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 monteCarlo(agent, maxDepth=3, trials=12, frequency=10):
PROCESSES = 4
model = VelocityModel(
regressionModel=joblib.load('models/gradient-m.model'),
frequency=frequency)
actions = np.array(agent.getActions())
initialState, isTerminal = agent.getState(), 0
jobs = [None] * len(actions) * trials
while bool(isTerminal) is False:
initialState = agent.getState()
qs = {i: [] for i in actions}
for index, a in enumerate(np.repeat(actions, trials)):
virtualAgent, isTerminal = RLAgent(
'virtual',
alternativeModel=model,
decisionFrequency=math.inf,
maxDepth=maxDepth,
initialState=initialState), False
virtualAgent.setReward(reward)
virtualAgent.goal = agent.getGoal()
virtualAgent.goalMargins = agent.getGoalMargins()
virtualAgent.setRl(
partial(monteCarloSearch,
actions=getRandomActions(a, actions, maxDepth)))
jobs[index] = virtualAgent
pool = Pool(8)
results = [pool.apply_async(job.run) for job in jobs]
for result in results:
action, score = result.get()
qs[action].append(score)
pool.close()
pool.join()
yield actions[np.argmax([np.average(qs[a]) for a in actions])]
r, nextState, isTerminal = (yield)
f = 1 / (nextState.lastUpdate - initialState.lastUpdate)
# correct for deviations from desired freq.
model.frequency = f
agent.logger.info(f)
yield
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()