def main():
# Control code here
sumoCmd = [sumoBinary, "-c", sumoConfig, "--start"]
traci.start(sumoCmd)
TLIds = traci.trafficlight.getIDList()
actionsMap = makemap(TLIds)
detectorIDs = traci.inductionloop.getIDList()
state_space_size = traci.inductionloop.getIDCount() * 2
action_space_size = len(actionsMap)
agent = Learner(state_space_size, action_space_size, 0.1)
agent.load("./save/traffic.h5")
# Get number of induction loops
state = get_state(detectorIDs)
total_reward = 0
simulationSteps = 0
while simulationSteps < 5000:
action = agent.act(state)
lightsPhase = actionsMap[action]
for light, index in zip(TLIds, range(len(TLIds))):
traci.trafficlight.setPhase(light, lightsPhase[index])
for i in range(2):
traci.simulationStep()
time.sleep(0.4)
simulationSteps += 2
next_state = get_state(detectorIDs)
reward = calc_reward(state, next_state)
total_reward += reward
agent.remember(state, action, reward, next_state)
state = next_state
traci.close()
print "Simulation Reward: {}".format(total_reward)
def main():
# Control code here
sumoCmd = [sumoBinary, "-c", sumoConfig, "--start"]
traci.start(sumoCmd)
TLIds = traci.trafficlight.getIDList()
actionsMap = makemap(TLIds)
detectorIDs = traci.inductionloop.getIDList()
print TLIds
state_space_size = traci.inductionloop.getIDCount()*2
action_space_size = len(actionsMap)
agent = Learner(state_space_size, action_space_size, 1.0)
# agent.load("./save/traffic.h5")
traci.close()
epochs = 20000
def main():
# Control code here
sumoGuiCmd = [
sumoBinary, "-c", sumoConfig, "--fcd-output", dumpFile,
"--tripinfo-output", tripInfoFinal
]
sumoCliCmd = [sumoCli, "-c", sumoConfig, "--start"]
traci.start(sumoCliCmd)
TLIds = traci.trafficlight.getIDList()
actionsMap = makemap(TLIds)
detectorIDs = traci.inductionloop.getIDList()
state_space_size = traci.inductionloop.getIDCount() * 2
action_space_size = len(actionsMap)
print('hello', action_space_size)
agent = Learner(state_space_size, action_space_size, 1.0)
#agent.load("./save/EdgeDensityModel.h5")
traci.close()
epochs = 10
for simulation in range(epochs):
if (simulation % 9 == 0 and simulation != 0):
traci.start(sumoGuiCmd)
else:
traci.start(sumoCliCmd)
# Get number of induction loops
state = get_state(detectorIDs)
state1 = get_state_edge_density()
total_reward = 0
simulationSteps = 0
while simulationSteps < 1000:
action = agent.act(state)
lightsPhase = actionsMap[action]
for light, index in zip(TLIds, range(len(TLIds))):
traci.trafficlight.setPhase(light, lightsPhase[index])
for i in range(2):
traci.simulationStep()
simulationSteps += 2
next_state = get_state(detectorIDs)
next_state1 = get_state_edge_density()
reward = calc_reward(state, next_state)
#reward = calc_reward_edge_density(state1, next_state1)
total_reward += reward
#agent.remember(state1, action, reward, next_state1)
state = next_state
state1 = next_state1
traci.close()
def main():
# Control code here
sumoCmd = [sumoBinary, "-c", sumoConfig, "--start"]
traci.start(sumoCmd)
TLIds = traci.trafficlights.getIDList()
actionsMap = makemap(TLIds)
detectorIDs = traci.inductionloop.getIDList()
state_space_size = traci.inductionloop.getIDCount()*2
action_space_size = len(actionsMap)
agent = Learner(state_space_size, action_space_size, 1.0)
# agent.load("./save/traffic.h5")
traci.close()
epochs = 1000
for simulation in range(epochs):
traci.start(sumoCmd)
# Get number of induction loops
state = get_state(detectorIDs)
total_reward = 0
simulationSteps = 0
while simulationSteps < 1000:
action = agent.act(state)
lightsPhase = actionsMap[action]
for light, index in zip(TLIds, range(len(TLIds))):
traci.trafficlights.setPhase(light, lightsPhase[index])
for i in range(2):
traci.simulationStep()
simulationSteps += 2
next_state = get_state(detectorIDs)
reward = calc_reward(state, next_state)
total_reward += reward
agent.remember(state, action, reward, next_state)
state = next_state
traci.close()
with open("ResultsOfSimulations.txt", "a") as f:
f.write("Simulation {}: {}\n".format(simulation, total_reward))
agent.replay()
if simulation % 10 == 0:
agent.save("./save/traffic.h5")
def main():
# Control code here
sumoCmd = [sumoBinary, "-c", sumoConfig, "--start"]
traci.start(sumoCmd)
TLIds = traci.trafficlights.getIDList()
actionsMap = makemap(TLIds)
detectorIDs = traci.inductionloop.getIDList()
state_space_size = traci.inductionloop.getIDCount() * 2
action_space_size = len(actionsMap)
agent = Learner(state_space_size, action_space_size, 1.0)
# agent.load("./save/traffic.h5")
epochs = 10
for simulation in range(epochs):
traci.start(sumoCmd)
# Get number of induction loops
state = get_state(detectorIDs)
state1 = get_state_edge_density()
total_reward = 0
simulationSteps = 0
while simulationSteps < 1000:
action = agent.act(state)
lightsPhase = actionsMap[action]
for light, index in zip(TLIds, range(len(TLIds))):
traci.trafficlights.setPhase(light, lightsPhase[index])
for i in range(2):
traci.simulationStep()
simulationSteps += 2
next_state = get_state(detectorIDs)
next_state1 = get_state_edge_density()
#calculate reward
reward = calc_reward(state, next_state)
total_reward += reward
#remember actions and rewards
agent.remember(state, action, reward, next_state)
state = next_state
state1 = next_state1
print(state1)