def getMARLState(tls_id):
P, V, L = getDTSE(tls_id)
# traffic light ids of lights that connect to the tls_id light
# Here we assume the ids are all integer
tls_id1 = str((int(tls_id) + 1) % 4)
tls_id2 = str((int(tls_id) - 1) % 4)
if ma == 1:
phase1 = tl.getPhase(tls_id1) // 2
phase2 = tl.getPhase(tls_id2) // 2
#print('MARL tls ids & phases:', tls_id, tls_id1, tls_id2, phase1, phase2)
other_signal_state1 = torch.zeros(1, A).float()
other_signal_state1[0][phase1] = 1
other_signal_state2 = torch.zeros(1, A).float()
other_signal_state2[0][phase2] = 1
elif ma == 2:
state_info1 = cumHaltingNumber(tls_id1)
state_info2 = cumHaltingNumber(tls_id2)
other_signal_state1 = torch.Tensor([[state_info1]])
other_signal_state2 = torch.Tensor([[state_info2]])
L = torch.cat((L, other_signal_state1, other_signal_state2), 1)
return P, V, L
def getState(tls_id):
EW = 0
N = 0
for lane_id in tl.getControlledLanes(tls_id):
det_id = la_mapping[lane_id]
if det_id[-1] in ["E", "W"]:
EW += la.getLastStepHaltingNumber(det_id)
else: # in ["N", "S"]
N += la.getLastStepHaltingNumber(det_id)
phase = tl.getPhase(tls_id) % 2 # only in this case, for four phases
db('State:', EW, N)
sm = stateMap(EW, N, phase)
visits.append((EW, N))
return sm
def inductionloop():
"""execute the TraCI control loop"""
# we start with phase 2 where EW has green
tl.setPhase("0", 2)
i = 2
while sim.getMinExpectedNumber() > 0:
traci.simulationStep()
print("E:", la.getLastStepHaltingNumber("0"))
print("N:", la.getLastStepHaltingNumber("1"))
# Get occupancy of East lane
#print(lane.getLastStepOccupancy("2i_0"))
if tl.getPhase("0") == 2:
# we are not already switching
if traci.inductionloop.getLastStepVehicleNumber("0") > 0:
# there is a vehicle from the north, switch
tl.setPhase("0", 3)
else:
# otherwise try to keep green for EW
tl.setPhase("0", 2)
traci.close()
sys.stdout.flush()
def getDTSE(tls_id):
# Get all vehicles in the lanearea
# Given a list of relevant vehicles, convert it t
# Array of intersection state maps
DTSE_pos = np.zeros((NUM_LANES * 4, num_cells))
DTSE_spd = np.zeros((NUM_LANES * 4, num_cells))
for l, lane_id in enumerate(tl.getControlledLanes(tls_id)):
det_id = la_mapping[lane_id]
det_len = la.getLength(det_id)
lane_len = lane.getLength(la.getLaneID(det_id))
assert det_len == LANEAREA_LENGTH
for vid in la.getLastStepVehicleIDs(det_id):
# What is the correct encoding of pos to cell? Should low indices specify cars close or far away for traffic light?
#print('lid',veh.getLanePosition(vid))
pos = LANEAREA_LENGTH - lane_len + veh.getLanePosition(vid)
if pos < 0:
continue
cell = int(
min(pos // c, num_cells)
) # in case pos ends up being exactly the length of the detector
speed = veh.getSpeed(vid)
#print("Veh {}: {}".format(vid, pos))
#print('cell',cell)
# Record binary position and speed arrays
DTSE_pos[l][cell] = 1
DTSE_spd[l][cell] = speed
phase = int(tl.getPhase(tls_id)) % 2
signal_state = torch.zeros(1, A).float()
signal_state[0][phase] = 1
# Add batch dimension and input filter dimensions to state vectors
DTSE_pos = torch.from_numpy(DTSE_pos).unsqueeze(0).unsqueeze(0).float()
DTSE_spd = torch.from_numpy(DTSE_spd).unsqueeze(0).unsqueeze(0).float()
return DTSE_pos, DTSE_spd, signal_state
tl_list=[]
for connection in connection_list:
tl_list.append( connection[2])
return tl_list
#lust= import_datasets()
#tls= extract_tl_ids(lust.iloc[0])
#print tls
#connections = dataset[5]==[from,to,tl,dir,state]
if True:
TLSID= "0"
while step < 1000:
simulationStep()
arrived_vehicles_in_last_step= simulation.getArrivedNumber()
departed_vehicles_in_last_step= simulation.getDepartedNumber()
current_simulation_time_ms= simulation.getCurrentTime()
print arrived_vehicles_in_last_step
phase= trafficlights.getPhase(TLSID)
trafficlights.setRedYellowGreenState(TLSID, "grrrrrrrrrrr")
lanes= trafficlights.getControlledLanes(TLSID)
print len(lanes)
#for lane in lanes:
# print lane
print phase
#if traci.inductionloop.getLastStepVehicleNumber("0") > 0:
# traci.trafficlights.setRedYellowGreenState("0", "GrGr")
step += 1
close()
for connection in connection_list:
tl_list.append(connection[2])
return tl_list
#lust= import_datasets()
#tls= extract_tl_ids(lust.iloc[0])
#print tls
#connections = dataset[5]==[from,to,tl,dir,state]
if True:
TLSID = "0"
while step < 1000:
simulationStep()
arrived_vehicles_in_last_step = simulation.getArrivedNumber()
departed_vehicles_in_last_step = simulation.getDepartedNumber()
current_simulation_time_ms = simulation.getCurrentTime()
print arrived_vehicles_in_last_step
phase = trafficlights.getPhase(TLSID)
trafficlights.setRedYellowGreenState(TLSID, "grrrrrrrrrrr")
lanes = trafficlights.getControlledLanes(TLSID)
print len(lanes)
#for lane in lanes:
# print lane
print phase
#if traci.inductionloop.getLastStepVehicleNumber("0") > 0:
# traci.trafficlights.setRedYellowGreenState("0", "GrGr")
step += 1
close()
def run(models, state_fn, action_time=12, learn=True, writer=None):
print("File beginning", flush=True)
tls_ids = tl.getIDList()
step() #traci.simulationStep()
s = [None for i in range(len(tls_ids))]
a = [None for i in range(len(tls_ids))]
r = [None for i in range(len(tls_ids))]
s_ = [None for i in range(len(tls_ids))]
next_action_times = np.zeros(len(tls_ids), dtype=int)
for t, tls_id in enumerate(tls_ids):
tl.setPhase(tls_id, 2)
# Find current states
s[t] = state_fn(tls_id)
checkpoint = False
while sim.getMinExpectedNumber() > 0:
if not checkpoint and "2000" in veh.getIDList():
writer.add_text("Rate switch", "Vehicle 2000 encountered",
sim.getCurrentTime())
checkpoint = True
for t, tls_id in enumerate(tls_ids):
db("Phase duration for " + tls_id, tl.getPhaseDuration(tls_id))
if next_action_times[t] > 0:
continue
tl.setPhaseDuration(tls_id, 10000)
# Take actions
a[t] = models[t].select_action(s[t])
current_phase = tl.getPhase(tls_id)
if a[t] * 2 != current_phase: # Change action
db('Change to other green light')
tl.setPhase(tls_id, (current_phase + 1) % 4)
#print("phase:", tl.getPhase("0"))
yellow_time = (tl.getNextSwitch(tls_id) -
sim.getCurrentTime()) // 1000
else:
yellow_time = 0
next_action_times[t] = action_time + yellow_time
db('actions:', a)
db("-" * 10)
# Move simulation forward one step and obtain reward
#db(action_time + yellow_time)
step_size = min(next_action_times)
for j in range(step_size):
step() #traci.simulationStep()
if sim.getMinExpectedNumber() <= 0:
return
next_action_times -= step_size
# Get some kind of reward signal from the E2 loops
# Let's say its negative of the number of cars on the loop
for t, tls_id in enumerate(tls_ids):
if next_action_times[t] > 0:
continue
r[t] = getReward(tls_id)
if writer:
writer.add_scalar('reward', r[t], sim.getCurrentTime())
rewards_t.append(r)
db("reward:", r)
s_[t] = state_fn(tls_id)
if learn:
models[t].update(s[t], a[t], r[t], s_[t], alpha)
s[t] = s_[t]