def main():
traci.start(sumoCmd)
simulation_step = 0
atom_time = 30
yell_time = 3
traffic_data = []
controlTLIds = "0"
phaseDefs = ['GrrrGrrr', 'rGrrrGrr', 'rrGrrrGr', 'rrrGrrrG']
yelloPhases = ['yrrryrrr', 'ryrrryrr', 'rryrrryr', 'rrryrrry']
index = 0
while simulation_step < 864000:
ryg = yelloPhases[index % 4]
traci.trafficlight.setRedYellowGreenState(controlTLIds, ryg)
for j in range(yell_time):
traci.simulationStep()
ryg = phaseDefs[index % 4]
traci.trafficlight.setRedYellowGreenState(controlTLIds, ryg)
for j in range(atom_time-yell_time):
traci.simulationStep()
# for _ in range(ato_time):
# traci.simulationStep()
simulation_step = simulation_step + atom_time
index += 1
traffic_data.append(measure_traffic_step(controlTLIds,simulation_step))
traffic_data = pd.DataFrame(traffic_data)
traffic_data.to_csv("/home/hjj/exp_test_v2/Test/TestData/real/static_real.csv")
traci.close()
collect_tripinfo(trip_file,'/home/hjj/exp_test_v2/Test/TestData/real/static_real')
def main():
traci.start(sumoCmd)
agent = Agent(traci, True)
traci.close()
# start simulation
traci.start(sumoCmd)
agent.reset()
simulation_steps = 0
traffic_data = []
yell_time = 3
atom_time = 30
controlTLIds = "0"
while simulation_steps < 60000:
traffic_data.append(
measure_traffic_step(controlTLIds, simulation_steps))
agent.state = agent._getState()
action, current_phase = agent.agentAct()
if action == current_phase:
agent.setRYG(action, False)
for j in range(atom_time):
traci.simulationStep()
else:
agent.setRYG(current_phase, True)
for j in range(yell_time):
traci.simulationStep()
agent.setRYG(action, False)
for j in range(atom_time - yell_time):
traci.simulationStep()
agent._getReward() # stop training
simulation_steps += atom_time
# The END of simulationSteps loop
traci.close()
traffic_data = pd.DataFrame(traffic_data)
traffic_data.to_csv(
"/home/hjj/exp_test/Test/TestData/near_unba/a2c/interval30/a2c_near_unba_30_new.csv"
)
collect_tripinfo(
trip_file,
'/home/hjj/exp_test/Test/TestData/near_unba/a2c/interval30/a2c_near_unba_30_new'
)
def main():
traci.start(sumoCmd)
simulation_step = 0
ato_time = 30
traffic_data = []
controlTLIds = "0"
while simulation_step < 6000:
for _ in range(ato_time):
traci.simulationStep()
simulation_step = simulation_step + ato_time
traffic_data.append(measure_traffic_step(controlTLIds,
simulation_step))
traffic_data = pd.DataFrame(traffic_data)
traffic_data.to_csv("run_data/static_over.csv")
traci.close()
collect_tripinfo(trip_file, 'run_data/static_over')
def main():
traci.start(sumoCmd)
agent = Agent(traci, True)
traci.close()
# start simulation
traci.start(sumoCmd)
agent.reset()
simulation_steps = 0
traffic_data = []
yell_time = 3
atom_time = 30
controlTLIds = "0"
while simulation_steps < 864000:
traffic_data.append(
measure_traffic_step(controlTLIds, simulation_steps))
action, current_phase = agent.agentAct()
if action == current_phase:
agent.setRYG(action, False)
for j in range(atom_time):
traci.simulationStep()
else:
agent.setRYG(current_phase, True)
for j in range(yell_time):
traci.simulationStep()
agent.setRYG(action, False)
for j in range(atom_time - yell_time):
traci.simulationStep()
agent.agentCulReward(is_sim=True) # stop training
simulation_steps += atom_time
# The END of simulationSteps loop
traci.close()
traffic_data = pd.DataFrame(traffic_data)
traffic_data.to_csv(
"/home/hjj/exp_test_v2/Test/TestData/real/dqn_real.csv")
collect_tripinfo(trip_file,
'/home/hjj/exp_test_v2/Test/TestData/real/dqn_real')