def merge_example(sumo_binary=None):
sumo_params = SumoParams(sumo_binary="sumo-gui",
emission_path="./data/",
sim_step=0.2,
restart_instance=True)
if sumo_binary is not None:
sumo_params.sumo_binary = sumo_binary
vehicles = Vehicles()
vehicles.add(veh_id="human",
acceleration_controller=(IDMController, {
"noise": 0.2
}),
num_vehicles=5)
env_params = EnvParams(additional_params=ADDITIONAL_ENV_PARAMS,
sims_per_step=5,
warmup_steps=0)
inflow = InFlows()
inflow.add(veh_type="human",
edge="inflow_highway",
vehs_per_hour=(1 - RL_PENETRATION) * FLOW_RATE,
departLane="free",
departSpeed=10)
inflow.add(veh_type="human",
edge="inflow_merge",
vehs_per_hour=100,
departLane="free",
departSpeed=7.5)
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
additional_net_params["merge_lanes"] = 1
additional_net_params["highway_lanes"] = 1
additional_net_params["pre_merge_length"] = 500
net_params = NetParams(in_flows=inflow,
no_internal_links=False,
additional_params=additional_net_params)
initial_config = InitialConfig(spacing="uniform",
perturbation=5.0,
lanes_distribution=float("inf"))
scenario = MergeScenario(name="merge-baseline",
generator_class=MergeGenerator,
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env = WaveAttenuationMergePOEnv(env_params, sumo_params, scenario)
return SumoExperiment(env, scenario)
def merge_baseline(num_runs, render=True):
"""Run script for all merge 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 consider a highway network with an upstream merging lane producing
# shockwaves
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
additional_net_params["merge_lanes"] = 1
additional_net_params["highway_lanes"] = 1
additional_net_params["pre_merge_length"] = 500
# RL vehicles constitute 5% of the total number of vehicles
vehicles = Vehicles()
vehicles.add(veh_id="human",
acceleration_controller=(SumoCarFollowingController, {}),
speed_mode=9,
num_vehicles=5)
# Vehicles are introduced from both sides of merge, with RL vehicles
# entering from the highway portion as well
inflow = InFlows()
inflow.add(veh_type="human", edge="inflow_highway",
vehs_per_hour=FLOW_RATE,
departLane="free", departSpeed=10)
inflow.add(veh_type="human", edge="inflow_merge", vehs_per_hour=100,
departLane="free", departSpeed=7.5)
sumo_params = SumoParams(
restart_instance=True,
sim_step=0.5, # time step decreased to prevent occasional crashes
render=render,
)
env_params = EnvParams(
horizon=HORIZON,
sims_per_step=5, # value raised to ensure sec/step match experiment
warmup_steps=0,
evaluate=True, # Set to True to evaluate traffic metric performance
additional_params={
"max_accel": 1.5,
"max_decel": 1.5,
"target_velocity": 20,
"num_rl": NUM_RL,
},
)
initial_config = InitialConfig()
net_params = NetParams(
inflows=inflow,
no_internal_links=False,
additional_params=additional_net_params,
)
scenario = MergeScenario(name="merge",
generator_class=MergeGenerator,
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env = WaveAttenuationMergePOEnv(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
# number of parallel workers
PARALLEL_ROLLOUTS = 10
# inflow rate at the highway
FLOW_RATE = 2000
# percent of autonomous vehicles
RL_PENETRATION = 0.1
# initial number of vehicles
NUM_VEH = 5
# initial number of AVs
NUM_AV = math.floor(RL_PENETRATION * NUM_VEH)
# We consider a highway network with an upstream merging lane producing
# shockwaves
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
additional_net_params["merge_lanes"] = 1
additional_net_params["highway_lanes"] = 1
additional_net_params["pre_merge_length"] = 500
# RL vehicles constitute 5% of the total number of vehicles
vehicles = Vehicles()
vehicles.add(veh_id="human",
acceleration_controller=(IDMController, {"noise": 0.2}),
speed_mode="no_collide",
num_vehicles=NUM_VEH-NUM_AV)
vehicles.add(veh_id="rl",
acceleration_controller=(RLController, {}),
speed_mode="no_collide",
num_vehicles=NUM_AV)
def run_task(_):
"""Implement the run_task method needed to run experiments with rllab."""
sumo_params = SumoParams(
render=True, sim_step=0.2, restart_instance=True)
# RL vehicles constitute 5% of the total number of vehicles
vehicles = Vehicles()
vehicles.add(
veh_id="human",
acceleration_controller=(IDMController, {
"noise": 0.2
}),
speed_mode="no_collide",
num_vehicles=5)
vehicles.add(
veh_id="rl",
acceleration_controller=(RLController, {}),
speed_mode="no_collide",
num_vehicles=0)
# Vehicles are introduced from both sides of merge, with RL vehicles
# entering from the highway portion as well
inflow = InFlows()
inflow.add(
veh_type="human",
edge="inflow_highway",
vehs_per_hour=(1 - RL_PENETRATION) * FLOW_RATE,
departLane="free",
departSpeed=10)
inflow.add(
veh_type="rl",
edge="inflow_highway",
vehs_per_hour=RL_PENETRATION * FLOW_RATE,
departLane="free",
departSpeed=10)
inflow.add(
veh_type="human",
edge="inflow_merge",
vehs_per_hour=100,
departLane="free",
departSpeed=7.5)
additional_env_params = {
"target_velocity": 25,
"num_rl": NUM_RL,
"max_accel": 1.5,
"max_decel": 1.5
}
env_params = EnvParams(
horizon=HORIZON,
sims_per_step=5,
warmup_steps=0,
additional_params=additional_env_params)
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
additional_net_params["merge_lanes"] = 1
additional_net_params["highway_lanes"] = 1
additional_net_params["pre_merge_length"] = 500
net_params = NetParams(
inflows=inflow,
no_internal_links=False,
additional_params=additional_net_params)
initial_config = InitialConfig(
spacing="uniform", lanes_distribution=float("inf"))
scenario = MergeScenario(
name="merge-rl",
generator_class=MergeGenerator,
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env_name = "WaveAttenuationMergePOEnv"
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)
env = normalize(env)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(32, 32, 32),
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=HORIZON * N_ROLLOUTS,
max_path_length=HORIZON,
n_itr=1000,
# whole_paths=True,
discount=0.999,
)
algo.train(),
def merge_example(render=None):
"""
Perform a simulation of vehicles on a merge.
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 merge.
"""
sumo_params = SumoParams(render=True,
emission_path="./data/",
sim_step=0.2,
restart_instance=True)
if render is not None:
sumo_params.render = render
vehicles = Vehicles()
vehicles.add(veh_id="human",
acceleration_controller=(IDMController, {
"noise": 0.2
}),
speed_mode="no_collide",
num_vehicles=5)
env_params = EnvParams(additional_params=ADDITIONAL_ENV_PARAMS,
sims_per_step=5,
warmup_steps=0)
inflow = InFlows()
inflow.add(veh_type="human",
edge="inflow_highway",
vehs_per_hour=FLOW_RATE,
departLane="free",
departSpeed=10)
inflow.add(veh_type="human",
edge="inflow_merge",
vehs_per_hour=100,
departLane="free",
departSpeed=7.5)
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
additional_net_params["merge_lanes"] = 1
additional_net_params["highway_lanes"] = 1
additional_net_params["pre_merge_length"] = 500
net_params = NetParams(inflows=inflow,
no_internal_links=False,
additional_params=additional_net_params)
initial_config = InitialConfig(spacing="uniform", perturbation=5.0)
scenario = MergeScenario(name="merge-baseline",
generator_class=MergeGenerator,
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env = WaveAttenuationMergePOEnv(env_params, sumo_params, scenario)
return SumoExperiment(env, scenario)