def main():
model = VelocityModel(
regressionModel=joblib.load('models/gradient-m.model'), frequency=10.0)
agent = RLAgent('agent',
decisionFrequency=20.0,
defaultSpeed=4,
defaultAltitude=6,
yawRate=60,
alternativeModel=model,
maxDepth=math.inf,
initialState=None)
agent.setRl(verifyModel)
agent.start()
agent.join()
def main():
agent = RLAgent('agent',
decisionFrequency=10.0,
defaultSpeed=4,
defaultAltitude=6,
yawRate=60)
agent.defineState(orientation=getOrientation,
position=getPosition,
angularVelocity=getAngularVelocity,
linearVelocity=getVelocity,
linearAcceleration=getLinearAcceleration,
angularAcceleration=getAngularAcceleration)
agent.setRl(partial(flightLogger, dataset='datasets/' + 'replay.csv'))
agent.start()
agent.join()
def main():
agent = RLAgent('agent',
decisionFrequency=10.0,
defaultSpeed=4,
defaultAltitude=20,
yawRate=70)
# callbacks will be called in the order they were specified, beware of order of execution (if any state parameter is
# dependant on another)
# state is lazily updated by the environment as the agent needs it , agent always get the freshest estimate of the
# state, state updates are done by the environment in a rate that corresponds to agent decision making freq.
agent.defineState(orientation=getOrientation,
angularVelocity=getAngularVelocity,
linearVelocity=getVelocity,
position=getPosition)
agent.setRl(monteCarlo)
agent.setReward(reward)
agent.setGoal(position=np.array([-40, -50, 0]))
agent.setGoalMargins(position=np.array([0.5, 0.5, math.inf]))
agent.start()
agent.join()