def figure_eight_example(render=None):
"""Perform a simulation of vehicles on a figure eight.
Parameters
----------
render: bool, optional
specifies whether to use the gui during execution
Returns
-------
exp: flow.core.experiment.Experiment
A non-rl experiment demonstrating the performance of human-driven
vehicles on a figure eight.
"""
sim_params = AimsunParams(sim_step=0.5, render=False, emission_path='data')
if render is not None:
sim_params.render = render
vehicles = VehicleParams()
vehicles.add(veh_id="human",
acceleration_controller=(IDMController, {}),
num_vehicles=14)
env_params = EnvParams()
net_params = NetParams(additional_params=ADDITIONAL_NET_PARAMS.copy())
scenario = Figure8Scenario(name="figure8",
vehicles=vehicles,
net_params=net_params)
env = TestEnv(env_params, sim_params, scenario, simulator='aimsun')
return Experiment(env)
def figure_eight_example(render=None):
"""
Perform a simulation of vehicles on a figure eight.
Parameters
----------
render: bool, optional
specifies whether to use the gui during execution
Returns
-------
exp: flow.core.experiment.Experiment
A non-rl experiment demonstrating the performance of human-driven
vehicles on a figure eight.
"""
sim_params = SumoParams(render=True)
if render is not None:
sim_params.render = render
vehicles = VehicleParams()
vehicles.add(
veh_id="idm",
acceleration_controller=(IDMController, {}),
lane_change_controller=(StaticLaneChanger, {}),
routing_controller=(ContinuousRouter, {}),
car_following_params=SumoCarFollowingParams(
speed_mode="obey_safe_speed",
decel=1.5,
),
initial_speed=0,
num_vehicles=14)
env_params = EnvParams(additional_params=ADDITIONAL_ENV_PARAMS)
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
net_params = NetParams(additional_params=additional_net_params)
scenario = Figure8Scenario(
name="figure8",
vehicles=vehicles,
net_params=net_params)
env = AccelEnv(env_params, sim_params, scenario)
return Experiment(env)
def figure_eight_example(render=None):
"""
Perform a simulation of vehicles on a figure eight.
Parameters
----------
render: bool, optional
specifies whether to use sumo's gui during execution
Returns
-------
exp: flow.core.SumoExperiment type
A non-rl experiment demonstrating the performance of human-driven
vehicles on a figure eight.
"""
sumo_params = SumoParams(render=True)
if render is not None:
sumo_params.render = render
vehicles = Vehicles()
vehicles.add(veh_id="idm",
acceleration_controller=(IDMController, {}),
lane_change_controller=(StaticLaneChanger, {}),
routing_controller=(ContinuousRouter, {}),
speed_mode="no_collide",
initial_speed=0,
num_vehicles=14)
env_params = EnvParams(additional_params=ADDITIONAL_ENV_PARAMS)
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
net_params = NetParams(no_internal_links=False,
additional_params=additional_net_params)
scenario = Figure8Scenario(name="figure8",
vehicles=vehicles,
net_params=net_params)
env = AccelEnv(env_params, sumo_params, scenario)
return SumoExperiment(env, scenario)
def figure_eight_exp_setup(sim_params=None,
vehicles=None,
env_params=None,
net_params=None,
initial_config=None,
traffic_lights=None):
"""
Create an environment and scenario pair for figure eight test experiments.
Parameters
----------
sim_params : flow.core.params.SumoParams
sumo-related configuration parameters, defaults to a time step of 0.1s
and no sumo-imposed failsafe on human or rl vehicles
vehicles : Vehicles type
vehicles to be placed in the network, default is one vehicles with an
IDM acceleration controller and ContinuousRouter routing controller.
env_params : flow.core.params.EnvParams
environment-specific parameters, defaults to a environment with no
failsafes, where other parameters do not matter for non-rl runs
net_params : flow.core.params.NetParams
network-specific configuration parameters, defaults to a figure eight
with a 30 m radius
initial_config : flow.core.params.InitialConfig
specifies starting positions of vehicles, defaults to evenly
distributed vehicles across the length of the network
traffic_lights: flow.core.params.TrafficLightParams
traffic light signals, defaults to no traffic lights in the network
"""
logging.basicConfig(level=logging.WARNING)
if sim_params is None:
# set default sim_params configuration
sim_params = SumoParams(sim_step=0.1, render=False)
if vehicles is None:
# set default vehicles configuration
vehicles = VehicleParams()
vehicles.add(veh_id="idm",
acceleration_controller=(IDMController, {}),
car_following_params=SumoCarFollowingParams(
speed_mode="aggressive", ),
routing_controller=(ContinuousRouter, {}),
num_vehicles=1)
if env_params is None:
# set default env_params configuration
additional_env_params = {
"target_velocity": 8,
"max_accel": 1,
"max_decel": 1,
"sort_vehicles": False
}
env_params = EnvParams(additional_params=additional_env_params)
if net_params is None:
# set default net_params configuration
additional_net_params = {
"radius_ring": 30,
"lanes": 1,
"speed_limit": 30,
"resolution": 40
}
net_params = NetParams(additional_params=additional_net_params)
if initial_config is None:
# set default initial_config configuration
initial_config = InitialConfig(lanes_distribution=1)
if traffic_lights is None:
# set default to no traffic lights
traffic_lights = TrafficLightParams()
# create the scenario
scenario = Figure8Scenario(name="FigureEightTest",
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config,
traffic_lights=traffic_lights)
# create the environment
env = AccelEnv(env_params=env_params,
sim_params=sim_params,
scenario=scenario)
# reset the environment
env.reset()
return env, scenario
def run_task(*_):
"""Implement the run_task method needed to run experiments with rllab."""
sumo_params = SumoParams(sim_step=0.1, render=True)
vehicles = Vehicles()
vehicles.add(veh_id="rl",
acceleration_controller=(RLController, {}),
routing_controller=(ContinuousRouter, {}),
sumo_car_following_params=SumoCarFollowingParams(
speed_mode="no_collide", ),
num_vehicles=1)
vehicles.add(veh_id="idm",
acceleration_controller=(IDMController, {
"noise": 0.2
}),
routing_controller=(ContinuousRouter, {}),
sumo_car_following_params=SumoCarFollowingParams(
speed_mode="no_collide", ),
num_vehicles=13)
additional_env_params = {
"target_velocity": 20,
"max_accel": 3,
"max_decel": 3
}
env_params = EnvParams(horizon=HORIZON,
additional_params=additional_env_params)
additional_net_params = {
"radius_ring": 30,
"lanes": 1,
"speed_limit": 30,
"resolution": 40
}
net_params = NetParams(no_internal_links=False,
additional_params=additional_net_params)
initial_config = InitialConfig(spacing="uniform")
print("XXX name", exp_tag)
scenario = Figure8Scenario(exp_tag,
vehicles,
net_params,
initial_config=initial_config)
env_name = "AccelEnv"
pass_params = (env_name, sumo_params, vehicles, env_params, net_params,
initial_config, scenario)
env = GymEnv(env_name, record_video=False, register_params=pass_params)
horizon = env.horizon
env = normalize(env)
policy = GaussianMLPPolicy(env_spec=env.spec, hidden_sizes=(16, 16))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=15000,
max_path_length=horizon,
n_itr=500,
# whole_paths=True,
discount=0.999,
# step_size=v["step_size"],
)
algo.train(),
def figure_eight_baseline(num_runs, render=True):
"""Run script for all figure eight baselines.
Parameters
----------
num_runs : int
number of rollouts the performance of the environment is evaluated
over
render : bool, optional
specifies whether to use sumo's gui during execution
Returns
-------
SumoExperiment
class needed to run simulations
"""
# We place 1 autonomous vehicle and 13 human-driven vehicles in the network
vehicles = Vehicles()
vehicles.add(veh_id="human",
acceleration_controller=(IDMController, {
"noise": 0.2
}),
routing_controller=(ContinuousRouter, {}),
speed_mode="no_collide",
num_vehicles=14)
sumo_params = SumoParams(
sim_step=0.1,
render=render,
)
env_params = EnvParams(
horizon=HORIZON,
evaluate=True, # Set to True to evaluate traffic metrics
additional_params={
"target_velocity": 20,
"max_accel": 3,
"max_decel": 3,
},
)
initial_config = InitialConfig()
net_params = NetParams(
no_internal_links=False,
additional_params=ADDITIONAL_NET_PARAMS,
)
scenario = Figure8Scenario(name="figure_eight",
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env = AccelEnv(env_params, sumo_params, scenario)
exp = SumoExperiment(env, scenario)
results = exp.run(num_runs, HORIZON)
avg_speed = np.mean(results["mean_returns"])
return avg_speed