setFrame(self.frame)
print "Finished initialization"
def setSARSA(self, type=None):
if type is None:
type = self.options['SARSA']['type']
if type == "discrete":
self.Sarsa = SARSADiscrete(
sensorObj=self.Sensor,
actionSet=self.Controller.actionSet,
collisionThreshold=self.collisionThreshold,
alphaStepSize=self.options['SARSA']['alphaStepSize'],
useQLearningUpdate=self.options['SARSA']['useQLearningUpdate'],
lam=self.options['SARSA']['lam'],
numInnerBins=self.options['SARSA']['numInnerBins'],
numOuterBins=self.options['SARSA']['numOuterBins'],
binCutoff=self.options['SARSA']['binCutoff'],
burnInTime=self.options['SARSA']['burnInTime'],
epsilonGreedy=self.options['SARSA']['epsilonGreedy'],
epsilonGreedyExponent=self.options['SARSA']
['epsilonGreedyExponent'],
forceDriveStraight=self.options['SARSA']['forceDriveStraight'])
elif type == "continuous":
self.Sarsa = SARSAContinuous(
sensorObj=self.Sensor,
actionSet=self.Controller.actionSet,
lam=self.options['SARSA']['lam'],
alphaStepSize=self.options['SARSA']['alphaStepSize'],
collisionThreshold=self.collisionThreshold,
burnInTime=self.options['SARSA']['burnInTime'],
epsilonGreedy=self.options['SARSA']['epsilonGreedy'],
epsilonGreedyExponent=self.options['SARSA']
['epsilonGreedyExponent'])
else:
raise ValueError(
"sarsa type must be either discrete or continuous")
def runSingleSimulation(self,
updateQValues=True,
controllerType='default',
simulationCutoff=None):
if self.verbose:
print "using QValue based controller = ", useQValueController
self.setCollisionFreeInitialState()
currentCarState = np.copy(self.Car.state)
nextCarState = np.copy(self.Car.state)
self.setRobotFrameState(currentCarState[0], currentCarState[1],
currentCarState[2])
currentRaycast = self.Sensor.raycastAll(self.frame)
nextRaycast = np.zeros(self.Sensor.numRays)
self.Sarsa.resetElibilityTraces()
# record the reward data
runData = dict()
reward = 0
discountedReward = 0
avgReward = 0
startIdx = self.counter
while (self.counter < self.numTimesteps - 1):
idx = self.counter
currentTime = self.t[idx]
self.stateOverTime[idx, :] = currentCarState
x = self.stateOverTime[idx, 0]
y = self.stateOverTime[idx, 1]
theta = self.stateOverTime[idx, 2]
self.setRobotFrameState(x, y, theta)
# self.setRobotState(currentCarState[0], currentCarState[1], currentCarState[2])
currentRaycast = self.Sensor.raycastAll(self.frame)
self.raycastData[idx, :] = currentRaycast
S_current = (currentCarState, currentRaycast)
if controllerType not in self.colorMap.keys():
print
raise ValueError("controller of type " + controllerType +
" not supported")
if controllerType in ["default", "defaultRandom"]:
if controllerType == "defaultRandom":
controlInput, controlInputIdx = self.Controller.computeControlInput(
currentCarState,
currentTime,
self.frame,
raycastDistance=currentRaycast,
randomize=True)
else:
controlInput, controlInputIdx = self.Controller.computeControlInput(
currentCarState,
currentTime,
self.frame,
raycastDistance=currentRaycast,
randomize=False)
if controllerType in ["learned", "learnedRandom"]:
if controllerType == "learned":
randomizeControl = False
else:
randomizeControl = True
counterForGreedyDecay = self.counter - self.idxDict[
"learnedRandom"]
controlInput, controlInputIdx, emptyQValue = self.Sarsa.computeGreedyControlPolicy(
S_current,
counter=counterForGreedyDecay,
randomize=randomizeControl)
self.emptyQValue[idx] = emptyQValue
if emptyQValue and self.options['SARSA'][
'useSupervisedTraining']:
controlInput, controlInputIdx = self.Controller.computeControlInput(
currentCarState,
currentTime,
self.frame,
raycastDistance=currentRaycast,
randomize=False)
self.controlInputData[idx] = controlInput
nextCarState = self.Car.simulateOneStep(controlInput=controlInput,
dt=self.dt)
# want to compute nextRaycast so we can do the SARSA algorithm
x = nextCarState[0]
y = nextCarState[1]
theta = nextCarState[2]
self.setRobotFrameState(x, y, theta)
nextRaycast = self.Sensor.raycastAll(self.frame)
# Compute the next control input
S_next = (nextCarState, nextRaycast)
if controllerType in ["default", "defaultRandom"]:
if controllerType == "defaultRandom":
nextControlInput, nextControlInputIdx = self.Controller.computeControlInput(
nextCarState,
currentTime,
self.frame,
raycastDistance=nextRaycast,
randomize=True)
else:
nextControlInput, nextControlInputIdx = self.Controller.computeControlInput(
nextCarState,
currentTime,
self.frame,
raycastDistance=nextRaycast,
randomize=False)
if controllerType in ["learned", "learnedRandom"]:
if controllerType == "learned":
randomizeControl = False
else:
randomizeControl = True
counterForGreedyDecay = self.counter - self.idxDict[
"learnedRandom"]
nextControlInput, nextControlInputIdx, emptyQValue = self.Sarsa.computeGreedyControlPolicy(
S_next,
counter=counterForGreedyDecay,
randomize=randomizeControl)
if emptyQValue and self.options['SARSA'][
'useSupervisedTraining']:
nextControlInput, nextControlInputIdx = self.Controller.computeControlInput(
nextCarState,
currentTime,
self.frame,
raycastDistance=nextRaycast,
randomize=False)
# if useQValueController:
# nextControlInput, nextControlInputIdx, emptyQValue = self.Sarsa.computeGreedyControlPolicy(S_next)
#
# if not useQValueController or emptyQValue:
# nextControlInput, nextControlInputIdx = self.Controller.computeControlInput(nextCarState, currentTime, self.frame,
# raycastDistance=nextRaycast)
# compute the reward
reward = self.Reward.computeReward(S_next, controlInput)
self.rewardData[idx] = reward
discountedReward += self.Sarsa.gamma**(self.counter -
startIdx) * reward
avgReward += reward
###### SARSA update
if updateQValues:
self.Sarsa.sarsaUpdate(S_current, controlInputIdx, reward,
S_next, nextControlInputIdx)
#bookkeeping
currentCarState = nextCarState
currentRaycast = nextRaycast
self.counter += 1
# break if we are in collision
if self.checkInCollision(nextRaycast):
if self.verbose:
print "Had a collision, terminating simulation"
break
if self.counter >= simulationCutoff:
break
# fill in the last state by hand
self.stateOverTime[self.counter, :] = currentCarState
self.raycastData[self.counter, :] = currentRaycast
# return the total reward
avgRewardNoCollisionPenalty = avgReward - reward
avgReward = avgReward * 1.0 / max(1, self.counter - startIdx)
avgRewardNoCollisionPenalty = avgRewardNoCollisionPenalty * 1.0 / max(
1, self.counter - startIdx)
# this extra multiplication is so that it is in the same "units" as avgReward
runData['discountedReward'] = discountedReward * (1 - self.Sarsa.gamma)
runData['avgReward'] = avgReward
runData['avgRewardNoCollisionPenalty'] = avgRewardNoCollisionPenalty
# this just makes sure we don't get stuck in an infinite loop.
if startIdx == self.counter:
self.counter += 1
return runData
def setNumpyRandomSeed(self, seed=1):
np.random.seed(seed)
def runBatchSimulation(self, endTime=None, dt=0.05):
# for use in playback
self.dt = self.options['dt']
self.Sarsa.setDiscountFactor(dt)
self.endTime = self.supervisedTrainingTime + self.learningRandomTime + self.learningEvalTime + self.defaultControllerTime
self.t = np.arange(0.0, self.endTime, dt)
maxNumTimesteps = np.size(self.t)
self.stateOverTime = np.zeros((maxNumTimesteps + 1, 3))
self.raycastData = np.zeros((maxNumTimesteps + 1, self.Sensor.numRays))
self.controlInputData = np.zeros(maxNumTimesteps + 1)
self.rewardData = np.zeros(maxNumTimesteps + 1)
self.emptyQValue = np.zeros(maxNumTimesteps + 1, dtype='bool')
self.numTimesteps = maxNumTimesteps
self.controllerTypeOrder = [
'defaultRandom', 'learnedRandom', 'learned', 'default'
]
self.counter = 0
self.simulationData = []
self.initializeStatusBar()
self.idxDict = dict()
numRunsCounter = 0
# three while loops for different phases of simulation, supervisedTraining, learning, default
self.idxDict['defaultRandom'] = self.counter
loopStartIdx = self.counter
simCutoff = min(loopStartIdx + self.supervisedTrainingTime / dt,
self.numTimesteps)
while ((self.counter - loopStartIdx < self.supervisedTrainingTime / dt)
and self.counter < self.numTimesteps):
self.printStatusBar()
useQValueController = False
startIdx = self.counter
runData = self.runSingleSimulation(updateQValues=True,
controllerType='defaultRandom',
simulationCutoff=simCutoff)
runData['startIdx'] = startIdx
runData['controllerType'] = "defaultRandom"
runData['duration'] = self.counter - runData['startIdx']
runData['endIdx'] = self.counter
runData['runNumber'] = numRunsCounter
numRunsCounter += 1
self.simulationData.append(runData)
self.idxDict['learnedRandom'] = self.counter
loopStartIdx = self.counter
simCutoff = min(loopStartIdx + self.learningRandomTime / dt,
self.numTimesteps)
while ((self.counter - loopStartIdx < self.learningRandomTime / dt)
and self.counter < self.numTimesteps):
self.printStatusBar()
startIdx = self.counter
runData = self.runSingleSimulation(updateQValues=True,
controllerType='learnedRandom',
simulationCutoff=simCutoff)
runData['startIdx'] = startIdx
runData['controllerType'] = "learnedRandom"
runData['duration'] = self.counter - runData['startIdx']
runData['endIdx'] = self.counter
runData['runNumber'] = numRunsCounter
numRunsCounter += 1
self.simulationData.append(runData)
self.idxDict['learned'] = self.counter
loopStartIdx = self.counter
simCutoff = min(loopStartIdx + self.learningEvalTime / dt,
self.numTimesteps)
while ((self.counter - loopStartIdx < self.learningEvalTime / dt)
and self.counter < self.numTimesteps):
self.printStatusBar()
startIdx = self.counter
runData = self.runSingleSimulation(updateQValues=False,
controllerType='learned',
simulationCutoff=simCutoff)
runData['startIdx'] = startIdx
runData['controllerType'] = "learned"
runData['duration'] = self.counter - runData['startIdx']
runData['endIdx'] = self.counter
runData['runNumber'] = numRunsCounter
numRunsCounter += 1
self.simulationData.append(runData)
self.idxDict['default'] = self.counter
loopStartIdx = self.counter
simCutoff = min(loopStartIdx + self.defaultControllerTime / dt,
self.numTimesteps)
while ((self.counter - loopStartIdx < self.defaultControllerTime / dt)
and self.counter < self.numTimesteps - 1):
self.printStatusBar()
startIdx = self.counter
runData = self.runSingleSimulation(updateQValues=False,
controllerType='default',
simulationCutoff=simCutoff)
runData['startIdx'] = startIdx
runData['controllerType'] = "default"
runData['duration'] = self.counter - runData['startIdx']
runData['endIdx'] = self.counter
runData['runNumber'] = numRunsCounter
numRunsCounter += 1
self.simulationData.append(runData)
# BOOKKEEPING
# truncate stateOverTime, raycastData, controlInputs to be the correct size
self.numTimesteps = self.counter + 1
self.stateOverTime = self.stateOverTime[0:self.counter + 1, :]
self.raycastData = self.raycastData[0:self.counter + 1, :]
self.controlInputData = self.controlInputData[0:self.counter + 1]
self.rewardData = self.rewardData[0:self.counter + 1]
self.endTime = 1.0 * self.counter / self.numTimesteps * self.endTime
def initializeStatusBar(self):
self.numTicks = 10
self.nextTickComplete = 1.0 / float(self.numTicks)
self.nextTickIdx = 1
print "Simulation percentage complete: (", self.numTicks, " # is complete)"
def printStatusBar(self):
fractionDone = float(self.counter) / float(self.numTimesteps)
if fractionDone > self.nextTickComplete:
self.nextTickIdx += 1
self.nextTickComplete += 1.0 / self.numTicks
timeSoFar = time.time() - self.startSimTime
estimatedTimeLeft_sec = (1 -
fractionDone) * timeSoFar / fractionDone
estimatedTimeLeft_minutes = estimatedTimeLeft_sec / 60.0
print "#" * self.nextTickIdx, "-" * (
self.numTicks - self.nextTickIdx
), "estimated", estimatedTimeLeft_minutes, "minutes left"
def setCollisionFreeInitialState(self):
tol = 5
while True:
x = np.random.uniform(self.world.Xmin + tol, self.world.Xmax - tol,
1)[0]
y = np.random.uniform(self.world.Ymin + tol, self.world.Ymax - tol,
1)[0]
theta = np.random.uniform(0, 2 * np.pi, 1)[0]
self.Car.setCarState(x, y, theta)
self.setRobotFrameState(x, y, theta)
if not self.checkInCollision():
break
# if self.checkInCollision():
# print "IN COLLISION"
# else:
# print "COLLISION FREE"
return x, y, theta
def setupPlayback(self):
self.timer = TimerCallback(targetFps=30)
self.timer.callback = self.tick
playButtonFps = 1.0 / self.dt
print "playButtonFPS", playButtonFps
self.playTimer = TimerCallback(targetFps=playButtonFps)
self.playTimer.callback = self.playTimerCallback
self.sliderMovedByPlayTimer = False
panel = QtGui.QWidget()
l = QtGui.QHBoxLayout(panel)
playButton = QtGui.QPushButton('Play/Pause')
playButton.connect('clicked()', self.onPlayButton)
slider = QtGui.QSlider(QtCore.Qt.Horizontal)
slider.connect('valueChanged(int)', self.onSliderChanged)
self.sliderMax = self.numTimesteps
slider.setMaximum(self.sliderMax)
self.slider = slider
l.addWidget(playButton)
l.addWidget(slider)
w = QtGui.QWidget()
l = QtGui.QVBoxLayout(w)
l.addWidget(self.view)
l.addWidget(panel)
w.showMaximized()
self.frame.connectFrameModified(self.updateDrawIntersection)
self.updateDrawIntersection(self.frame)
applogic.resetCamera(viewDirection=[0.2, 0, -1])
self.view.showMaximized()
self.view.raise_()
panel = screengrabberpanel.ScreenGrabberPanel(self.view)
panel.widget.show()
elapsed = time.time() - self.startSimTime
simRate = self.counter / elapsed
print "Total run time", elapsed
print "Ticks (Hz)", simRate
print "Number of steps taken", self.counter
self.app.start()
def run(self, launchApp=True):
self.counter = 1
self.runBatchSimulation()
# self.Sarsa.plotWeights()
if launchApp:
self.setupPlayback()
def updateDrawIntersection(self, frame):
origin = np.array(frame.transform.GetPosition())
#print "origin is now at", origin
d = DebugData()
sliderIdx = self.slider.value
controllerType = self.getControllerTypeFromCounter(sliderIdx)
colorMaxRange = self.colorMap[controllerType]
# if the QValue was empty then color it green
if self.emptyQValue[sliderIdx]:
colorMaxRange = [1, 1, 0] # this is yellow
for i in xrange(self.Sensor.numRays):
ray = self.Sensor.rays[:, i]
rayTransformed = np.array(frame.transform.TransformNormal(ray))
#print "rayTransformed is", rayTransformed
intersection = self.Sensor.raycast(
self.locator, origin,
origin + rayTransformed * self.Sensor.rayLength)
if intersection is not None:
d.addLine(origin, intersection, color=[1, 0, 0])
else:
d.addLine(origin,
origin + rayTransformed * self.Sensor.rayLength,
color=colorMaxRange)
vis.updatePolyData(d.getPolyData(), 'rays', colorByName='RGB255')
#camera = self.view.camera()
#camera.SetFocalPoint(frame.transform.GetPosition())
#camera.SetPosition(frame.transform.TransformPoint((-30,0,10)))
def getControllerTypeFromCounter(self, counter):
name = self.controllerTypeOrder[0]
for controllerType in self.controllerTypeOrder[1:]:
if counter >= self.idxDict[controllerType]:
name = controllerType
return name
def setRobotFrameState(self, x, y, theta):
t = vtk.vtkTransform()
t.Translate(x, y, 0.0)
t.RotateZ(np.degrees(theta))
self.robot.getChildFrame().copyFrame(t)
# returns true if we are in collision
def checkInCollision(self, raycastDistance=None):
if raycastDistance is None:
self.setRobotFrameState(self.Car.state[0], self.Car.state[1],
self.Car.state[2])
raycastDistance = self.Sensor.raycastAll(self.frame)
if np.min(raycastDistance) < self.collisionThreshold:
return True
else:
return False
def tick(self):
#print timer.elapsed
#simulate(t.elapsed)
x = np.sin(time.time())
y = np.cos(time.time())
self.setRobotFrameState(x, y, 0.0)
if (time.time() - self.playTime) > self.endTime:
self.playTimer.stop()
def tick2(self):
newtime = time.time() - self.playTime
print time.time() - self.playTime
x = np.sin(newtime)
y = np.cos(newtime)
self.setRobotFrameState(x, y, 0.0)
# just increment the slider, stop the timer if we get to the end
def playTimerCallback(self):
self.sliderMovedByPlayTimer = True
currentIdx = self.slider.value
nextIdx = currentIdx + 1
self.slider.setSliderPosition(nextIdx)
if currentIdx >= self.sliderMax:
print "reached end of tape, stopping playTime"
self.playTimer.stop()
def onSliderChanged(self, value):
if not self.sliderMovedByPlayTimer:
self.playTimer.stop()
numSteps = len(self.stateOverTime)
idx = int(np.floor(numSteps * (1.0 * value / self.sliderMax)))
idx = min(idx, numSteps - 1)
x, y, theta = self.stateOverTime[idx]
self.setRobotFrameState(x, y, theta)
self.sliderMovedByPlayTimer = False
def onPlayButton(self):
if self.playTimer.isActive():
self.onPauseButton()
return
print 'play'
self.playTimer.start()
self.playTime = time.time()
def onPauseButton(self):
print 'pause'
self.playTimer.stop()
def computeRunStatistics(self):
numRuns = len(self.simulationData)
runStart = np.zeros(numRuns)
runDuration = np.zeros(numRuns)
grid = np.arange(1, numRuns + 1)
discountedReward = np.zeros(numRuns)
avgReward = np.zeros(numRuns)
avgRewardNoCollisionPenalty = np.zeros(numRuns)
idxMap = dict()
for controllerType, color in self.colorMap.iteritems():
idxMap[controllerType] = np.zeros(numRuns, dtype=bool)
for idx, val in enumerate(self.simulationData):
runStart[idx] = val['startIdx']
runDuration[idx] = val['duration']
discountedReward[idx] = val['discountedReward']
avgReward[idx] = val['avgReward']
avgRewardNoCollisionPenalty[idx] = val[
'avgRewardNoCollisionPenalty']
controllerType = val['controllerType']
idxMap[controllerType][idx] = True
def plotRunData(self,
controllerTypeToPlot=None,
showPlot=True,
onlyLearned=False):
if controllerTypeToPlot == None:
controllerTypeToPlot = self.colorMap.keys()
if onlyLearned:
controllerTypeToPlot = ['learnedRandom', 'learned']
numRuns = len(self.simulationData)
runStart = np.zeros(numRuns)
runDuration = np.zeros(numRuns)
grid = np.arange(1, numRuns + 1)
discountedReward = np.zeros(numRuns)
avgReward = np.zeros(numRuns)
avgRewardNoCollisionPenalty = np.zeros(numRuns)
idxMap = dict()
for controllerType, color in self.colorMap.iteritems():
idxMap[controllerType] = np.zeros(numRuns, dtype=bool)
for idx, val in enumerate(self.simulationData):
runStart[idx] = val['startIdx']
runDuration[idx] = val['duration']
discountedReward[idx] = val['discountedReward']
avgReward[idx] = val['avgReward']
avgRewardNoCollisionPenalty[idx] = val[
'avgRewardNoCollisionPenalty']
controllerType = val['controllerType']
idxMap[controllerType][idx] = True
# usedQValueController[idx] = (val['controllerType'] == "QValue")
# usedDefaultController[idx] = (val['controllerType'] == "default")
# usedDefaultRandomController[idx] = (val['controllerType'] == "defaultRandom")
# usedQValueRandomController[idx] = (val['controllerType'] == "QValueRandom")
self.runStatistics = dict()
dataMap = {
'duration': runDuration,
'discountedReward': discountedReward,
'avgReward': avgReward,
'avgRewardNoCollisionPenalty': avgRewardNoCollisionPenalty
}
def computeRunStatistics(dataMap):
for controllerType, idx in idxMap.iteritems():
d = dict()
for dataName, dataSet in dataMap.iteritems():
# average the appropriate values in dataset
d[dataName] = np.sum(
dataSet[idx]) / (1.0 * np.size(dataSet[idx]))
self.runStatistics[controllerType] = d
computeRunStatistics(dataMap)
if not showPlot:
return
plt.figure()
#
# idxDefaultRandom = np.where(usedDefaultRandomController==True)[0]
# idxQValueController = np.where(usedQValueController==True)[0]
# idxQValueControllerRandom = np.where(usedQValueControllerRandom==True)[0]
# idxDefault = np.where(usedDefaultController==True)[0]
#
# plotData = dict()
# plotData['defaultRandom'] = {'idx': idxDefaultRandom, 'color': 'b'}
# plotData['QValue'] = {'idx': idxQValueController, 'color': 'y'}
# plotData['default'] = {'idx': idxDefault, 'color': 'g'}
def scatterPlot(dataToPlot):
for controllerType in controllerTypeToPlot:
idx = idxMap[controllerType]
plt.scatter(grid[idx],
dataToPlot[idx],
color=self.colorMap[controllerType])
def barPlot(dataName):
plt.title(dataName)
barWidth = 0.5
barCounter = 0
index = np.arange(len(controllerTypeToPlot))
for controllerType in controllerTypeToPlot:
val = self.runStatistics[controllerType]
plt.bar(barCounter,
val[dataName],
barWidth,
color=self.colorMap[controllerType],
label=controllerType)
barCounter += 1
plt.xticks(index + barWidth / 2.0, controllerTypeToPlot)
plt.subplot(4, 1, 1)
plt.title('run duration')
scatterPlot(runDuration)
# for controllerType, idx in idxMap.iteritems():
# plt.scatter(grid[idx], runDuration[idx], color=self.colorMap[controllerType])
# plt.scatter(runStart[idxDefaultRandom], runDuration[idxDefaultRandom], color='b')
# plt.scatter(runStart[idxQValueController], runDuration[idxQValueController], color='y')
# plt.scatter(runStart[idxDefault], runDuration[idxDefault], color='g')
plt.xlabel('run #')
plt.ylabel('episode duration')
plt.subplot(2, 1, 2)
plt.title('discounted reward')
scatterPlot(discountedReward)
# for key, val in plotData.iteritems():
# plt.scatter(grid[idx], discountedReward[idx], color=self.colorMap[controllerType])
# plt.subplot(3,1,3)
# plt.title("average reward")
# scatterPlot(avgReward)
# for key, val in plotData.iteritems():
# plt.scatter(grid[val['idx']],avgReward[val['idx']], color=val['color'])
# plt.subplot(4,1,4)
# plt.title("average reward no collision penalty")
# scatterPlot(avgRewardNoCollisionPenalty)
# # for key, val in plotData.iteritems():
# # plt.scatter(grid[val['idx']],avgRewardNoCollisionPenalty[val['idx']], color=val['color'])
## plot summary statistics
plt.figure()
plt.subplot(2, 1, 1)
barPlot("duration")
plt.subplot(2, 1, 2)
barPlot("discountedReward")
# plt.subplot(3,1,3)
# barPlot("avgReward")
#
# plt.subplot(4,1,4)
# barPlot("avgRewardNoCollisionPenalty")
plt.show()
def plotMultipleRunData(self,
simList,
toPlot=['duration', 'discountedReward'],
controllerType='learned'):
plt.figure()
numPlots = len(toPlot)
grid = np.arange(len(simList))
def plot(fieldToPlot, plotNum):
plt.subplot(numPlots, 1, plotNum)
plt.title(fieldToPlot)
val = 0 * grid
barWidth = 0.5
barCounter = 0
for idx, sim in enumerate(simList):
value = sim.runStatistics[controllerType][fieldToPlot]
plt.bar(idx, value, barWidth)
counter = 1
for fieldToPlot in toPlot:
plot(fieldToPlot, counter)
counter += 1
plt.show()
def saveToFile(self, filename):
# should also save the run data if it is available, i.e. stateOverTime, rewardOverTime
filename = 'data/' + filename + ".out"
my_shelf = shelve.open(filename, 'n')
my_shelf['options'] = self.options
if self.options['SARSA']['type'] == "discrete":
my_shelf['SARSA_QValues'] = self.Sarsa.QValues
my_shelf['simulationData'] = self.simulationData
my_shelf['stateOverTime'] = self.stateOverTime
my_shelf['raycastData'] = self.raycastData
my_shelf['controlInputData'] = self.controlInputData
my_shelf['emptyQValue'] = self.emptyQValue
my_shelf['numTimesteps'] = self.numTimesteps
my_shelf['idxDict'] = self.idxDict
my_shelf['counter'] = self.counter
my_shelf.close()
@staticmethod
def loadFromFile(filename):
filename = 'data/' + filename + ".out"
sim = Simulator(autoInitialize=False, verbose=False)
my_shelf = shelve.open(filename)
sim.options = my_shelf['options']
sim.initialize()
if sim.options['SARSA']['type'] == "discrete":
sim.Sarsa.QValues = np.array(my_shelf['SARSA_QValues'])
sim.simulationData = my_shelf['simulationData']
# sim.runStatistics = my_shelf['runStatistics']
sim.stateOverTime = np.array(my_shelf['stateOverTime'])
sim.raycastData = np.array(my_shelf['raycastData'])
sim.controlInputData = np.array(my_shelf['controlInputData'])
sim.emptyQValue = np.array(my_shelf['emptyQValue'])
sim.numTimesteps = my_shelf['numTimesteps']
sim.idxDict = my_shelf['idxDict']
sim.counter = my_shelf['counter']
my_shelf.close()
return sim
class Simulator(object):
def __init__(
self,
percentObsDensity=20,
endTime=40,
randomizeControl=False,
nonRandomWorld=False,
circleRadius=0.7,
worldScale=1.0,
supervisedTrainingTime=500,
autoInitialize=True,
verbose=True,
sarsaType="discrete",
):
self.verbose = verbose
self.randomizeControl = randomizeControl
self.startSimTime = time.time()
self.collisionThreshold = 1.3
self.randomSeed = 5
self.Sarsa_numInnerBins = 4
self.Sarsa_numOuterBins = 4
self.Sensor_rayLength = 8
self.sarsaType = sarsaType
self.percentObsDensity = percentObsDensity
self.supervisedTrainingTime = 10
self.learningRandomTime = 10
self.learningEvalTime = 10
self.defaultControllerTime = 10
self.nonRandomWorld = nonRandomWorld
self.circleRadius = circleRadius
self.worldScale = worldScale
# create the visualizer object
self.app = ConsoleApp()
# view = app.createView(useGrid=False)
self.view = self.app.createView(useGrid=False)
self.initializeOptions()
self.initializeColorMap()
if autoInitialize:
self.initialize()
def initializeOptions(self):
self.options = dict()
self.options["Reward"] = dict()
self.options["Reward"]["actionCost"] = 0.1
self.options["Reward"]["collisionPenalty"] = 100.0
self.options["Reward"]["raycastCost"] = 20.0
self.options["SARSA"] = dict()
self.options["SARSA"]["type"] = "discrete"
self.options["SARSA"]["lam"] = 0.7
self.options["SARSA"]["alphaStepSize"] = 0.2
self.options["SARSA"]["useQLearningUpdate"] = False
self.options["SARSA"]["epsilonGreedy"] = 0.2
self.options["SARSA"]["burnInTime"] = 500
self.options["SARSA"]["epsilonGreedyExponent"] = 0.3
self.options["SARSA"]["exponentialDiscountFactor"] = 0.05 # so gamma = e^(-rho*dt)
self.options["SARSA"]["numInnerBins"] = 5
self.options["SARSA"]["numOuterBins"] = 4
self.options["SARSA"]["binCutoff"] = 0.5
self.options["World"] = dict()
self.options["World"]["obstaclesInnerFraction"] = 0.85
self.options["World"]["randomSeed"] = 40
self.options["World"]["percentObsDensity"] = 7.5
self.options["World"]["nonRandomWorld"] = True
self.options["World"]["circleRadius"] = 1.75
self.options["World"]["scale"] = 1.0
self.options["Sensor"] = dict()
self.options["Sensor"]["rayLength"] = 10
self.options["Sensor"]["numRays"] = 20
self.options["Car"] = dict()
self.options["Car"]["velocity"] = 12
self.options["dt"] = 0.05
self.options["runTime"] = dict()
self.options["runTime"]["supervisedTrainingTime"] = 10
self.options["runTime"]["learningRandomTime"] = 10
self.options["runTime"]["learningEvalTime"] = 10
self.options["runTime"]["defaultControllerTime"] = 10
def setDefaultOptions(self):
defaultOptions = dict()
defaultOptions["Reward"] = dict()
defaultOptions["Reward"]["actionCost"] = 0.1
defaultOptions["Reward"]["collisionPenalty"] = 100.0
defaultOptions["Reward"]["raycastCost"] = 20.0
defaultOptions["SARSA"] = dict()
defaultOptions["SARSA"]["type"] = "discrete"
defaultOptions["SARSA"]["lam"] = 0.7
defaultOptions["SARSA"]["alphaStepSize"] = 0.2
defaultOptions["SARSA"]["useQLearningUpdate"] = False
defaultOptions["SARSA"]["useSupervisedTraining"] = True
defaultOptions["SARSA"]["epsilonGreedy"] = 0.2
defaultOptions["SARSA"]["burnInTime"] = 500
defaultOptions["SARSA"]["epsilonGreedyExponent"] = 0.3
defaultOptions["SARSA"]["exponentialDiscountFactor"] = 0.05 # so gamma = e^(-rho*dt)
defaultOptions["SARSA"]["numInnerBins"] = 5
defaultOptions["SARSA"]["numOuterBins"] = 4
defaultOptions["SARSA"]["binCutoff"] = 0.5
defaultOptions["SARSA"]["forceDriveStraight"] = True
defaultOptions["World"] = dict()
defaultOptions["World"]["obstaclesInnerFraction"] = 0.85
defaultOptions["World"]["randomSeed"] = 40
defaultOptions["World"]["percentObsDensity"] = 7.5
defaultOptions["World"]["nonRandomWorld"] = True
defaultOptions["World"]["circleRadius"] = 1.75
defaultOptions["World"]["scale"] = 1.0
defaultOptions["Sensor"] = dict()
defaultOptions["Sensor"]["rayLength"] = 10
defaultOptions["Sensor"]["numRays"] = 20
defaultOptions["Car"] = dict()
defaultOptions["Car"]["velocity"] = 12
defaultOptions["dt"] = 0.05
defaultOptions["runTime"] = dict()
defaultOptions["runTime"]["supervisedTrainingTime"] = 10
defaultOptions["runTime"]["learningRandomTime"] = 10
defaultOptions["runTime"]["learningEvalTime"] = 10
defaultOptions["runTime"]["defaultControllerTime"] = 10
for k in defaultOptions:
self.options.setdefault(k, defaultOptions[k])
for k in defaultOptions:
if not isinstance(defaultOptions[k], dict):
continue
for j in defaultOptions[k]:
self.options[k].setdefault(j, defaultOptions[k][j])
def initializeColorMap(self):
self.colorMap = dict()
self.colorMap["defaultRandom"] = [0, 0, 1]
self.colorMap["learnedRandom"] = [1.0, 0.54901961, 0.0] # this is orange
self.colorMap["learned"] = [0.58039216, 0.0, 0.82745098] # this is yellow
self.colorMap["default"] = [0, 1, 0]
def initialize(self):
self.dt = self.options["dt"]
self.controllerTypeOrder = ["defaultRandom", "learnedRandom", "learned", "default"]
self.setDefaultOptions()
self.Sensor = SensorObj(
rayLength=self.options["Sensor"]["rayLength"], numRays=self.options["Sensor"]["numRays"]
)
self.Controller = ControllerObj(self.Sensor)
self.Car = CarPlant(controller=self.Controller, velocity=self.options["Car"]["velocity"])
self.Reward = Reward(
self.Sensor,
collisionThreshold=self.collisionThreshold,
actionCost=self.options["Reward"]["actionCost"],
collisionPenalty=self.options["Reward"]["collisionPenalty"],
raycastCost=self.options["Reward"]["raycastCost"],
)
self.setSARSA()
# create the things needed for simulation
om.removeFromObjectModel(om.findObjectByName("world"))
self.world = World.buildCircleWorld(
percentObsDensity=self.options["World"]["percentObsDensity"],
circleRadius=self.options["World"]["circleRadius"],
nonRandom=self.options["World"]["nonRandomWorld"],
scale=self.options["World"]["scale"],
randomSeed=self.options["World"]["randomSeed"],
obstaclesInnerFraction=self.options["World"]["obstaclesInnerFraction"],
)
om.removeFromObjectModel(om.findObjectByName("robot"))
self.robot, self.frame = World.buildRobot()
self.locator = World.buildCellLocator(self.world.visObj.polyData)
self.Sensor.setLocator(self.locator)
self.frame = self.robot.getChildFrame()
self.frame.setProperty("Scale", 3)
self.frame.setProperty("Edit", True)
self.frame.widget.HandleRotationEnabledOff()
rep = self.frame.widget.GetRepresentation()
rep.SetTranslateAxisEnabled(2, False)
rep.SetRotateAxisEnabled(0, False)
rep.SetRotateAxisEnabled(1, False)
self.supervisedTrainingTime = self.options["runTime"]["supervisedTrainingTime"]
self.learningRandomTime = self.options["runTime"]["learningRandomTime"]
self.learningEvalTime = self.options["runTime"]["learningEvalTime"]
self.defaultControllerTime = self.options["runTime"]["defaultControllerTime"]
self.Car.setFrame(self.frame)
print "Finished initialization"
def setSARSA(self, type=None):
if type is None:
type = self.options["SARSA"]["type"]
if type == "discrete":
self.Sarsa = SARSADiscrete(
sensorObj=self.Sensor,
actionSet=self.Controller.actionSet,
collisionThreshold=self.collisionThreshold,
alphaStepSize=self.options["SARSA"]["alphaStepSize"],
useQLearningUpdate=self.options["SARSA"]["useQLearningUpdate"],
lam=self.options["SARSA"]["lam"],
numInnerBins=self.options["SARSA"]["numInnerBins"],
numOuterBins=self.options["SARSA"]["numOuterBins"],
binCutoff=self.options["SARSA"]["binCutoff"],
burnInTime=self.options["SARSA"]["burnInTime"],
epsilonGreedy=self.options["SARSA"]["epsilonGreedy"],
epsilonGreedyExponent=self.options["SARSA"]["epsilonGreedyExponent"],
forceDriveStraight=self.options["SARSA"]["forceDriveStraight"],
)
elif type == "continuous":
self.Sarsa = SARSAContinuous(
sensorObj=self.Sensor,
actionSet=self.Controller.actionSet,
lam=self.options["SARSA"]["lam"],
alphaStepSize=self.options["SARSA"]["alphaStepSize"],
collisionThreshold=self.collisionThreshold,
burnInTime=self.options["SARSA"]["burnInTime"],
epsilonGreedy=self.options["SARSA"]["epsilonGreedy"],
epsilonGreedyExponent=self.options["SARSA"]["epsilonGreedyExponent"],
)
else:
raise ValueError("sarsa type must be either discrete or continuous")
def runSingleSimulation(self, updateQValues=True, controllerType="default", simulationCutoff=None):
if self.verbose:
print "using QValue based controller = ", useQValueController
self.setCollisionFreeInitialState()
currentCarState = np.copy(self.Car.state)
nextCarState = np.copy(self.Car.state)
self.setRobotFrameState(currentCarState[0], currentCarState[1], currentCarState[2])
currentRaycast = self.Sensor.raycastAll(self.frame)
nextRaycast = np.zeros(self.Sensor.numRays)
self.Sarsa.resetElibilityTraces()
# record the reward data
runData = dict()
reward = 0
discountedReward = 0
avgReward = 0
startIdx = self.counter
while self.counter < self.numTimesteps - 1:
idx = self.counter
currentTime = self.t[idx]
self.stateOverTime[idx, :] = currentCarState
x = self.stateOverTime[idx, 0]
y = self.stateOverTime[idx, 1]
theta = self.stateOverTime[idx, 2]
self.setRobotFrameState(x, y, theta)
# self.setRobotState(currentCarState[0], currentCarState[1], currentCarState[2])
currentRaycast = self.Sensor.raycastAll(self.frame)
self.raycastData[idx, :] = currentRaycast
S_current = (currentCarState, currentRaycast)
if controllerType not in self.colorMap.keys():
print
raise ValueError("controller of type " + controllerType + " not supported")
if controllerType in ["default", "defaultRandom"]:
if controllerType == "defaultRandom":
controlInput, controlInputIdx = self.Controller.computeControlInput(
currentCarState, currentTime, self.frame, raycastDistance=currentRaycast, randomize=True
)
else:
controlInput, controlInputIdx = self.Controller.computeControlInput(
currentCarState, currentTime, self.frame, raycastDistance=currentRaycast, randomize=False
)
if controllerType in ["learned", "learnedRandom"]:
if controllerType == "learned":
randomizeControl = False
else:
randomizeControl = True
counterForGreedyDecay = self.counter - self.idxDict["learnedRandom"]
controlInput, controlInputIdx, emptyQValue = self.Sarsa.computeGreedyControlPolicy(
S_current, counter=counterForGreedyDecay, randomize=randomizeControl
)
self.emptyQValue[idx] = emptyQValue
if emptyQValue and self.options["SARSA"]["useSupervisedTraining"]:
controlInput, controlInputIdx = self.Controller.computeControlInput(
currentCarState, currentTime, self.frame, raycastDistance=currentRaycast, randomize=False
)
self.controlInputData[idx] = controlInput
nextCarState = self.Car.simulateOneStep(controlInput=controlInput, dt=self.dt)
# want to compute nextRaycast so we can do the SARSA algorithm
x = nextCarState[0]
y = nextCarState[1]
theta = nextCarState[2]
self.setRobotFrameState(x, y, theta)
nextRaycast = self.Sensor.raycastAll(self.frame)
# Compute the next control input
S_next = (nextCarState, nextRaycast)
if controllerType in ["default", "defaultRandom"]:
if controllerType == "defaultRandom":
nextControlInput, nextControlInputIdx = self.Controller.computeControlInput(
nextCarState, currentTime, self.frame, raycastDistance=nextRaycast, randomize=True
)
else:
nextControlInput, nextControlInputIdx = self.Controller.computeControlInput(
nextCarState, currentTime, self.frame, raycastDistance=nextRaycast, randomize=False
)
if controllerType in ["learned", "learnedRandom"]:
if controllerType == "learned":
randomizeControl = False
else:
randomizeControl = True
counterForGreedyDecay = self.counter - self.idxDict["learnedRandom"]
nextControlInput, nextControlInputIdx, emptyQValue = self.Sarsa.computeGreedyControlPolicy(
S_next, counter=counterForGreedyDecay, randomize=randomizeControl
)
if emptyQValue and self.options["SARSA"]["useSupervisedTraining"]:
nextControlInput, nextControlInputIdx = self.Controller.computeControlInput(
nextCarState, currentTime, self.frame, raycastDistance=nextRaycast, randomize=False
)
# if useQValueController:
# nextControlInput, nextControlInputIdx, emptyQValue = self.Sarsa.computeGreedyControlPolicy(S_next)
#
# if not useQValueController or emptyQValue:
# nextControlInput, nextControlInputIdx = self.Controller.computeControlInput(nextCarState, currentTime, self.frame,
# raycastDistance=nextRaycast)
# compute the reward
reward = self.Reward.computeReward(S_next, controlInput)
self.rewardData[idx] = reward
discountedReward += self.Sarsa.gamma ** (self.counter - startIdx) * reward
avgReward += reward
###### SARSA update
if updateQValues:
self.Sarsa.sarsaUpdate(S_current, controlInputIdx, reward, S_next, nextControlInputIdx)
# bookkeeping
currentCarState = nextCarState
currentRaycast = nextRaycast
self.counter += 1
# break if we are in collision
if self.checkInCollision(nextRaycast):
if self.verbose:
print "Had a collision, terminating simulation"
break
if self.counter >= simulationCutoff:
break
# fill in the last state by hand
self.stateOverTime[self.counter, :] = currentCarState
self.raycastData[self.counter, :] = currentRaycast
# return the total reward
avgRewardNoCollisionPenalty = avgReward - reward
avgReward = avgReward * 1.0 / max(1, self.counter - startIdx)
avgRewardNoCollisionPenalty = avgRewardNoCollisionPenalty * 1.0 / max(1, self.counter - startIdx)
# this extra multiplication is so that it is in the same "units" as avgReward
runData["discountedReward"] = discountedReward * (1 - self.Sarsa.gamma)
runData["avgReward"] = avgReward
runData["avgRewardNoCollisionPenalty"] = avgRewardNoCollisionPenalty
# this just makes sure we don't get stuck in an infinite loop.
if startIdx == self.counter:
self.counter += 1
return runData
def setNumpyRandomSeed(self, seed=1):
np.random.seed(seed)
def runBatchSimulation(self, endTime=None, dt=0.05):
# for use in playback
self.dt = self.options["dt"]
self.Sarsa.setDiscountFactor(dt)
self.endTime = (
self.supervisedTrainingTime + self.learningRandomTime + self.learningEvalTime + self.defaultControllerTime
)
self.t = np.arange(0.0, self.endTime, dt)
maxNumTimesteps = np.size(self.t)
self.stateOverTime = np.zeros((maxNumTimesteps + 1, 3))
self.raycastData = np.zeros((maxNumTimesteps + 1, self.Sensor.numRays))
self.controlInputData = np.zeros(maxNumTimesteps + 1)
self.rewardData = np.zeros(maxNumTimesteps + 1)
self.emptyQValue = np.zeros(maxNumTimesteps + 1, dtype="bool")
self.numTimesteps = maxNumTimesteps
self.controllerTypeOrder = ["defaultRandom", "learnedRandom", "learned", "default"]
self.counter = 0
self.simulationData = []
self.initializeStatusBar()
self.idxDict = dict()
numRunsCounter = 0
# three while loops for different phases of simulation, supervisedTraining, learning, default
self.idxDict["defaultRandom"] = self.counter
loopStartIdx = self.counter
simCutoff = min(loopStartIdx + self.supervisedTrainingTime / dt, self.numTimesteps)
while (self.counter - loopStartIdx < self.supervisedTrainingTime / dt) and self.counter < self.numTimesteps:
self.printStatusBar()
useQValueController = False
startIdx = self.counter
runData = self.runSingleSimulation(
updateQValues=True, controllerType="defaultRandom", simulationCutoff=simCutoff
)
runData["startIdx"] = startIdx
runData["controllerType"] = "defaultRandom"
runData["duration"] = self.counter - runData["startIdx"]
runData["endIdx"] = self.counter
runData["runNumber"] = numRunsCounter
numRunsCounter += 1
self.simulationData.append(runData)
self.idxDict["learnedRandom"] = self.counter
loopStartIdx = self.counter
simCutoff = min(loopStartIdx + self.learningRandomTime / dt, self.numTimesteps)
while (self.counter - loopStartIdx < self.learningRandomTime / dt) and self.counter < self.numTimesteps:
self.printStatusBar()
startIdx = self.counter
runData = self.runSingleSimulation(
updateQValues=True, controllerType="learnedRandom", simulationCutoff=simCutoff
)
runData["startIdx"] = startIdx
runData["controllerType"] = "learnedRandom"
runData["duration"] = self.counter - runData["startIdx"]
runData["endIdx"] = self.counter
runData["runNumber"] = numRunsCounter
numRunsCounter += 1
self.simulationData.append(runData)
self.idxDict["learned"] = self.counter
loopStartIdx = self.counter
simCutoff = min(loopStartIdx + self.learningEvalTime / dt, self.numTimesteps)
while (self.counter - loopStartIdx < self.learningEvalTime / dt) and self.counter < self.numTimesteps:
self.printStatusBar()
startIdx = self.counter
runData = self.runSingleSimulation(
updateQValues=False, controllerType="learned", simulationCutoff=simCutoff
)
runData["startIdx"] = startIdx
runData["controllerType"] = "learned"
runData["duration"] = self.counter - runData["startIdx"]
runData["endIdx"] = self.counter
runData["runNumber"] = numRunsCounter
numRunsCounter += 1
self.simulationData.append(runData)
self.idxDict["default"] = self.counter
loopStartIdx = self.counter
simCutoff = min(loopStartIdx + self.defaultControllerTime / dt, self.numTimesteps)
while (self.counter - loopStartIdx < self.defaultControllerTime / dt) and self.counter < self.numTimesteps - 1:
self.printStatusBar()
startIdx = self.counter
runData = self.runSingleSimulation(
updateQValues=False, controllerType="default", simulationCutoff=simCutoff
)
runData["startIdx"] = startIdx
runData["controllerType"] = "default"
runData["duration"] = self.counter - runData["startIdx"]
runData["endIdx"] = self.counter
runData["runNumber"] = numRunsCounter
numRunsCounter += 1
self.simulationData.append(runData)
# BOOKKEEPING
# truncate stateOverTime, raycastData, controlInputs to be the correct size
self.numTimesteps = self.counter + 1
self.stateOverTime = self.stateOverTime[0 : self.counter + 1, :]
self.raycastData = self.raycastData[0 : self.counter + 1, :]
self.controlInputData = self.controlInputData[0 : self.counter + 1]
self.rewardData = self.rewardData[0 : self.counter + 1]
self.endTime = 1.0 * self.counter / self.numTimesteps * self.endTime
def initializeStatusBar(self):
self.numTicks = 10
self.nextTickComplete = 1.0 / float(self.numTicks)
self.nextTickIdx = 1
print "Simulation percentage complete: (", self.numTicks, " # is complete)"
def printStatusBar(self):
fractionDone = float(self.counter) / float(self.numTimesteps)
if fractionDone > self.nextTickComplete:
self.nextTickIdx += 1
self.nextTickComplete += 1.0 / self.numTicks
timeSoFar = time.time() - self.startSimTime
estimatedTimeLeft_sec = (1 - fractionDone) * timeSoFar / fractionDone
estimatedTimeLeft_minutes = estimatedTimeLeft_sec / 60.0
print "#" * self.nextTickIdx, "-" * (
self.numTicks - self.nextTickIdx
), "estimated", estimatedTimeLeft_minutes, "minutes left"
def setCollisionFreeInitialState(self):
tol = 5
while True:
x = np.random.uniform(self.world.Xmin + tol, self.world.Xmax - tol, 1)[0]
y = np.random.uniform(self.world.Ymin + tol, self.world.Ymax - tol, 1)[0]
theta = np.random.uniform(0, 2 * np.pi, 1)[0]
self.Car.setCarState(x, y, theta)
self.setRobotFrameState(x, y, theta)
if not self.checkInCollision():
break
# if self.checkInCollision():
# print "IN COLLISION"
# else:
# print "COLLISION FREE"
return x, y, theta
def setupPlayback(self):
self.timer = TimerCallback(targetFps=30)
self.timer.callback = self.tick
playButtonFps = 1.0 / self.dt
print "playButtonFPS", playButtonFps
self.playTimer = TimerCallback(targetFps=playButtonFps)
self.playTimer.callback = self.playTimerCallback
self.sliderMovedByPlayTimer = False
panel = QtGui.QWidget()
l = QtGui.QHBoxLayout(panel)
playButton = QtGui.QPushButton("Play/Pause")
playButton.connect("clicked()", self.onPlayButton)
slider = QtGui.QSlider(QtCore.Qt.Horizontal)
slider.connect("valueChanged(int)", self.onSliderChanged)
self.sliderMax = self.numTimesteps
slider.setMaximum(self.sliderMax)
self.slider = slider
l.addWidget(playButton)
l.addWidget(slider)
w = QtGui.QWidget()
l = QtGui.QVBoxLayout(w)
l.addWidget(self.view)
l.addWidget(panel)
w.showMaximized()
self.frame.connectFrameModified(self.updateDrawIntersection)
self.updateDrawIntersection(self.frame)
applogic.resetCamera(viewDirection=[0.2, 0, -1])
self.view.showMaximized()
self.view.raise_()
panel = screengrabberpanel.ScreenGrabberPanel(self.view)
panel.widget.show()
elapsed = time.time() - self.startSimTime
simRate = self.counter / elapsed
print "Total run time", elapsed
print "Ticks (Hz)", simRate
print "Number of steps taken", self.counter
self.app.start()
def run(self, launchApp=True):
self.counter = 1
self.runBatchSimulation()
# self.Sarsa.plotWeights()
if launchApp:
self.setupPlayback()
def updateDrawIntersection(self, frame):
origin = np.array(frame.transform.GetPosition())
# print "origin is now at", origin
d = DebugData()
sliderIdx = self.slider.value
controllerType = self.getControllerTypeFromCounter(sliderIdx)
colorMaxRange = self.colorMap[controllerType]
# if the QValue was empty then color it green
if self.emptyQValue[sliderIdx]:
colorMaxRange = [1, 1, 0] # this is yellow
for i in xrange(self.Sensor.numRays):
ray = self.Sensor.rays[:, i]
rayTransformed = np.array(frame.transform.TransformNormal(ray))
# print "rayTransformed is", rayTransformed
intersection = self.Sensor.raycast(self.locator, origin, origin + rayTransformed * self.Sensor.rayLength)
if intersection is not None:
d.addLine(origin, intersection, color=[1, 0, 0])
else:
d.addLine(origin, origin + rayTransformed * self.Sensor.rayLength, color=colorMaxRange)
vis.updatePolyData(d.getPolyData(), "rays", colorByName="RGB255")
# camera = self.view.camera()
# camera.SetFocalPoint(frame.transform.GetPosition())
# camera.SetPosition(frame.transform.TransformPoint((-30,0,10)))
def getControllerTypeFromCounter(self, counter):
name = self.controllerTypeOrder[0]
for controllerType in self.controllerTypeOrder[1:]:
if counter >= self.idxDict[controllerType]:
name = controllerType
return name
def setRobotFrameState(self, x, y, theta):
t = vtk.vtkTransform()
t.Translate(x, y, 0.0)
t.RotateZ(np.degrees(theta))
self.robot.getChildFrame().copyFrame(t)
# returns true if we are in collision
def checkInCollision(self, raycastDistance=None):
if raycastDistance is None:
self.setRobotFrameState(self.Car.state[0], self.Car.state[1], self.Car.state[2])
raycastDistance = self.Sensor.raycastAll(self.frame)
if np.min(raycastDistance) < self.collisionThreshold:
return True
else:
return False
def tick(self):
# print timer.elapsed
# simulate(t.elapsed)
x = np.sin(time.time())
y = np.cos(time.time())
self.setRobotFrameState(x, y, 0.0)
if (time.time() - self.playTime) > self.endTime:
self.playTimer.stop()
def tick2(self):
newtime = time.time() - self.playTime
print time.time() - self.playTime
x = np.sin(newtime)
y = np.cos(newtime)
self.setRobotFrameState(x, y, 0.0)
# just increment the slider, stop the timer if we get to the end
def playTimerCallback(self):
self.sliderMovedByPlayTimer = True
currentIdx = self.slider.value
nextIdx = currentIdx + 1
self.slider.setSliderPosition(nextIdx)
if currentIdx >= self.sliderMax:
print "reached end of tape, stopping playTime"
self.playTimer.stop()
def onSliderChanged(self, value):
if not self.sliderMovedByPlayTimer:
self.playTimer.stop()
numSteps = len(self.stateOverTime)
idx = int(np.floor(numSteps * (1.0 * value / self.sliderMax)))
idx = min(idx, numSteps - 1)
x, y, theta = self.stateOverTime[idx]
self.setRobotFrameState(x, y, theta)
self.sliderMovedByPlayTimer = False
def onPlayButton(self):
if self.playTimer.isActive():
self.onPauseButton()
return
print "play"
self.playTimer.start()
self.playTime = time.time()
def onPauseButton(self):
print "pause"
self.playTimer.stop()
def computeRunStatistics(self):
numRuns = len(self.simulationData)
runStart = np.zeros(numRuns)
runDuration = np.zeros(numRuns)
grid = np.arange(1, numRuns + 1)
discountedReward = np.zeros(numRuns)
avgReward = np.zeros(numRuns)
avgRewardNoCollisionPenalty = np.zeros(numRuns)
idxMap = dict()
for controllerType, color in self.colorMap.iteritems():
idxMap[controllerType] = np.zeros(numRuns, dtype=bool)
for idx, val in enumerate(self.simulationData):
runStart[idx] = val["startIdx"]
runDuration[idx] = val["duration"]
discountedReward[idx] = val["discountedReward"]
avgReward[idx] = val["avgReward"]
avgRewardNoCollisionPenalty[idx] = val["avgRewardNoCollisionPenalty"]
controllerType = val["controllerType"]
idxMap[controllerType][idx] = True
def plotRunData(self, controllerTypeToPlot=None, showPlot=True, onlyLearned=False):
if controllerTypeToPlot == None:
controllerTypeToPlot = self.colorMap.keys()
if onlyLearned:
controllerTypeToPlot = ["learnedRandom", "learned"]
numRuns = len(self.simulationData)
runStart = np.zeros(numRuns)
runDuration = np.zeros(numRuns)
grid = np.arange(1, numRuns + 1)
discountedReward = np.zeros(numRuns)
avgReward = np.zeros(numRuns)
avgRewardNoCollisionPenalty = np.zeros(numRuns)
idxMap = dict()
for controllerType, color in self.colorMap.iteritems():
idxMap[controllerType] = np.zeros(numRuns, dtype=bool)
for idx, val in enumerate(self.simulationData):
runStart[idx] = val["startIdx"]
runDuration[idx] = val["duration"]
discountedReward[idx] = val["discountedReward"]
avgReward[idx] = val["avgReward"]
avgRewardNoCollisionPenalty[idx] = val["avgRewardNoCollisionPenalty"]
controllerType = val["controllerType"]
idxMap[controllerType][idx] = True
# usedQValueController[idx] = (val['controllerType'] == "QValue")
# usedDefaultController[idx] = (val['controllerType'] == "default")
# usedDefaultRandomController[idx] = (val['controllerType'] == "defaultRandom")
# usedQValueRandomController[idx] = (val['controllerType'] == "QValueRandom")
self.runStatistics = dict()
dataMap = {
"duration": runDuration,
"discountedReward": discountedReward,
"avgReward": avgReward,
"avgRewardNoCollisionPenalty": avgRewardNoCollisionPenalty,
}
def computeRunStatistics(dataMap):
for controllerType, idx in idxMap.iteritems():
d = dict()
for dataName, dataSet in dataMap.iteritems():
# average the appropriate values in dataset
d[dataName] = np.sum(dataSet[idx]) / (1.0 * np.size(dataSet[idx]))
self.runStatistics[controllerType] = d
computeRunStatistics(dataMap)
if not showPlot:
return
plt.figure()
#
# idxDefaultRandom = np.where(usedDefaultRandomController==True)[0]
# idxQValueController = np.where(usedQValueController==True)[0]
# idxQValueControllerRandom = np.where(usedQValueControllerRandom==True)[0]
# idxDefault = np.where(usedDefaultController==True)[0]
#
# plotData = dict()
# plotData['defaultRandom'] = {'idx': idxDefaultRandom, 'color': 'b'}
# plotData['QValue'] = {'idx': idxQValueController, 'color': 'y'}
# plotData['default'] = {'idx': idxDefault, 'color': 'g'}
def scatterPlot(dataToPlot):
for controllerType in controllerTypeToPlot:
idx = idxMap[controllerType]
plt.scatter(grid[idx], dataToPlot[idx], color=self.colorMap[controllerType])
def barPlot(dataName):
plt.title(dataName)
barWidth = 0.5
barCounter = 0
index = np.arange(len(controllerTypeToPlot))
for controllerType in controllerTypeToPlot:
val = self.runStatistics[controllerType]
plt.bar(barCounter, val[dataName], barWidth, color=self.colorMap[controllerType], label=controllerType)
barCounter += 1
plt.xticks(index + barWidth / 2.0, controllerTypeToPlot)
plt.subplot(4, 1, 1)
plt.title("run duration")
scatterPlot(runDuration)
# for controllerType, idx in idxMap.iteritems():
# plt.scatter(grid[idx], runDuration[idx], color=self.colorMap[controllerType])
# plt.scatter(runStart[idxDefaultRandom], runDuration[idxDefaultRandom], color='b')
# plt.scatter(runStart[idxQValueController], runDuration[idxQValueController], color='y')
# plt.scatter(runStart[idxDefault], runDuration[idxDefault], color='g')
plt.xlabel("run #")
plt.ylabel("episode duration")
plt.subplot(2, 1, 2)
plt.title("discounted reward")
scatterPlot(discountedReward)
# for key, val in plotData.iteritems():
# plt.scatter(grid[idx], discountedReward[idx], color=self.colorMap[controllerType])
# plt.subplot(3,1,3)
# plt.title("average reward")
# scatterPlot(avgReward)
# for key, val in plotData.iteritems():
# plt.scatter(grid[val['idx']],avgReward[val['idx']], color=val['color'])
# plt.subplot(4,1,4)
# plt.title("average reward no collision penalty")
# scatterPlot(avgRewardNoCollisionPenalty)
# # for key, val in plotData.iteritems():
# # plt.scatter(grid[val['idx']],avgRewardNoCollisionPenalty[val['idx']], color=val['color'])
## plot summary statistics
plt.figure()
plt.subplot(2, 1, 1)
barPlot("duration")
plt.subplot(2, 1, 2)
barPlot("discountedReward")
# plt.subplot(3,1,3)
# barPlot("avgReward")
#
# plt.subplot(4,1,4)
# barPlot("avgRewardNoCollisionPenalty")
plt.show()
def plotMultipleRunData(self, simList, toPlot=["duration", "discountedReward"], controllerType="learned"):
plt.figure()
numPlots = len(toPlot)
grid = np.arange(len(simList))
def plot(fieldToPlot, plotNum):
plt.subplot(numPlots, 1, plotNum)
plt.title(fieldToPlot)
val = 0 * grid
barWidth = 0.5
barCounter = 0
for idx, sim in enumerate(simList):
value = sim.runStatistics[controllerType][fieldToPlot]
plt.bar(idx, value, barWidth)
counter = 1
for fieldToPlot in toPlot:
plot(fieldToPlot, counter)
counter += 1
plt.show()
def saveToFile(self, filename):
# should also save the run data if it is available, i.e. stateOverTime, rewardOverTime
filename = "data/" + filename + ".out"
my_shelf = shelve.open(filename, "n")
my_shelf["options"] = self.options
if self.options["SARSA"]["type"] == "discrete":
my_shelf["SARSA_QValues"] = self.Sarsa.QValues
my_shelf["simulationData"] = self.simulationData
my_shelf["stateOverTime"] = self.stateOverTime
my_shelf["raycastData"] = self.raycastData
my_shelf["controlInputData"] = self.controlInputData
my_shelf["emptyQValue"] = self.emptyQValue
my_shelf["numTimesteps"] = self.numTimesteps
my_shelf["idxDict"] = self.idxDict
my_shelf["counter"] = self.counter
my_shelf.close()
@staticmethod
def loadFromFile(filename):
filename = "data/" + filename + ".out"
sim = Simulator(autoInitialize=False, verbose=False)
my_shelf = shelve.open(filename)
sim.options = my_shelf["options"]
sim.initialize()
if sim.options["SARSA"]["type"] == "discrete":
sim.Sarsa.QValues = np.array(my_shelf["SARSA_QValues"])
sim.simulationData = my_shelf["simulationData"]
# sim.runStatistics = my_shelf['runStatistics']
sim.stateOverTime = np.array(my_shelf["stateOverTime"])
sim.raycastData = np.array(my_shelf["raycastData"])
sim.controlInputData = np.array(my_shelf["controlInputData"])
sim.emptyQValue = np.array(my_shelf["emptyQValue"])
sim.numTimesteps = my_shelf["numTimesteps"]
sim.idxDict = my_shelf["idxDict"]
sim.counter = my_shelf["counter"]
my_shelf.close()
return sim
