def execute(self, action):
#print('execute')
run = Assembler(self.carID)
self.output = run.getTensor()
if self.output is None:
term = True
#print('is none')
return self.getObservation(), term, 0 #20000
rew = Reward('ego', run.getTraffic(), self.output, run.getPrioritizedTraffic())
coll, term = rew.collision()
if term is True:
cost = coll
return self.getObservation(), term, cost
# over, term = rew.overtime()
# if term is True:
# cost = over
# return self.getObservation(), term, cost
traci.vehicle.setSpeedMode('ego', 0)
num_vec = traci.vehicle.getIDList()
for i in range(len(num_vec)):
if num_vec[i] != 'ego':
traci.vehicle.setLaneChangeMode(num_vec[i], 512)
carID = 'ego'
act = Action(carID)
#print(action)
if action == 0:
act.decelerate()
#print('dec')
elif action == 1:
act.accelerate()
#print('acc')
#elif action == 2:
# act.emergency()
else:
act.remain()
#print('rem')
gap, term = rew.emergency_gap()
#wary = rew.wary_before_intersection()
#brake = rew.emergency_brake()
#print(self.output[0:20])
#print(gap)
cost = rew.optimum_speed_deviation() + gap #+ over
traci.simulationStep()
#print(self.getObservation())
#print(term)
return self.getObservation(), term, cost
def execute(self, action):
run = Assembler(self.carID)
self.output = run.getTensor()
#print(run.getTensor())
if self.output is None:
term = True
return self.getObservation(), term, 0
rew = Reward('ego', run.getTraffic(), self.output)
coll, term = rew.collision()
if term is True:
cost = coll
return self.getObservation(), term, cost
traci.vehicle.setSpeedMode('ego', 0)
num_vec = traci.vehicle.getIDList()
for i in range(len(num_vec)):
if num_vec[i] != 'ego':
traci.vehicle.setLaneChangeMode(num_vec[i], 512)
###
#if i % 2 == 0:
#traci.vehicle.setSpeedMode(num_vec[i], 23)
carID = 'ego'
act = Action(carID)
if action == 0:
act.decelerate()
# print('dec')
elif action == 1:
act.accelerate()
# print('acc')
else:
act.remain()
# print('rem')
gap, term = rew.emergency_gap()
wary = rew.wary_before_intersection()
#win = rew.target()
brake = rew.emergency_brake()
cost = rew.optimum_speed_deviation() + brake + gap + wary
traci.simulationStep()
#print(self.output)
return self.getObservation(), term, cost