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 sugiyama_example(render=None):
"""Perform a simulation of vehicles on a ring road.
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 ring road.
"""
sim_params = AimsunParams(sim_step=0.5, render=True, emission_path='data')
if render is not None:
sim_params.render = render
vehicles = VehicleParams()
vehicles.add(veh_id="idm",
acceleration_controller=(IDMController, {}),
num_vehicles=22)
env_params = EnvParams()
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
net_params = NetParams(additional_params=additional_net_params)
initial_config = InitialConfig(bunching=20)
network = RingNetwork(name="sugiyama",
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env = TestEnv(env_params, sim_params, network, simulator='aimsun')
return Experiment(env)
# name of the experiment
exp_tag='aimsun_small_template',
# name of the flow environment the experiment is running on
env_name=TestEnv,
# name of the network class the experiment is running on
network=Network,
# simulator that is used by the experiment
simulator='aimsun',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=AimsunParams(sim_step=0.1,
render=True,
emission_path='data',
replication_name="Replication 930",
centroid_config_name="Centroid Configuration 910"),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(horizon=3000, ),
# network-related parameters (see flow.core.params.NetParams and the
# network's documentation or ADDITIONAL_NET_PARAMS component)
net=NetParams(inflows=InFlows(), template=template_path),
# vehicles to be placed in the network at the start of a rollout (see
# flow.core.params.VehicleParams)
veh=vehicles,
)
def bottleneck_example(flow_rate,
horizon,
restart_instance=False,
render=None):
"""
Perform a simulation of vehicles on a bottleneck.
Parameters
----------
flow_rate : float
total inflow rate of vehicles into the bottleneck
horizon : int
time horizon
restart_instance: bool, optional
whether to restart the instance upon reset
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 bottleneck.
"""
if render is None:
render = False
sim_params = AimsunParams(sim_step=0.5,
render=render,
restart_instance=restart_instance)
vehicles = VehicleParams()
vehicles.add(veh_id="human", num_vehicles=1)
additional_env_params = {
"target_velocity": 40,
"max_accel": 1,
"max_decel": 1,
"lane_change_duration": 5,
"add_rl_if_exit": False,
"disable_tb": DISABLE_TB,
"disable_ramp_metering": DISABLE_RAMP_METER
}
env_params = EnvParams(horizon=horizon,
additional_params=additional_env_params)
inflow = InFlows()
inflow.add(veh_type="human",
edge="1",
vehsPerHour=flow_rate,
departLane="random",
departSpeed=10)
traffic_lights = TrafficLightParams()
if not DISABLE_TB:
traffic_lights.add(node_id="2")
if not DISABLE_RAMP_METER:
traffic_lights.add(node_id="3")
additional_net_params = {"scaling": SCALING, "speed_limit": 30 / 3.6}
net_params = NetParams(inflows=inflow,
additional_params=additional_net_params)
initial_config = InitialConfig(spacing="random",
min_gap=5,
lanes_distribution=float("inf"),
edges_distribution=["2", "3", "4", "5"])
network = BottleneckNetwork(name="bay_bridge_toll",
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config,
traffic_lights=traffic_lights)
env = BottleneckEnv(env_params, sim_params, network, simulator='aimsun')
return Experiment(env)
def grid_example(render=None):
"""
Perform a simulation of vehicles on a grid.
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 and balanced traffic lights on a grid.
"""
inner_length = 300
long_length = 500
short_length = 300
N_ROWS = 2
N_COLUMNS = 3
num_cars_left = 20
num_cars_right = 20
num_cars_top = 20
num_cars_bot = 20
tot_cars = (num_cars_left + num_cars_right) * N_COLUMNS \
+ (num_cars_top + num_cars_bot) * N_ROWS
grid_array = {
"short_length": short_length,
"inner_length": inner_length,
"long_length": long_length,
"row_num": N_ROWS,
"col_num": N_COLUMNS,
"cars_left": num_cars_left,
"cars_right": num_cars_right,
"cars_top": num_cars_top,
"cars_bot": num_cars_bot
}
sim_params = AimsunParams(sim_step=0.5, render=True)
if render is not None:
sim_params.render = render
vehicles = VehicleParams()
vehicles.add(veh_id="human", num_vehicles=tot_cars)
env_params = EnvParams(additional_params=ADDITIONAL_ENV_PARAMS)
tl_logic = TrafficLightParams(baseline=False)
phases = [{
"duration": "31",
"minDur": "8",
"maxDur": "45",
"yellow": "3",
"state": "GGGrrrGGGrrr"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"yellow": "3",
"state": "yyyrrryyyrrr"
}, {
"duration": "31",
"minDur": "8",
"maxDur": "45",
"yellow": "3",
"state": "rrrGGGrrrGGG"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"yellow": "3",
"state": "rrryyyrrryyy"
}]
tl_logic.add("center0", phases=phases, programID=1)
tl_logic.add("center1", phases=phases, programID=1)
tl_logic.add("center2", tls_type="actuated", phases=phases, programID=1)
tl_logic.add("center3", phases=phases, programID=1)
tl_logic.add("center4", phases=phases, programID=1)
tl_logic.add("center5", tls_type="actuated", phases=phases, programID=1)
additional_net_params = {
"grid_array": grid_array,
"speed_limit": 35,
"horizontal_lanes": 1,
"vertical_lanes": 1
}
net_params = NetParams(no_internal_links=False,
additional_params=additional_net_params)
initial_config = InitialConfig(spacing='custom')
scenario = SimpleGridScenario(name="grid-intersection",
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config,
traffic_lights=tl_logic)
env = AccelEnv(env_params, sim_params, scenario, simulator='aimsun')
return Experiment(env)
def run_task(*_):
"""Implement the run_task method needed to run experiments with rllab."""
sim_params = AimsunParams(sim_step=0.5, render=False, seed=0)
vehicles = VehicleParams()
vehicles.add(veh_id="rl",
acceleration_controller=(RLController, {}),
routing_controller=(ContinuousRouter, {}),
num_vehicles=1)
vehicles.add(veh_id="idm",
acceleration_controller=(IDMController, {}),
routing_controller=(ContinuousRouter, {}),
num_vehicles=21)
additional_env_params = {
"target_velocity": 8,
"ring_length": None,
"max_accel": 1,
"max_decel": 1
}
env_params = EnvParams(horizon=HORIZON,
additional_params=additional_env_params,
warmup_steps=1500)
additional_net_params = {
"length": 230,
"lanes": 1,
"speed_limit": 30,
"resolution": 40
}
net_params = NetParams(additional_params=additional_net_params)
initial_config = InitialConfig(spacing="uniform", bunching=50)
print("XXX name", exp_tag)
scenario = LoopScenario(exp_tag,
vehicles,
net_params,
initial_config=initial_config)
env_name = "WaveAttenuationPOEnv"
simulator = 'aimsun'
pass_params = (env_name, sim_params, vehicles, env_params, net_params,
initial_config, scenario, simulator)
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=(3, 3),
)
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(),
# Get the flow_params object.
module = __import__("exp_configs.non_rl", fromlist=[flags.exp_config])
flow_params = getattr(module, flags.exp_config).flow_params
# Get the custom callables for the runner.
if hasattr(getattr(module, flags.exp_config), "custom_callables"):
callables = getattr(module, flags.exp_config).custom_callables
else:
callables = None
flow_params['sim'].render = not flags.no_render
flow_params['simulator'] = 'aimsun' if flags.aimsun else 'traci'
# If Aimsun is being called, replace SumoParams with AimsunParams.
if flags.aimsun:
sim_params = AimsunParams()
sim_params.__dict__.update(flow_params['sim'].__dict__)
flow_params['sim'] = sim_params
# Specify an emission path if they are meant to be generated.
if flags.gen_emission:
flow_params['sim'].emission_path = "./data"
# Create the flow_params object
fp_ = flow_params['exp_tag']
dir_ = flow_params['sim'].emission_path
with open(os.path.join(dir_, "{}.json".format(fp_)), 'w') as outfile:
json.dump(flow_params,
outfile,
cls=FlowParamsEncoder,
sort_keys=True,