'resolution': 40
})
from flow.core.params import InitialConfig
initial_config = InitialConfig(spacing="uniform", perturbation=1)
from flow.core.params import TrafficLightParams
traffic_lights = TrafficLightParams()
from flow.envs.ring.accel import AccelEnv
from flow.core.params import SumoParams
sim_params = SumoParams(sim_step=0.1, render=True, emission_path='data')
from flow.envs.ring.accel import ADDITIONAL_ENV_PARAMS
print(ADDITIONAL_ENV_PARAMS)
from flow.core.params import EnvParams
env_params = EnvParams(additional_params=ADDITIONAL_ENV_PARAMS)
from flow.core.experiment import Experiment
flow_params = dict(
exp_tag='ring_example',
env_name=AccelEnv,
network=RingNetwork,
def traffic_light_grid_mxn_exp_setup(row_num=1,
col_num=1,
sim_params=None,
vehicles=None,
env_params=None,
net_params=None,
initial_config=None,
tl_logic=None):
"""
Create an environment and network pair for traffic light grid 1x1 test experiments.
Parameters
----------
row_num: int, optional
number of horizontal rows of edges in the traffic light grid network
col_num: int, optional
number of vertical columns of edges in the traffic light grid network
sim_params : flow.core.params.SumoParams
sumo-related configuration parameters, defaults to a time step of 1s
and no sumo-imposed failsafe on human or rl vehicles
vehicles : Vehicles type
vehicles to be placed in the network, default is 5 vehicles per edge
for a total of 20 vehicles with an IDM acceleration controller and
GridRouter routing controller.
env_params : flow.core.params.EnvParams
environment-specific parameters, defaults to a environment with
failsafes, where other parameters do not matter for non-rl runs
net_params : flow.core.params.NetParams
network-specific configuration parameters, defaults to a 1x1 traffic
light grid with traffic lights on
initial_config : flow.core.params.InitialConfig
specifies starting positions of vehicles, defaults to evenly
distributed vehicles across the length of the network
tl_logic: flow.core.params.TrafficLightParams
specifies logic of any traffic lights added to the system
"""
logging.basicConfig(level=logging.WARNING)
if tl_logic is None:
tl_logic = TrafficLightParams(baseline=False)
if sim_params is None:
# set default sim_params configuration
sim_params = SumoParams(sim_step=1, render=False)
if vehicles is None:
vehicles_per_edge = 5
num_edges = 2 * (row_num + col_num)
total_vehicles = num_edges * vehicles_per_edge
vehicles = VehicleParams()
vehicles.add(veh_id="idm",
acceleration_controller=(IDMController, {}),
car_following_params=SumoCarFollowingParams(min_gap=2.5,
tau=1.1,
max_speed=30),
routing_controller=(GridRouter, {}),
num_vehicles=total_vehicles)
if env_params is None:
# set default env_params configuration
additional_env_params = {
"target_velocity": 50,
"switch_time": 3.0,
"tl_type": "controlled",
"discrete": False
}
env_params = EnvParams(additional_params=additional_env_params,
horizon=100)
if net_params is None:
# set default net_params configuration
total_vehicles = vehicles.num_vehicles
num_entries = 2 * row_num + 2 * col_num
assert total_vehicles % num_entries == 0, "{} total vehicles should " \
"be divisible by {" \
"}".format(total_vehicles,
num_entries)
grid_array = {
"short_length": 100,
"inner_length": 300,
"long_length": 3000,
"row_num": row_num,
"col_num": col_num,
"cars_left": int(total_vehicles / num_entries),
"cars_right": int(total_vehicles / num_entries),
"cars_top": int(total_vehicles / num_entries),
"cars_bot": int(total_vehicles / num_entries)
}
additional_net_params = {
"length": 200,
"lanes": 2,
"speed_limit": 35,
"resolution": 40,
"grid_array": grid_array,
"horizontal_lanes": 1,
"vertical_lanes": 1
}
net_params = NetParams(additional_params=additional_net_params)
if initial_config is None:
# set default initial_config configuration
initial_config = InitialConfig(spacing="custom",
additional_params={"enter_speed": 30})
flow_params = dict(
# name of the experiment
exp_tag="Grid1x1Test",
# name of the flow environment the experiment is running on
env_name=TrafficLightGridTestEnv,
# name of the network class the experiment is running on
network=TrafficLightGridNetwork,
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=sim_params,
# environment related parameters (see flow.core.params.EnvParams)
env=env_params,
# network-related parameters (see flow.core.params.NetParams and the
# network's documentation or ADDITIONAL_NET_PARAMS component)
net=net_params,
# vehicles to be placed in the network at the start of a rollout (see
# flow.core.params.VehicleParams)
veh=vehicles,
# parameters specifying the positioning of vehicles upon initialization/
# reset (see flow.core.params.InitialConfig)
initial=initial_config,
# traffic lights to be introduced to specific nodes (see
# flow.core.params.TrafficLightParams)
tls=tl_logic)
# create the network
network = TrafficLightGridNetwork(name="Grid1x1Test",
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config,
traffic_lights=tl_logic)
# create the environment
env = TrafficLightGridTestEnv(env_params=env_params,
sim_params=sim_params,
network=network)
# reset the environment
env.reset()
return env, network, flow_params
# name of the experiment
exp_tag="merge_4_Sim_Number100_Initial_Angel405_PunishDelay_RL10",
# name of the flow environment the experiment is running on
env_name=MergePOEnvPunishDelay,
# name of the network class the experiment is running on
network=MergeNetwork,
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(
restart_instance=True,
sim_step=0.5,
render=False,
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
horizon=HORIZON,
sims_per_step=2,
warmup_steps=0,
additional_params={
"max_accel": 9,
"max_decel": 9,
"target_velocity": 30,
"num_rl": NUM_RL,
"max_num_vehicles": VEHICLE_NUMBER,
"main_rl": MAIN_RL,
"3merge_i696_ALLHUMAN_horizon1500_wamrup0_SM9_inflow2000_merge200_depart10_noheadway",
# name of the flow environment the experiment is running on
#env_name=MergePOEnv,
env_name=MergePOEnv,
# name of the scenario class the experiment is running on
network=Network,
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(
no_step_log=False, # this disables log writing?
sim_step=0.5, # Daniel updated from osm.sumocfg
lateral_resolution=0.25, # determines lateral discretization of lanes
render=
False, #True, # False for training, True for debugging
restart_instance=True,
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
horizon=HORIZON,
sims_per_step=2, #5,
warmup_steps=0,
additional_params={
"max_accel": 1.5,
"max_decel": 1.5,
"target_velocity": 30,
"num_rl":
NUM_RL, # used by WaveAttenuationMergePOEnv e.g. to fix action dimension
def para_produce_rl(HORIZON=3000, NUM_AUTOMATED=4):
# time horizon of a single rollout
HORIZON = 3000
# number of rollouts per training iteration
N_ROLLOUTS = 20
# number of parallel workers
N_CPUS = 2
# number of automated vehicles. Must be less than or equal to 22.
NUM_AUTOMATED = NUM_AUTOMATED
# We evenly distribute the automated vehicles in the network.
num_human = 22 - NUM_AUTOMATED
humans_remaining = num_human
vehicles = VehicleParams()
for i in range(NUM_AUTOMATED):
# Add one automated vehicle.
vehicles.add(veh_id="rl_{}".format(i),
acceleration_controller=(RLController, {}),
routing_controller=(ContinuousRouter, {}),
num_vehicles=1)
# Add a fraction of the remaining human vehicles.
vehicles_to_add = round(humans_remaining / (NUM_AUTOMATED - i))
humans_remaining -= vehicles_to_add
vehicles.add(veh_id="human_{}".format(i),
acceleration_controller=(IDMController, {
"noise": 0.2
}),
car_following_params=SumoCarFollowingParams(min_gap=0),
routing_controller=(ContinuousRouter, {}),
num_vehicles=vehicles_to_add)
flow_params = dict(
# name of the experiment
exp_tag="multiagent_ring",
# name of the flow environment the experiment is running on
env_name=MultiAgentWaveAttenuationPOEnv,
# name of the network class the experiment is running on
network=RingNetwork,
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(sim_step=0.1, render=False, restart_instance=False),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
horizon=HORIZON,
warmup_steps=750,
clip_actions=False,
additional_params={
"max_accel": 1,
"max_decel": 1,
"ring_length": [220, 270],
},
),
# network-related parameters (see flow.core.params.NetParams and the
# network's documentation or ADDITIONAL_NET_PARAMS component)
net=NetParams(additional_params={
"length": 260,
"lanes": 1,
"speed_limit": 30,
"resolution": 40,
}, ),
# vehicles to be placed in the network at the start of a rollout (see
# flow.core.params.VehicleParams)
veh=vehicles,
# parameters specifying the positioning of vehicles upon initialization/
# reset (see flow.core.params.InitialConfig)
initial=InitialConfig())
flow_params['env'].horizon = HORIZON
return flow_params
speed_mode='no_collide',
num_vehicles=1)
flow_params = dict(
# name of the experiment
exp_tag='figure_eight_intersection_control',
# name of the flow environment the experiment is running on
env_name='AccelEnv',
# name of the scenario class the experiment is running on
scenario='Figure8Scenario',
# sumo-related parameters (see flow.core.params.SumoParams)
sumo=SumoParams(
sim_step=0.1,
render=False,
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
horizon=HORIZON,
additional_params={
'target_velocity': 20,
'max_accel': 3,
'max_decel': 3,
},
),
# network-related parameters (see flow.core.params.NetParams and the
# scenario's documentation or ADDITIONAL_NET_PARAMS component)
net=NetParams(
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=False)
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",
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env = WaveAttenuationMergePOEnv(env_params, sumo_params, scenario)
return SumoExperiment(env, scenario)
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.
"""
v_enter = 10
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 = SumoParams(sim_step=0.1, render=True)
if render is not None:
sim_params.render = render
vehicles = VehicleParams()
vehicles.add(
veh_id="human",
routing_controller=(GridRouter, {}),
num_vehicles=tot_cars)
env_params = EnvParams(additional_params=ADDITIONAL_ENV_PARAMS)
tl_logic = TrafficLightParams(baseline=False)
phases = [{
"duration": "31",
"minDur": "8",
"maxDur": "45",
"state": "GrGrGrGrGrGr"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"state": "yryryryryryr"
}, {
"duration": "31",
"minDur": "8",
"maxDur": "45",
"state": "rGrGrGrGrGrG"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"state": "ryryryryryry"
}]
tl_logic.add("center0", phases=phases, programID=1)
tl_logic.add("center1", phases=phases, programID=1)
tl_logic.add("center2", phases=phases, programID=1, tls_type="actuated")
additional_net_params = {
"grid_array": grid_array,
"speed_limit": 35,
"horizontal_lanes": 1,
"vertical_lanes": 1
}
if USE_INFLOWS:
initial_config, net_params = get_flow_params(
col_num=n_columns,
row_num=n_rows,
additional_net_params=additional_net_params)
else:
initial_config, net_params = get_non_flow_params(
enter_speed=v_enter,
add_net_params=additional_net_params)
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)
return Experiment(env)
initial_config, net_params = \
get_non_flow_params(V_ENTER, additional_net_params)
flow_params = dict(
# name of the experiment
exp_tag='green_wave',
# name of the flow environment the experiment is running on
env_name='PO_TrafficLightGridEnv',
# name of the scenario class the experiment is running on
scenario='SimpleGridScenario',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(
sim_step=1,
render=False,
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
horizon=HORIZON,
additional_params=additional_env_params,
),
# network-related parameters (see flow.core.params.NetParams and the
# scenario's documentation or ADDITIONAL_NET_PARAMS component)
net=net_params,
# vehicles to be placed in the network at the start of a rollout (see
# flow.core.vehicles.Vehicles)
veh=vehicles,
edge='8649816#9',
vehs_per_hour=200,
departSpeed=10,
departLane="random",
departPos="last")
initial_config = InitialConfig(spacing="random", perturbation=1)
net_params = NetParams(inflows=inflow,
osm_path='Kenmore.osm',
no_internal_links=False)
# create the remainding parameters
env_params = EnvParams()
env_params.additional_params = osm_traffic_env.ADDITIONAL_ENV_PARAMS
sim_params = SumoParams(render=True, restart_instance=True)
# initial_config = InitialConfig()
vehicles = VehicleParams()
vehicles.add('human',
num_vehicles=100,
acceleration_controller=(IDMController, {}),
routing_controller=(OSMRouter, {}))
# create the scenario
scenario = OSMScenario(name='Kenmore',
net_params=net_params,
initial_config=initial_config,
vehicles=vehicles)
# create the environment
env = OSMTrafficTestEnvironment(env_params=env_params,
def get_flow_params(config):
"""Returns Flow experiment parameters, given an experiment result folder
Parameters
----------
config : dict
stored RLlib configuration dict
Returns
-------
dict
Dict of flow parameters, like net_params, env_params, vehicle
characteristics, etc
"""
# collect all data from the json file
flow_params = json.loads(config['env_config']['flow_params'])
# reinitialize the vehicles class from stored data
veh = Vehicles()
for veh_params in flow_params["veh"]:
module = __import__(
"flow.controllers",
fromlist=[veh_params['acceleration_controller'][0]])
acc_class = getattr(module, veh_params['acceleration_controller'][0])
lc_class = getattr(module, veh_params['lane_change_controller'][0])
acc_controller = (acc_class, veh_params['acceleration_controller'][1])
lc_controller = (lc_class, veh_params['lane_change_controller'][1])
rt_controller = None
if veh_params['routing_controller'] is not None:
rt_class = getattr(module, veh_params['routing_controller'][0])
rt_controller = (rt_class, veh_params['routing_controller'][1])
sumo_cf_params = SumoCarFollowingParams()
sumo_cf_params.__dict__ = veh_params["sumo_car_following_params"]
sumo_lc_params = SumoLaneChangeParams()
sumo_lc_params.__dict__ = veh_params["sumo_lc_params"]
del veh_params["sumo_car_following_params"], \
veh_params["sumo_lc_params"], \
veh_params["acceleration_controller"], \
veh_params["lane_change_controller"], \
veh_params["routing_controller"]
veh.add(acceleration_controller=acc_controller,
lane_change_controller=lc_controller,
routing_controller=rt_controller,
sumo_car_following_params=sumo_cf_params,
sumo_lc_params=sumo_lc_params,
**veh_params)
# convert all parameters from dict to their object form
sumo = SumoParams()
sumo.__dict__ = flow_params["sumo"].copy()
net = NetParams()
net.__dict__ = flow_params["net"].copy()
net.in_flows = InFlows()
if flow_params["net"]["in_flows"]:
net.in_flows.__dict__ = flow_params["net"]["in_flows"].copy()
env = EnvParams()
env.__dict__ = flow_params["env"].copy()
initial = InitialConfig()
if "initial" in flow_params:
initial.__dict__ = flow_params["initial"].copy()
tls = TrafficLights()
if "tls" in flow_params:
tls.__dict__ = flow_params["tls"].copy()
flow_params["sumo"] = sumo
flow_params["env"] = env
flow_params["initial"] = initial
flow_params["net"] = net
flow_params["veh"] = veh
flow_params["tls"] = tls
return flow_params
def bay_bridge_bottleneck_example(render=None, use_traffic_lights=False):
"""Perform a simulation of the toll portion of the Bay Bridge.
This consists of the toll booth and sections of the road leading up to it.
Parameters
----------
render : bool, optional
specifies whether to use sumo's gui during execution
use_traffic_lights: bool, optional
whether to activate the traffic lights in the scenario
Note
----
Unlike the bay_bridge_example, inflows are always activated here.
"""
sumo_params = SumoParams(sim_step=0.4, overtake_right=True)
if render is not None:
sumo_params.render = render
sumo_car_following_params = SumoCarFollowingParams(speedDev=0.2)
sumo_lc_params = SumoLaneChangeParams(
model="LC2013", lcCooperative=0.2, lcSpeedGain=15)
vehicles = Vehicles()
vehicles.add(
veh_id="human",
acceleration_controller=(SumoCarFollowingController, {}),
routing_controller=(BayBridgeRouter, {}),
speed_mode="all_checks",
lane_change_mode="no_lat_collide",
sumo_car_following_params=sumo_car_following_params,
sumo_lc_params=sumo_lc_params,
num_vehicles=50)
additional_env_params = {}
env_params = EnvParams(additional_params=additional_env_params)
inflow = InFlows()
inflow.add(
veh_type="human",
edge="393649534",
probability=0.2,
departLane="random",
departSpeed=10)
inflow.add(
veh_type="human",
edge="4757680",
probability=0.2,
departLane="random",
departSpeed=10)
inflow.add(
veh_type="human",
edge="32661316",
probability=0.2,
departLane="random",
departSpeed=10)
inflow.add(
veh_type="human",
edge="90077193#0",
vehs_per_hour=2000,
departLane="random",
departSpeed=10)
net_params = NetParams(
inflows=inflow, no_internal_links=False, netfile=NETFILE)
# download the netfile from AWS
if use_traffic_lights:
my_url = "https://s3-us-west-1.amazonaws.com/flow.netfiles/" \
"bay_bridge_TL_all_green.net.xml"
else:
my_url = "https://s3-us-west-1.amazonaws.com/flow.netfiles/" \
"bay_bridge_junction_fix.net.xml"
my_file = urllib.request.urlopen(my_url)
data_to_write = my_file.read()
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)), NETFILE),
"wb+") as f:
f.write(data_to_write)
initial_config = InitialConfig(
spacing="uniform", # "random",
min_gap=15)
scenario = BayBridgeTollScenario(
name="bay_bridge_toll",
generator_class=BayBridgeTollGenerator,
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env = BayBridgeEnv(env_params, sumo_params, scenario)
return SumoExperiment(env, scenario)
def minicity_example(render=None,
save_render=None,
sight_radius=None,
pxpm=None,
show_radius=None):
"""
Perform a simulation of vehicles on modified minicity of University of
Delaware.
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 the minicity scenario.
"""
sim_params = SumoParams(sim_step=0.25)
# update sim_params values if provided as inputs
sim_params.render = render or sim_params.render
sim_params.save_render = save_render or sim_params.save_render
sim_params.sight_radius = sight_radius or sim_params.sight_radius
sim_params.pxpm = pxpm or sim_params.pxpm
sim_params.show_radius = show_radius or sim_params.show_radius
vehicles = VehicleParams()
vehicles.add(veh_id="idm",
acceleration_controller=(IDMController, {}),
routing_controller=(MinicityRouter, {}),
car_following_params=SumoCarFollowingParams(speed_mode=1, ),
lane_change_params=SumoLaneChangeParams(
lane_change_mode="no_lat_collide", ),
initial_speed=0,
num_vehicles=90)
vehicles.add(veh_id="rl",
acceleration_controller=(RLController, {}),
routing_controller=(MinicityRouter, {}),
car_following_params=SumoCarFollowingParams(
speed_mode="obey_safe_speed", ),
initial_speed=0,
num_vehicles=10)
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)
initial_config = InitialConfig(spacing="random", min_gap=5)
scenario = MiniCityScenario(name="minicity",
vehicles=vehicles,
initial_config=initial_config,
net_params=net_params)
env = AccelEnv(env_params, sim_params, scenario)
return Experiment(env)
def minicity_example(render=None,
save_render=None,
sight_radius=None,
pxpm=None,
show_radius=None):
"""
Perform a simulation of vehicles on modified minicity of University of
Delaware.
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 the minicity scenario.
"""
sumo_params = SumoParams(sim_step=0.25)
if render is not None:
sumo_params.render = render
if save_render is not None:
sumo_params.save_render = save_render
if sight_radius is not None:
sumo_params.sight_radius = sight_radius
if pxpm is not None:
sumo_params.pxpm = pxpm
if show_radius is not None:
sumo_params.show_radius = show_radius
vehicles = Vehicles()
vehicles.add(veh_id="idm",
acceleration_controller=(IDMController, {}),
routing_controller=(MinicityRouter, {}),
speed_mode=1,
lane_change_mode="no_lat_collide",
initial_speed=0,
num_vehicles=90)
vehicles.add(veh_id="rl",
acceleration_controller=(RLController, {}),
routing_controller=(MinicityRouter, {}),
speed_mode="no_collide",
initial_speed=0,
num_vehicles=10)
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)
initial_config = InitialConfig(spacing="random", min_gap=5)
scenario = MiniCityScenario(name="minicity",
vehicles=vehicles,
initial_config=initial_config,
net_params=net_params)
env = AccelEnv(env_params, sumo_params, scenario)
return SumoExperiment(env, scenario)
flow_params = dict(
# name of the experiment
exp_tag='Lanechange',
# name of the flow environment the experiment is running on
env_name=LaneChangeAccelEnv,
# name of the network class the experiment is running on
network=LanechangeNetwork,
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(
render=True,
lateral_resolution=1.0,
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
horizon=2000,
additional_params=ADDITIONAL_ENV_PARAMS.copy(),
),
# network-related parameters (see flow.core.params.NetParams and the
# network's documentation or ADDITIONAL_NET_PARAMS component)
net=NetParams(
inflows=inflow,
additional_params=ADDITIONAL_NET_PARAMS.copy(),
),
# name of the experiment
exp_tag="bottleneck_0",
# name of the flow environment the experiment is running on
env_name="DesiredVelocityEnv",
# name of the scenario class the experiment is running on
scenario="BottleneckScenario",
# name of the generator used to create/modify network configuration files
generator="BottleneckGenerator",
# sumo-related parameters (see flow.core.params.SumoParams)
sumo=SumoParams(
sim_step=0.5,
sumo_binary="sumo",
print_warnings=False,
restart_instance=True,
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
warmup_steps=40,
sims_per_step=1,
horizon=HORIZON,
additional_params=additional_env_params,
),
# network-related parameters (see flow.core.params.NetParams and the
# scenario's documentation or ADDITIONAL_NET_PARAMS component)
net=NetParams(
in_flows=inflow,
}),
routing_controller=(ContinuousRouter, {}),
car_following_params=SumoCarFollowingParams(speed_mode='aggressive', ),
num_vehicles=50)
vehicles.add(veh_id="rl",
lane_change_controller=(SimLaneChangeController, {}),
lane_change_params=SumoLaneChangeParams(
lane_change_mode="aggressive", ),
acceleration_controller=(RLController, {}),
routing_controller=(ContinuousRouter, {}),
car_following_params=SumoCarFollowingParams(
speed_mode='obey_safe_speed', ),
num_vehicles=1)
from flow.core.params import SumoParams
sim_params = SumoParams(sim_step=0.1, render=False)
# EnvParams specifies environment and experiment-specific parameters that either affect the training process or the dynamics of various components within the network. For the environment WaveAttenuationPOEnv, these parameters are used to dictate bounds on the accelerations of the autonomous vehicles, as well as the range of ring lengths (and accordingly network densities) the agent is trained on.
#Finally, it is important to specify here the horizon of the experiment, which is the duration of one episode (during which the RL-agent acquire data).
from flow.core.params import EnvParams
env_params = EnvParams(
# length of one rollout
horizon=HORIZON,
warmup_steps=750,
clip_actions=False,
additional_params={
# maximum acceleration of autonomous vehicles
"max_accel": 1,
def make_flow_params(n_rows, n_columns, edge_inflow):
"""
Generate the flow params for the experiment.
:param n_rows:
:param n_columns:
:param edge_inflow:
:return:
"""
# we place a sufficient number of vehicles to ensure they confirm with the
# total number specified above. We also use a "right_of_way" speed mode to
# support traffic light compliance
vehicles = VehicleParams()
num_vehicles = (N_LEFT + N_RIGHT) * n_columns + (N_BOTTOM + N_TOP) * n_rows
vehicles.add(
veh_id="human",
acceleration_controller=(SimCarFollowingController, {}),
car_following_params=SumoCarFollowingParams(
min_gap=2.5,
max_speed=V_ENTER,
decel=7.5, # avoid collisions at emergency stops
speed_mode="right_of_way",
),
routing_controller=(GridRouter, {}),
num_vehicles=num_vehicles)
# inflows of vehicles are place on all outer edges (listed here)
outer_edges = []
outer_edges += ["left{}_{}".format(n_rows, i) for i in range(n_columns)]
outer_edges += ["right0_{}".format(i) for i in range(n_rows)]
outer_edges += ["bot{}_0".format(i) for i in range(n_rows)]
outer_edges += ["top{}_{}".format(i, n_columns) for i in range(n_rows)]
# equal inflows for each edge (as dictate by the EDGE_INFLOW constant)
inflow = InFlows()
for edge in outer_edges:
inflow.add(veh_type="human",
edge=edge,
vehs_per_hour=edge_inflow,
departLane="free",
departSpeed=V_ENTER)
flow_params = dict(
# name of the experiment
exp_tag="grid_0_{}x{}_i{}_multiagent".format(n_rows, n_columns,
edge_inflow),
# name of the flow environment the experiment is running on
env_name='MultiTrafficLightGridPOEnv',
# name of the scenario class the experiment is running on
scenario="SimpleGridScenario",
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(
restart_instance=True,
sim_step=1,
render=False,
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
horizon=HORIZON,
additional_params={
"target_velocity": 50,
"switch_time": 3,
"num_observed": 2,
"discrete": False,
"tl_type": "actuated",
"num_local_edges": 4,
"num_local_lights": 4,
},
),
# network-related parameters (see flow.core.params.NetParams and the
# scenario's documentation or ADDITIONAL_NET_PARAMS component)
net=NetParams(
inflows=inflow,
no_internal_links=False,
additional_params={
"speed_limit": V_ENTER + 5, # inherited from grid0 benchmark
"grid_array": {
"short_length": SHORT_LENGTH,
"inner_length": INNER_LENGTH,
"long_length": LONG_LENGTH,
"row_num": n_rows,
"col_num": n_columns,
"cars_left": N_LEFT,
"cars_right": N_RIGHT,
"cars_top": N_TOP,
"cars_bot": N_BOTTOM,
},
"horizontal_lanes": 1,
"vertical_lanes": 1,
},
),
# vehicles to be placed in the network at the start of a rollout (see
# flow.core.params.VehicleParams)
veh=vehicles,
# parameters specifying the positioning of vehicles upon initialization
# or reset (see flow.core.params.InitialConfig)
initial=InitialConfig(
spacing='custom',
shuffle=True,
),
)
return flow_params
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,
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 = 1353.6 # just from checking the new inflow
# check that the first inflow rate is approximately what the seeded
# value expects it to be
for _ in range(500):
env.step(rl_actions=None)
self.assertAlmostEqual(
env.k.vehicle.get_inflow_rate(250)/expected_inflow, 1, 1)
from rllab.envs.gym_env import GymEnv
from rllab.envs.normalized_env import normalize
from rllab.misc.instrument import run_experiment_lite
from rllab.algos.ppo import PPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.policies.gaussian_gru_policy import GaussianGRUPolicy
SCALING = 1
NUM_LANES = 4 * SCALING # number of lanes in the widest highway
DISABLE_TB = True
DISABLE_RAMP_METER = True
AV_FRAC = .1
N_CPUS = 32
i = 0
sim_params = SumoParams(
sim_step=0.5, render=False, restart_instance=True)
vehicles = VehicleParams()
vehicles.add(
veh_id="human",
lane_change_controller=(SimLaneChangeController, {}),
routing_controller=(ContinuousRouter, {}),
car_following_params=SumoCarFollowingParams(
speed_mode=9,
),
lane_change_params=SumoLaneChangeParams(
lane_change_mode=0, # 1621,#0b100000101,
),
num_vehicles=1 * SCALING)
flow_params = dict(
# name of the experiment
exp_tag="ring_human",
# name of the flow environment the experiment is running on
env_name=WaveAttenuationPOEnv,
# name of the network class the experiment is running on
network=RingNetwork,
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(
sim_step=0.1,
render=True,
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
horizon=HORIZON,
warmup_steps=750,
clip_actions=False,
additional_params={
"max_accel": 1,
"max_decel": 1,
"ring_length": [220, 270],
},
),
# network-related parameters (see flow.core.params.NetParams and the
# name of the experiment
exp_tag="1x1_DECENTRALIZED_Thesis",
# name of the flow environment the experiment is running on
env_name=DeCentralizedGridEnv,
# name of the network class the experiment is running on
network=TrafficLightGridNetwork,
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(
restart_instance=True,
sim_step=1,
render=False,
emission_path='~/flow/data',
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
horizon=200,
additional_params={
"target_velocity": 11,
"switch_time": 4,
"yellow_phase_duration": 4,
"num_observed": 2,
"discrete": False,
"tl_type": "actuated",
"num_local_edges": 4,
"num_local_lights": 4,
def para_produce_rl(HORIZON=3000):
# Create default environment parameters
env_params = EnvParams()
# Vehicle definition
vehicles = VehicleParams()
num_vehicles = 1
vehicles.add(
veh_id="human",
routing_controller=(GridRouter, {}),
lane_change_controller=(SimLaneChangeController, {}),
car_following_params=SumoCarFollowingParams(
min_gap=2.5,
decel=7.5, # avoid collisions at emergency stops
),
lane_change_params=SumoLaneChangeParams(
lane_change_mode=1621,
),
num_vehicles=num_vehicles)
# whether to allow turns at intersections
ALLOW_TURNS = False
# initialize traffic lights, used when you want define your own traffic lights
tl_logic = TrafficLightParams(baseline=False) # To see static traffic lights in action, the `TrafficLightParams` object should be instantiated with `baseline=False`
# when use off_ramp_grid.net.xml file, you should use a phase state example as "GGGgrrrrGGGgrrrr"
# when use off_ramp_grid_turn.net.xml file, you should use a phase state example as "GGGggrrrrrGGGggrrrrr"
if ALLOW_TURNS:
phases = [{
"duration": "31",
"minDur": "8",
"maxDur": "45",
# for actuated traffic lights, you can add these optional values below
# "maxGap": int, describes the maximum time gap between successive vehicle sthat will cause the current phase to be prolonged
# "detectorGap": int, determines the time distance between the (automatically generated) detector and the stop line in seconds
# "showDetectors": bool, toggles whether or not detectors are shown in sumo-gui
"state": "GGGggrrrrrGGGggrrrrr"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"state": "yyyyyrrrrryyyyyrrrrr"
}, {
"duration": "31",
"minDur": "8",
"maxDur": "45",
"state": "rrrrrGGGggrrrrrGGGgg"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"state": "rrrrryyyyyrrrrryyyyy"
}]
tl_logic.add("center0", phases=phases, programID=1, detectorGap=1,tls_type="actuated")
tl_logic.add("center1", phases=phases, programID=1, detectorGap=1,tls_type="actuated")
tl_logic.add("center2", phases=phases, programID=1, detectorGap=1,tls_type="actuated")
tl_logic.add("center3", phases=phases, programID=1, detectorGap=1,tls_type="actuated")
else:
phases = [{
"duration": "31",
"minDur": "8",
"maxDur": "45",
# for actuated traffic lights, you can add these optional values below
# "maxGap": int, describes the maximum time gap between successive vehicle sthat will cause the current phase to be prolonged
# "detectorGap": int, determines the time distance between the (automatically generated) detector and the stop line in seconds
# "showDetectors": bool, toggles whether or not detectors are shown in sumo-gui
"state": "GGGgrrrrGGGgrrrr"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"state": "yyyyrrrryyyyrrrr"
}, {
"duration": "31",
"minDur": "8",
"maxDur": "45",
"state": "rrrrGGGgrrrrGGGg"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"state": "rrrryyyyrrrryyyy"
}]
# THIS IS A BUG THAT I DON'T KNOW WHY IT HAPPENS!!!!!!
phase0 = [{
"duration": "31",
"minDur": "8",
"maxDur": "45",
"state": "GGrrGGrrGGrrGGrr"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"state": "yyrryyrryyrryyrr"
}, {
"duration": "31",
"minDur": "8",
"maxDur": "45",
"state": "rrGGrrGGrrGGrrGG"
}, {
"duration": "6",
"minDur": "3",
"maxDur": "6",
"state": "rryyrryyrryyrryy"
}]
tl_logic.add("center0", phases=phases, programID=1, detectorGap=1,tls_type="actuated")
tl_logic.add("center1", phases=phases, programID=1, detectorGap=1,tls_type="actuated")
tl_logic.add("center2", phases=phases, programID=1, detectorGap=1,tls_type="actuated")
tl_logic.add("center3", phases=phases, programID=1, detectorGap=1,tls_type="actuated")
flow_params = dict(
exp_tag='offramp_multiagent_inflow_1.0_speed_20',
env_name=MultiTrafficLightGridPOEnv,
network=offRampGrid,
simulator='traci',
sim=SumoParams(
sim_step=0.1,
render=False,
#emission_path='./data',
restart_instance=True,
),
env=EnvParams(
horizon=3000, additional_params=ADDITIONAL_ENV_PARAMS.copy(),
),
net=net_params,
veh=vehicles,
initial=initial_config,
# used when you define your own traffic lights
#tls=tl_logic,
)
#flow_params['env'].horizon = HORIZON
return flow_params
# name of the experiment (for multiple policies!)
exp_tag="multimultiagent-traffic-light",
# name of the flow environment the experiment is running on
env_name=MultiTrafficLightGridPOEnv,
# name of the network class the experiment is running on
network=TrafficLightGridNetwork,
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(
restart_instance=True,
sim_step=SIM_STEP,
render=False,
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
horizon=HORIZON,
additional_params={
"target_velocity": V_TARGET,
"switch_time": SWITCH_TIME,
"num_observed": CARS_OBSERVED,
"discrete": DISCRETE,
"tl_type": "actuated",
"num_local_edges": LOCAL_EDGES,
"num_local_lights": LOCAL_LIGHTS,
},
acceleration_controller=(RLController, {}),
lane_change_controller=(StaticLaneChanger, {}),
# routing_controller=(HighwayRouter, {}),
car_following_params=SumoCarFollowingParams(
speed_mode="obey_safe_speed", ),
lane_change_params=SumoLaneChangeParams(lane_change_mode=256,
model="SL2015",
lc_impatience="0.1",
lc_time_to_impatience="1.0"),
num_vehicles=0)
from flow.core.params import SumoParams
sim_params = SumoParams(
sim_step=0.2,
render=False,
lateral_resolution=1.0,
restart_instance=True,
)
import os
inflow = InFlows()
inflow.add(
veh_type="human",
edge="WC",
# depart_lane="best",
depart_lane=1,
arrivalLane=0,
probability=0.1,
depart_speed="random",
)
def run_task(_):
"""Implement the run_task method needed to run experiments with rllab."""
sumo_params = SumoParams(sumo_binary="sumo-gui",
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(in_flows=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 variable_lanes_exp_setup(sim_params=None,
vehicles=None,
env_params=None,
net_params=None,
initial_config=None,
traffic_lights=None):
"""
Create an environment and network variable-lane ring road.
Each edge in this network can have a different number of lanes. Used for
test purposes.
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 = {
"length": 230,
"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()
if traffic_lights is None:
# set default to no traffic lights
traffic_lights = TrafficLightParams()
flow_params = dict(
# name of the experiment
exp_tag="VariableLaneRingRoadTest",
# name of the flow environment the experiment is running on
env_name=AccelEnv,
# name of the network class the experiment is running on
network=VariableLanesNetwork,
# simulator that is used by the experiment
simulator='traci',
# sumo-related parameters (see flow.core.params.SumoParams)
sim=sim_params,
# environment related parameters (see flow.core.params.EnvParams)
env=env_params,
# network-related parameters (see flow.core.params.NetParams and the
# network's documentation or ADDITIONAL_NET_PARAMS component)
net=net_params,
# vehicles to be placed in the network at the start of a rollout (see
# flow.core.params.VehicleParams)
veh=vehicles,
# parameters specifying the positioning of vehicles upon initialization/
# reset (see flow.core.params.InitialConfig)
initial=initial_config,
# traffic lights to be introduced to specific nodes (see
# flow.core.params.TrafficLightParams)
tls=traffic_lights,
)
# create the network
network = VariableLanesNetwork(name="VariableLaneRingRoadTest",
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,
network=network)
# reset the environment
env.reset()
return env, network, flow_params
def get_flow_params(fixed_boundary,
stopping_penalty,
acceleration_penalty,
evaluate=False,
multiagent=False,
imitation=False):
"""Return the flow-specific parameters of the single lane highway network.
Parameters
----------
fixed_boundary : bool
specifies whether the boundary conditions update in between resets
stopping_penalty : bool
whether to include a stopping penalty
acceleration_penalty : bool
whether to include a regularizing penalty for accelerations by the AVs
evaluate : bool
whether to compute the evaluation reward
multiagent : bool
whether the automated vehicles are via a single-agent policy or a
shared multi-agent policy with the actions of individual vehicles
assigned by a separate policy call
imitation : bool
whether to use the imitation environment
Returns
-------
dict
flow-related parameters, consisting of the following keys:
* exp_tag: name of the experiment
* env_name: environment class of the flow environment the experiment
is running on. (note: must be in an importable module.)
* network: network class the experiment uses.
* simulator: simulator that is used by the experiment (e.g. aimsun)
* sim: simulation-related parameters (see flow.core.params.SimParams)
* env: environment related parameters (see flow.core.params.EnvParams)
* net: network-related parameters (see flow.core.params.NetParams and
the network's documentation or ADDITIONAL_NET_PARAMS component)
* veh: vehicles to be placed in the network at the start of a rollout
(see flow.core.params.VehicleParams)
* initial (optional): parameters affecting the positioning of vehicles
upon initialization/reset (see flow.core.params.InitialConfig)
* tls (optional): traffic lights to be introduced to specific nodes
(see flow.core.params.TrafficLightParams)
"""
# steps to run before the agent is allowed to take control (set to lower
# value during testing)
warmup_steps = 50 if os.environ.get("TEST_FLAG") else 500
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
additional_net_params.update({
# length of the highway
"length": 2500,
# number of lanes
"lanes": 1,
# speed limit for all edges
"speed_limit": 30,
# number of edges to divide the highway into
"num_edges": 2,
# whether to include a ghost edge of length 500m. This edge is provided
# a different speed limit.
"use_ghost_edge": True,
# speed limit for the ghost edge
"ghost_speed_limit": END_SPEED,
# length of the cell imposing a boundary
"boundary_cell_length": 300,
})
vehicles = VehicleParams()
inflows = InFlows()
# human vehicles
vehicles.add(
"human",
num_vehicles=0,
acceleration_controller=(IDMController, {
"a": 1.3,
"b": 2.0,
"noise": 0.3 if INCLUDE_NOISE else 0.0
}),
car_following_params=SumoCarFollowingParams(min_gap=0.5),
lane_change_params=SumoLaneChangeParams(
model="SL2015",
lc_sublane=2.0,
),
)
inflows.add(veh_type="human",
edge="highway_0",
vehs_per_hour=int(TRAFFIC_FLOW * (1 - PENETRATION_RATE)),
depart_lane="free",
depart_speed=TRAFFIC_SPEED,
name="idm_highway_inflow")
# automated vehicles
vehicles.add(
"rl",
num_vehicles=0,
acceleration_controller=(RLController, {}),
)
inflows.add(veh_type="rl",
edge="highway_0",
vehs_per_hour=int(TRAFFIC_FLOW * PENETRATION_RATE),
depart_lane="free",
depart_speed=TRAFFIC_SPEED,
name="rl_highway_inflow")
# SET UP THE FLOW PARAMETERS
if multiagent:
if imitation:
env_name = None # to be added later
else:
env_name = AVOpenMultiAgentEnv
else:
if imitation:
env_name = AVOpenImitationEnv
else:
env_name = AVOpenEnv
return dict(
# name of the experiment
exp_tag="highway",
# name of the flow environment the experiment is running on
env_name=env_name,
# name of the network class the experiment is running on
network=HighwayNetwork,
# simulator that is used by the experiment
simulator="traci",
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(evaluate=evaluate,
horizon=HORIZON,
warmup_steps=warmup_steps,
sims_per_step=3,
additional_params={
"max_accel": 0.5,
"max_decel": 0.5,
"target_velocity": 10,
"stopping_penalty": stopping_penalty,
"acceleration_penalty": acceleration_penalty,
"inflows": None if fixed_boundary else INFLOWS,
"rl_penetration": PENETRATION_RATE,
"num_rl": 10,
"control_range": [500, 2300],
"expert_model": (IDMController, {
"a": 1.3,
"b": 2.0,
}),
}),
# sumo-related parameters (see flow.core.params.SumoParams)
sim=SumoParams(
sim_step=0.4,
render=False,
restart_instance=True,
use_ballistic=True,
),
# network-related parameters (see flow.core.params.NetParams and the
# network's documentation or ADDITIONAL_NET_PARAMS component)
net=NetParams(inflows=inflows,
additional_params=additional_net_params),
# vehicles to be placed in the network at the start of a rollout (see
# flow.core.params.VehicleParams)
veh=vehicles,
# parameters specifying the positioning of vehicles upon init/reset
# (see flow.core.params.InitialConfig)
initial=InitialConfig(),
)
# name of the experiment
exp_tag="bottleneck_1",
# name of the flow environment the experiment is running on
env_name="DesiredVelocityEnv",
# name of the scenario class the experiment is running on
scenario="BottleneckScenario",
# name of the generator used to create/modify network configuration files
generator="BottleneckGenerator",
# sumo-related parameters (see flow.core.params.SumoParams)
sumo=SumoParams(
sim_step=0.5,
render=False,
print_warnings=False,
restart_instance=True,
),
# environment related parameters (see flow.core.params.EnvParams)
env=EnvParams(
warmup_steps=40,
sims_per_step=1,
horizon=HORIZON,
additional_params=additional_env_params,
),
# network-related parameters (see flow.core.params.NetParams and the
# scenario's documentation or ADDITIONAL_NET_PARAMS component)
net=NetParams(
inflows=inflow,
def test_no_junctions_highway(self):
additional_net_params = {
"length": 100,
"lanes": 3,
"speed_limit": 30,
"resolution": 40,
"num_edges": 1
}
net_params = NetParams(additional_params=additional_net_params)
vehicles = VehicleParams()
vehicles.add(veh_id="test",
acceleration_controller=(RLController, {}),
num_vehicles=3,
initial_speed=1.0)
# Test Cases
# 1. If there's only one vehicle in each lane, we should still
# find one leader and one follower for the central vehicle
initial_config = InitialConfig(lanes_distribution=float("inf"))
initial_config.spacing = "custom"
initial_pos = {
"start_positions": [('highway_0', 20), ('highway_0', 30),
('highway_0', 10)],
"start_lanes": [1, 2, 0]
}
initial_config.additional_params = initial_pos
env, scenario = highway_exp_setup(sim_params=SumoParams(sim_step=0.1,
render=False),
net_params=net_params,
vehicles=vehicles,
initial_config=initial_config)
env.reset()
# test the central car
# test_0 is car to test in central lane
# test_1 should be leading car in lane 2
# test_2 should be trailing car in lane 0
actual_lane_leaders = env.k.vehicle.get_lane_leaders("test_0")
expected_lane_leaders = ["", "", "test_1"]
self.assertTrue(actual_lane_leaders == expected_lane_leaders)
actual_lane_headways = env.k.vehicle.get_lane_headways("test_0")
expected_lane_headways = [1000, 1000, 5.0]
np.testing.assert_array_almost_equal(actual_lane_headways,
expected_lane_headways)
actual_lane_followers = env.k.vehicle.get_lane_followers("test_0")
expected_lane_followers = ["test_2", "", ""]
self.assertTrue(actual_lane_followers == expected_lane_followers)
actual_lane_tailways = env.k.vehicle.get_lane_tailways("test_0")
expected_lane_tailways = [5.0, 1000, 1000]
np.testing.assert_array_almost_equal(actual_lane_tailways,
expected_lane_tailways)
# test the leader/follower speed methods
expected_leader_speed = [0.0, 0.0, 1.0]
actual_leader_speed = env.k.vehicle.get_lane_leaders_speed("test_0")
np.testing.assert_array_almost_equal(actual_leader_speed,
expected_leader_speed)
expected_follower_speed = [1.0, 0.0, 0.0]
actual_follower_speed = env.k.vehicle.get_lane_followers_speed(
"test_0")
np.testing.assert_array_almost_equal(actual_follower_speed,
expected_follower_speed)
# Next, test the case where all vehicles are on the same
# edge and there's two vehicles in each lane
# Cases to test
# 1. For lane 0, should find a leader and follower for tested car
# 2. For lane 1, both vehicles are behind the test car
# 3. For lane 2, both vehicles are in front of the tested car
# 4. For lane 3, one vehicle in front and one behind the tested car
additional_net_params = {
"length": 100,
"lanes": 4,
"speed_limit": 30,
"resolution": 40,
"num_edges": 1
}
net_params = NetParams(additional_params=additional_net_params)
vehicles = VehicleParams()
vehicles.add(veh_id="test",
acceleration_controller=(RLController, {}),
num_vehicles=9,
initial_speed=1.0)
initial_config = InitialConfig(lanes_distribution=float("inf"))
initial_config.spacing = "custom"
initial_pos = {
"start_positions": [
('highway_0', 50),
('highway_0', 60),
('highway_0', 40),
('highway_0', 40),
('highway_0', 30),
('highway_0', 60),
('highway_0', 70),
('highway_0', 60),
('highway_0', 40),
],
"start_lanes": [0, 0, 0, 1, 1, 2, 2, 3, 3]
}
initial_config.additional_params = initial_pos
env, scenario = highway_exp_setup(sim_params=SumoParams(sim_step=0.1,
render=False),
net_params=net_params,
vehicles=vehicles,
initial_config=initial_config)
env.reset()
actual_lane_leaders = env.k.vehicle.get_lane_leaders("test_0")
expected_lane_leaders = ["test_1", "", "test_5", "test_7"]
self.assertTrue(actual_lane_leaders == expected_lane_leaders)
actual_lane_headways = env.k.vehicle.get_lane_headways("test_0")
expected_lane_headways = [5.0, 1000, 5.0, 5.0]
np.testing.assert_array_almost_equal(actual_lane_headways,
expected_lane_headways)
actual_lane_followers = env.k.vehicle.get_lane_followers("test_0")
expected_lane_followers = ["test_2", "test_3", "", "test_8"]
self.assertTrue(actual_lane_followers == expected_lane_followers)
actual_lane_tailways = env.k.vehicle.get_lane_tailways("test_0")
expected_lane_tailways = [5.0, 5.0, 1000, 5.0]
np.testing.assert_array_almost_equal(actual_lane_tailways,
expected_lane_tailways)
# test the leader/follower speed methods
expected_leader_speed = [1.0, 0.0, 1.0, 1.0]
actual_leader_speed = env.k.vehicle.get_lane_leaders_speed("test_0")
np.testing.assert_array_almost_equal(actual_leader_speed,
expected_leader_speed)
expected_follower_speed = [1.0, 1.0, 0.0, 1.0]
actual_follower_speed = env.k.vehicle.get_lane_followers_speed(
"test_0")
np.testing.assert_array_almost_equal(actual_follower_speed,
expected_follower_speed)
# Now test if all the vehicles are on different edges and
# different lanes
additional_net_params = {
"length": 100,
"lanes": 3,
"speed_limit": 30,
"resolution": 40,
"num_edges": 3
}
net_params = NetParams(additional_params=additional_net_params)
vehicles = VehicleParams()
vehicles.add(veh_id="test",
acceleration_controller=(RLController, {}),
num_vehicles=3,
initial_speed=1.0)
# Test Cases
# 1. If there's only one vehicle in each lane, we should still
# find one leader and one follower for the central vehicle
initial_config = InitialConfig(lanes_distribution=float("inf"))
initial_config.spacing = "custom"
initial_pos = {
"start_positions": [('highway_1', 50 - (100 / 3.0)),
('highway_2', 75 - (2 * 100 / 3.0)),
('highway_0', 25)],
"start_lanes": [1, 2, 0]
}
initial_config.additional_params = initial_pos
env, scenario = highway_exp_setup(sim_params=SumoParams(sim_step=0.1,
render=False),
net_params=net_params,
vehicles=vehicles,
initial_config=initial_config)
env.reset()
# test the central car
# test_0 is car to test in central lane
# test_1 should be leading car in lane 2
# test_2 should be trailing car in lane 0
actual_lane_leaders = env.k.vehicle.get_lane_leaders("test_0")
expected_lane_leaders = ["", "", "test_1"]
self.assertTrue(actual_lane_leaders == expected_lane_leaders)
actual_lane_headways = env.k.vehicle.get_lane_headways("test_0")
expected_lane_headways = [1000, 1000, 19.996667]
np.testing.assert_array_almost_equal(actual_lane_headways,
expected_lane_headways)
actual_lane_followers = env.k.vehicle.get_lane_followers("test_0")
expected_lane_followers = ["test_2", "", ""]
self.assertTrue(actual_lane_followers == expected_lane_followers)
actual_lane_tailways = env.k.vehicle.get_lane_tailways("test_0")
expected_lane_tailways = [19.996667, 1000, 1000]
np.testing.assert_array_almost_equal(actual_lane_tailways,
expected_lane_tailways)
# test the leader/follower speed methods
expected_leader_speed = [0.0, 0.0, 1.0]
actual_leader_speed = env.k.vehicle.get_lane_leaders_speed("test_0")
np.testing.assert_array_almost_equal(actual_leader_speed,
expected_leader_speed)
expected_follower_speed = [1.0, 0.0, 0.0]
actual_follower_speed = env.k.vehicle.get_lane_followers_speed(
"test_0")
np.testing.assert_array_almost_equal(actual_follower_speed,
expected_follower_speed)
# Now test if all the vehicles are on different edges and same
# lanes
additional_net_params = {
"length": 100,
"lanes": 3,
"speed_limit": 30,
"resolution": 40,
"num_edges": 3
}
net_params = NetParams(additional_params=additional_net_params)
vehicles = VehicleParams()
vehicles.add(veh_id="test",
acceleration_controller=(RLController, {}),
num_vehicles=3,
initial_speed=1.0)
# Test Cases
# 1. If there's only one vehicle in each lane, we should still
# find one leader and one follower for the central vehicle
initial_config = InitialConfig(lanes_distribution=float("inf"))
initial_config.spacing = "custom"
initial_pos = {
"start_positions": [('highway_1', 50 - (100 / 3.0)),
('highway_2', 75 - (2 * 100 / 3.0)),
('highway_0', 25)],
"start_lanes": [0, 0, 0]
}
initial_config.additional_params = initial_pos
env, scenario = highway_exp_setup(sim_params=SumoParams(sim_step=0.1,
render=False),
net_params=net_params,
vehicles=vehicles,
initial_config=initial_config)
env.reset()
# test the central car
# test_0 is car to test in lane 0
# test_1 should be leading car in lane 0
# test_2 should be trailing car in lane 0
actual_lane_leaders = env.k.vehicle.get_lane_leaders("test_0")
expected_lane_leaders = ["test_1", "", ""]
self.assertTrue(actual_lane_leaders == expected_lane_leaders)
actual_lane_headways = env.k.vehicle.get_lane_headways("test_0")
expected_lane_headways = [19.996667, 1000, 1000]
np.testing.assert_array_almost_equal(actual_lane_headways,
expected_lane_headways)
actual_lane_followers = env.k.vehicle.get_lane_followers("test_0")
expected_lane_followers = ["test_2", "", ""]
self.assertTrue(actual_lane_followers == expected_lane_followers)
actual_lane_tailways = env.k.vehicle.get_lane_tailways("test_0")
expected_lane_tailways = [19.996667, 1000, 1000]
np.testing.assert_array_almost_equal(actual_lane_tailways,
expected_lane_tailways)
# test the leader/follower speed methods
expected_leader_speed = [1.0, 0.0, 0.0]
actual_leader_speed = env.k.vehicle.get_lane_leaders_speed("test_0")
np.testing.assert_array_almost_equal(actual_leader_speed,
expected_leader_speed)
expected_follower_speed = [1.0, 0.0, 0.0]
actual_follower_speed = env.k.vehicle.get_lane_followers_speed(
"test_0")
np.testing.assert_array_almost_equal(actual_follower_speed,
expected_follower_speed)
