def run():
if len(sys.argv) < 2:
print('Please provide the path to the .sumocfg of a SUMO scenario')
exit()
print('started')
## GUI / CLI
# traci.start(["sumo-gui", "-c", sys.argv[1]])
traci.start(["sumo", "-W", "-c", sys.argv[1]])
print('traci init')
step = 0
startTime = datetime.now()
print('started at ', startTime.strftime('%Y-%m-%dT%H:%M:%SZ'))
while traci.simulation.getMinExpectedNumber() > 0:
traci.simulationStep()
step += 1
if (step % 1000 == 0):
print('\nTime: ',
datetime.now().strftime('%Y-%m-%dT%H:%M:%SZ'), '\n')
traci.close()
endTime = datetime.now()
print("Simulation Time:", startTime.strftime('%Y-%m-%dT%H:%M:%SZ'),
endTime.strftime('%Y-%m-%dT%H:%M:%SZ'), "differnce:",
(endTime - startTime).strftime('%Y-%m-%dT%H:%M:%SZ'))
sys.stdout.flush()
def _simulation_end(self):
if self.visual == False:
libsumo.close()
return
else:
traci.close()
return
def reset(self):
"""Resets the state of the environment and returns an initial observation.
Returns: observation (object): the initial observation of the
space.
"""
traci.close()
traci.start(self.cmd)
self._step = self.begin_time
# Begin with action id of 1
if not self.idle:
self._action = 1
self._setTrafficLights(self._action)
else:
self._action = self._getTrafficLights()
return (np.zeros(shape=(21,)), 1)
def main():
# CAUTION! step is in MILLISECONDS in version 0.30.0, but in SECONDS in version 1.1.0
#step = BEGIN_TIME*1000
step = BEGIN_TIME
#while step < END_TIME*1000:
while step <= END_TIME:
## if step%(3600*1000) == 0:
## print("Current Time: {}:00".format(step//(3600*1000)))
## for edge in edge_charoenRat:
## for lane in range(0,NUM_LANES[edge]):
## print("{}: {}".format(lane, traci.lanearea.getLastStepOccupancy("e2_{}_{}_0".format(edge,lane))))
traci.simulationStep(step=step)
## traci.simulationStep()
## step += STEP_SIZE*1000
step += STEP_SIZE
print(step)
traci.close()
def main():
step = float(BEGIN_TIME)
SIMULATION_STEPS = (END_TIME -
BEGIN_TIME) // 5 + 1 # 1 for the beginning step
print()
while step < END_TIME:
#break
if step % 3600 == 0:
print("Current Time: {}:00".format(step // 3600))
for edge in edge_charoenRat:
for lane in range(0, NUM_LANES[edge]):
print("{}: {}".format(
lane,
traci.lanearea.getLastStepOccupancy("e2_{}_{}_0".format(
edge, lane))))
traci.simulationStep(step=step)
step += STEP_SIZE
print(step)
traci.close()
traci.edge.getAdaptedTraveltime(edgeID, 25))
traci.edge.setEffort(edgeID, 1234.)
print("effort after adaption", traci.edge.getEffort(edgeID, 0))
traci.edge.setEffort(edgeID, 2468., 10, 20)
print("effort after adaption in interval (check time 0)",
traci.edge.getEffort(edgeID, 0))
print("effort after adaption in interval (check time 15)",
traci.edge.getEffort(edgeID, 15))
print("effort after adaption in interval (check time 25)",
traci.edge.getEffort(edgeID, 25))
traci.edge.setMaxSpeed(edgeID, 23.)
print("max speed after adaption", traci.lane.getMaxSpeed(edgeID + "_0"))
traci.edge.subscribe(edgeID)
print(traci.edge.getSubscriptionResults(edgeID))
for step in range(3, 6):
print("step", step)
traci.simulationStep()
print(traci.edge.getSubscriptionResults(edgeID))
for step in range(10):
traci.simulationStep()
print("3si count=%s meanSpeed=%s travelTime=%s" %
(traci.edge.getLastStepVehicleNumber("3si"),
traci.edge.getLastStepMeanSpeed("3si"),
traci.edge.getTraveltime("3si")))
traci.close()
def close(self):
"""Override _close in your subclass to perform any necessary cleanup.
Environments will automatically close() themselves when
garbage collected or when the program exits.
"""
traci.close()
parser.add_argument('--episodes', default=1500000, type=int)
args = parser.parse_args()
print(args)
background, shift = get_background(
'simulation_files/networks/simple.net.xml')
net = sumolib.net.readNet('simulation_files/networks/simple.net.xml')
bounding_box = net.getBBoxXY()
sumocfg = "simulation_files/networks/simple.net.xml"
libsumo.start(['-c', sumocfg])
edges = libsumo.edge.getIDList()
print(edges)
libsumo.close()
'''
all_episodes = []
for step_range in range(1, 150, 3):
total_reward = step = action = 0
action = 1
s = env.reset()
terminal = False
mistakes = 0; total_est = 0
while not terminal:
if step % step_range == 0:
action = not action
action = np.random.choice(range(2))
ns, r, terminal, _ = env.step(action)
cars = s[1]