def setUp(self):
self.sim_params = SumoParams(sim_step=0.5, restart_instance=True)
vehicles = VehicleParams()
vehicles.add(veh_id="human", num_vehicles=10)
env_params = EnvParams(
additional_params={
"max_accel": 3,
"max_decel": 3,
"lane_change_duration": 5,
"disable_tb": True,
"disable_ramp_metering": True,
"target_velocity": 30,
"add_rl_if_exit": True,
}
)
net_params = NetParams(
no_internal_links=False,
additional_params={"scaling": 1, "speed_limit": 23})
self.scenario = BottleneckScenario(
name="bay_bridge_toll",
vehicles=vehicles,
net_params=net_params)
self.env = BottleNeckAccelEnv(
env_params, self.sim_params, self.scenario)
self.env.reset()
def test_reset_inflows(self):
"""Tests that the inflow change within the expected range when calling
reset."""
# set a random seed for inflows to be the same every time
np.random.seed(seed=123)
sim_params = SumoParams(sim_step=0.5, restart_instance=True)
vehicles = VehicleParams()
vehicles.add(veh_id="human")
vehicles.add(veh_id="followerstopper")
# edge name, how many segments to observe/control, whether the segment
# is controlled
controlled_segments = [("1", 1, False), ("2", 2, True), ("3", 2, True),
("4", 2, True), ("5", 1, False)]
num_observed_segments = [("1", 1), ("2", 3), ("3", 3), ("4", 3),
("5", 1)]
env_params = EnvParams(
additional_params={
"target_velocity": 40,
"disable_tb": True,
"disable_ramp_metering": True,
"controlled_segments": controlled_segments,
"symmetric": False,
"observed_segments": num_observed_segments,
"reset_inflow": True, # this must be set to True for the test
"lane_change_duration": 5,
"max_accel": 3,
"max_decel": 3,
"inflow_range": [1000, 2000] # this is what we're testing
})
inflow = InFlows()
inflow.add(
veh_type="human",
edge="1",
vehs_per_hour=1500, # the initial inflow we're checking for
departLane="random",
departSpeed=10)
net_params = NetParams(inflows=inflow,
no_internal_links=False,
additional_params={
"scaling": 1,
"speed_limit": 23
})
scenario = BottleneckScenario(name="bay_bridge_toll",
vehicles=vehicles,
net_params=net_params)
env = DesiredVelocityEnv(env_params, sim_params, scenario)
# reset the environment and get a new inflow rate
env.reset()
expected_inflow = 1343.178 # just from checking the new inflow
# check that the first inflow rate is approximately 1500
for _ in range(500):
env.step(rl_actions=None)
self.assertAlmostEqual(
env.k.vehicle.get_inflow_rate(250) / expected_inflow, 1, 1)
# reset the environment and get a new inflow rate
env.reset()
expected_inflow = 1729.050 # just from checking the new inflow
# check that the new inflow rate is approximately as expected
for _ in range(500):
env.step(rl_actions=None)
self.assertAlmostEqual(
env.k.vehicle.get_inflow_rate(250) / expected_inflow, 1, 1)
