class SmartCar(ShowBase):
def __init__(self):
ShowBase.__init__(self)
# Override defaults
self.disableMouse()
self.setBackgroundColor(VBase3(160, 200, 150) / 255.0)
self.setFrameRateMeter(True)
# Lights
dlight = DirectionalLight("dlight")
dlnp = self.render.attachNewNode(dlight)
dlnp.setHpr(180.0, -70.0, 0)
self.render.setLight(dlnp)
alight = AmbientLight("alight")
alnp = self.render.attachNewNode(alight)
alight.setColor(VBase4(0.4, 0.4, 0.4, 1))
self.render.setLight(alnp)
# Collision traverser
self.cTrav = CollisionTraverser("collisionTraverser")
# Collision handlers
self.carCollisionHandler = CollisionHandlerEvent()
self.carCollisionHandler.addInPattern("%fn-into-%in")
# Camera controls
self.cameraController = CameraController(self, 400, math.pi / 4.0, math.pi / 4.0)
# Load the track
self.track = self.loader.loadModel("models/trackValencia")
checkpointsCollision = self.track.find("checkpoints").node()
checkpointsCollision.setIntoCollideMask(BitMask32(0xF0))
self.numCheckpoints = checkpointsCollision.getNumSolids()
self.track.reparentTo(self.render)
# Load the car
self.car = NeuralNetworkCar(self)
self.cameraController.follow(self.car.getNodePath())
# Reposition the car
# self.car.getNodePath().setH(180.0)
# Register car collisions with track
self.cTrav.addCollider(self.car.carCollider, self.carCollisionHandler)
self.accept("carCollider-into-trackCollision", self.car.onCrash)
self.accept("carCollider-into-checkpoints", self.car.onCheckpoint)
class SmartCar(ShowBase):
def __init__(self):
ShowBase.__init__(self)
# Override defaults
self.disableMouse()
self.setBackgroundColor(VBase3(160, 200, 150) / 255.0)
self.setFrameRateMeter(True)
# Lights
dlight = DirectionalLight("dlight")
dlnp = self.render.attachNewNode(dlight)
dlnp.setHpr(180.0, -70.0, 0)
self.render.setLight(dlnp)
alight = AmbientLight("alight")
alnp = self.render.attachNewNode(alight)
alight.setColor(VBase4(0.4, 0.4, 0.4, 1))
self.render.setLight(alnp)
# Collision traverser
self.cTrav = CollisionTraverser("collisionTraverser")
#self.cTrav.showCollisions(self.render)
# Collision handlers
self.carCollisionHandler = CollisionHandlerEvent()
self.carCollisionHandler.addInPattern("%fn-into-%in")
# Camera controls
self.cameraController = CameraController(self, 250, math.pi / 4.0, math.pi / 4.0)
#self.cameraController = CameraController(self, 300, -math.pi, math.pi / 4.0)
# Load the track
self.track = self.loader.loadModel("models/trackMotegi")
checkpointsCollision = self.track.find("checkpoints").node()
checkpointsCollision.setIntoCollideMask(BitMask32(0xF0))
self.numCheckpoints = checkpointsCollision.getNumSolids()
self.track.reparentTo(self.render)
# Load the car
#self.car = KeyboardCar(self)
self.car = NeuralNetworkCar(self)
self.cameraController.follow(self.car.getNodePath())
#self.cameraController.setTarget(self.car.getNodePath())
# Reposition the car
#self.car.getNodePath().setH(180.0)
# Register car collisions with track
self.cTrav.addCollider(self.car.carCollider, self.carCollisionHandler)
self.accept("carCollider-into-trackCollision", self.car.onCrash)
self.accept("carCollider-into-checkpoints", self.car.onCheckpoint)
# State logger
self.loggingActive = False
self.log = []
self.accept("l", self.toggleStateLogger)
def toggleStateLogger(self):
if not self.loggingActive:
print "LOG: started."
self.taskMgr.add(self.stateLogger, "StateLoggerTask")
else:
self.taskMgr.remove("StateLoggerTask")
self.writeLog("data/log.csv")
self.loggingActive = not self.loggingActive
def stateLogger(self, task):
row = [self.car.speed, self.car.steerAngle]
row.extend(self.car.sensorDistances)
row.append(float(self.car.arrowUpDown))
row.append(float(self.car.arrowLeftDown))
row.append(float(self.car.arrowRightDown))
self.log.append(row)
return task.again
def writeLog(self, filename):
with open(filename, 'wb') as file:
wr = csv.writer(file)
wr.writerows(self.log)
print "LOG: written."
def __init__(self):
ShowBase.__init__(self)
# Override defaults
self.disableMouse()
self.setBackgroundColor(VBase3(160, 200, 150) / 255.0)
self.setFrameRateMeter(True)
# Lights
dlight = DirectionalLight("dlight")
dlnp = self.render.attachNewNode(dlight)
dlnp.setHpr(180.0, -70.0, 0)
self.render.setLight(dlnp)
alight = AmbientLight("alight")
alnp = self.render.attachNewNode(alight)
alight.setColor(VBase4(0.4, 0.4, 0.4, 1))
self.render.setLight(alnp)
# Collision traverser
self.cTrav = CollisionTraverser("collisionTraverser")
# self.cTrav.showCollisions(self.render)
# Collision handlers
self.carCollisionHandler = CollisionHandlerEvent()
self.carCollisionHandler.addInPattern("%fn-into-%in")
# Camera controls
self.cameraController = CameraController(self, 600, math.radians(45),
math.radians(60))
# Load the track
self.track = self.loader.loadModel("models/trackMotegi")
# self.track = self.loader.loadModel("models/trackValencia")
checkpointsCollision = self.track.find("checkpoints").node()
checkpointsCollision.setIntoCollideMask(BitMask32(0xF0))
self.numCheckpoints = checkpointsCollision.getNumSolids()
self.track.reparentTo(self.render)
# Enemy cars
enemyColor = LColor(204 / 255.0, 72 / 255.0, 63 / 255.0, 1.0)
self.enemyPos = [(-150, -110, 60), (113, -233, 45), (-255, -264, 20),
(-364, -237, -90), (-204, -46, 230)]
self.enemyCars = []
for pos in self.enemyPos:
car = NeuralNetworkCar(self)
car.getNodePath().setColor(enemyColor)
car.getNodePath().setX(pos[0])
car.getNodePath().setY(pos[1])
car.getNodePath().setH(pos[2])
self.enemyCars.append(car)
# Neuroevolution
# self.loggingActive = False
self.inputLayerSize = 9
self.hiddenLayer1Size = 5
self.hiddenLayer2Size = 5
self.numLabels = 2
self.thetaSizes = [(self.hiddenLayer1Size, self.inputLayerSize + 1),
(self.hiddenLayer2Size, self.hiddenLayer1Size + 1),
(self.numLabels, self.hiddenLayer2Size + 1)]
self.generationSize = 15
self.weightInit = 0.12
self.replaceRatio = 0.02
self.scaleRatio = 0.02
self.addRatio = 0.02
self.generationCount = 1
self.cars = []
for i in range(self.generationSize):
car = NeuroevolutionCar(self, i, self.inputLayerSize)
# Register car collisions with track
self.cTrav.addCollider(car.carCollider, self.carCollisionHandler)
self.accept("carCollider{}-into-carCollider".format(i),
car.onCrash)
self.accept("carCollider{}-into-trackCollision".format(i),
car.onCrash)
self.accept("carCollider{}-into-checkpoints".format(i),
car.onCheckpoint)
self.cars.append(car)
# Initial generation
np.random.seed(0)
for i in range(self.generationSize):
theta1 = self.randWeights(self.thetaSizes[0], self.weightInit)
theta2 = self.randWeights(self.thetaSizes[1], self.weightInit)
theta3 = self.randWeights(self.thetaSizes[2], self.weightInit)
self.cars[i].startSimulation(theta1, theta2, theta3)
# Run learning task
self.taskMgr.add(self.neuroevolution, "NeuroevolutionTask")
# DEBUG
self.accept("l", self.logGeneration)
self.txtGen = OnscreenText(text='',
pos=(0.0, -0.04),
scale=0.05,
align=TextNode.ALeft,
fg=(1, 1, 1, 1),
bg=(0, 0, 0, .4))
self.txtGen.reparentTo(self.a2dTopLeft)