def _build_smarts(self):
agent_interfaces = {
agent_id: spec.interface
for agent_id, spec in self._agent_specs.items()
}
envision = None
if not self._headless or self._envision_record_data_replay_path:
envision = Envision(
endpoint=self._envision_endpoint,
sim_name=self._sim_name,
output_dir=self._envision_record_data_replay_path,
headless=self._headless,
)
sim = SMARTS(
agent_interfaces=agent_interfaces,
traffic_sim=SumoTrafficSimulation(
headless=self._sumo_headless,
time_resolution=self._fixed_timestep_sec,
num_external_sumo_clients=self._num_external_sumo_clients,
sumo_port=self._sumo_port,
auto_start=self._sumo_auto_start,
endless_traffic=self._endless_traffic,
),
envision=envision,
fixed_timestep_sec=self._fixed_timestep_sec,
)
return sim
def __init__(self, config):
self._config = config
# XXX: These are intentionally left public at PyMARL's request
self.n_agents = config.get("n_agents", 1)
self.episode_limit = config.get("episode_limit", 1000)
self.observation_space = config.get("observation_space",
DEFAULT_OBSERVATION_SPACE)
self.action_space = config.get("action_space", DEFAULT_ACTION_SPACE)
self.state_space = config.get("state_space", DEFAULT_STATE_SPACE)
self._agent_ids = ["Agent %i" % i for i in range(self.n_agents)]
self._reward_adapter = config.get("reward_adapter",
default_reward_adapter)
self._observation_adapter = config.get("observation_adapter",
default_obs_adapter)
self._action_adapter = config.get("action_adapter",
default_action_adapter)
self._done_adapter = config.get("done_adapter",
lambda dones: list(dones.values()))
self._state_adapter = config.get("state_adapter",
default_state_adapter)
self._headless = config.get("headless", False)
self._timestep_sec = config.get("timestep_sec", 0.01)
self._observations = None
self._state = None
self._steps = 0
seed = self._config.get("seed", 42)
smarts.core.seed(seed)
self._scenarios_iterator = Scenario.scenario_variations(
config["scenarios"], self._agent_ids)
agent_interfaces = {
agent_id: AgentInterface.from_type(
config.get("agent_type", AgentType.Laner),
max_episode_steps=self.episode_limit,
debug=config.get("debug", False),
)
for i, agent_id, in enumerate(self._agent_ids)
}
envision = None
if not self._headless:
envision = Envision(
endpoint=config.get("envision_endpoint", None),
output_dir=config.get("envision_record_data_replay_path",
None),
)
self._smarts = SMARTS(
agent_interfaces=agent_interfaces,
traffic_sim=SumoTrafficSimulation(
time_resolution=self._timestep_sec),
envision=envision,
timestep_sec=self._timestep_sec,
)
def main(scenarios, headless, seed):
agent_spec = AgentSpec(
interface=AgentInterface.from_type(AgentType.Laner,
max_episode_steps=None),
agent_builder=None,
observation_adapter=None,
)
smarts = SMARTS(
agent_interfaces={},
traffic_sim=SumoTrafficSimulation(headless=True, auto_start=True),
envision=Envision(),
)
scenarios_iterator = Scenario.scenario_variations(
scenarios,
list([]),
)
smarts.reset(next(scenarios_iterator))
for _ in range(5000):
smarts.step({})
smarts.attach_sensors_to_vehicles(
agent_spec, smarts.vehicle_index.social_vehicle_ids())
obs, _, _, _ = smarts.observe_from(
smarts.vehicle_index.social_vehicle_ids())
def setup_smarts(self,
headless: bool = True,
seed: int = 42,
time_ratio: float = 1.0):
"""Do the setup of the underlying SMARTS instance."""
assert not self._smarts
if not self._state_publisher:
raise RuntimeError("must call setup_ros() first.")
self._zoo_module = rospy.get_param("~zoo_module", "zoo")
headless = rospy.get_param("~headless", headless)
seed = rospy.get_param("~seed", seed)
time_ratio = rospy.get_param("~time_ratio", time_ratio)
assert time_ratio > 0.0
self._time_ratio = time_ratio
self._smarts = SMARTS(
agent_interfaces={},
traffic_sim=None,
fixed_timestep_sec=None,
envision=None if headless else Envision(),
external_provider=True,
)
assert self._smarts.external_provider
self._last_step_time = None
with self._reset_lock:
self._reset_msg = None
self._scenario_path = None
self._agents = {}
self._agents_to_add = {}
def main(scenarios, headless, seed):
scenarios_iterator = Scenario.scenario_variations(scenarios, [])
for _ in scenarios:
scenario = next(scenarios_iterator)
agent_missions = scenario.discover_missions_of_traffic_histories()
for agent_id, mission in agent_missions.items():
scenario.set_ego_missions({agent_id: mission})
agent_spec = AgentSpec(
interface=AgentInterface.from_type(AgentType.Laner,
max_episode_steps=None),
agent_builder=KeepLaneAgent,
)
agent = agent_spec.build_agent()
smarts = SMARTS(
agent_interfaces={agent_id: agent_spec.interface},
traffic_sim=SumoTrafficSimulation(headless=True,
auto_start=True),
envision=Envision(),
)
observations = smarts.reset(scenario)
dones = {agent_id: False}
while not dones[agent_id]:
agent_obs = observations[agent_id]
agent_action = agent.act(agent_obs)
observations, rewards, dones, infos = smarts.step(
{agent_id: agent_action})
def __init__(
self,
scenarios: Sequence[str],
agent_specs,
shuffle_scenarios=True,
headless=False,
visdom=False,
timestep_sec=0.1,
seed=42,
num_external_sumo_clients=0,
sumo_headless=True,
sumo_port=None,
sumo_auto_start=True,
endless_traffic=True,
envision_endpoint=None,
envision_record_data_replay_path=None,
zoo_workers=None,
auth_key=None,
):
self._log = logging.getLogger(self.__class__.__name__)
smarts.core.seed(seed)
self._agent_specs = agent_specs
self._dones_registered = 0
self._scenarios_iterator = Scenario.scenario_variations(
scenarios, list(agent_specs.keys()), shuffle_scenarios,
)
agent_interfaces = {
agent_id: agent.interface for agent_id, agent in agent_specs.items()
}
envision_client = None
if not headless:
envision_client = Envision(
endpoint=envision_endpoint, output_dir=envision_record_data_replay_path
)
visdom_client = None
if visdom:
visdom_client = VisdomClient()
self._smarts = SMARTS(
agent_interfaces=agent_interfaces,
traffic_sim=SumoTrafficSimulation(
headless=sumo_headless,
time_resolution=timestep_sec,
num_external_sumo_clients=num_external_sumo_clients,
sumo_port=sumo_port,
auto_start=sumo_auto_start,
endless_traffic=endless_traffic,
),
envision=envision_client,
visdom=visdom_client,
timestep_sec=timestep_sec,
zoo_workers=zoo_workers,
auth_key=auth_key,
)
def test_data_replay(agent_spec, scenarios_iterator, data_replay_path, monkeypatch):
"""We stub out the websocket the Envision client writes to and store the sent data.
We do the same under Envision client's replay feature and compare that the data
sent to the websocket is the same as before.
"""
def step_through_episodes(agent_spec, smarts, scenarios_iterator):
for i in range(NUM_EPISODES):
agent = agent_spec.build_agent()
scenario = next(scenarios_iterator)
obs = smarts.reset(scenario)
done = False
while not done:
obs = agent_spec.observation_adapter(obs[AGENT_ID])
action = agent.act(obs)
action = agent_spec.action_adapter(action)
obs, _, dones, _ = smarts.step({AGENT_ID: action})
done = dones[AGENT_ID]
# 1. Inspect sent data during SMARTS simulation
# Mock WebSocketApp so we can inspect the websocket frames being sent
FakeWebSocketApp, original_sent_data = fake_websocket_app_class()
monkeypatch.setattr(websocket, "WebSocketApp", FakeWebSocketApp)
assert original_sent_data.qsize() == 0
envision = Envision(output_dir=data_replay_path)
smarts = SMARTS(
agent_interfaces={AGENT_ID: agent_spec.interface},
traffic_sim=SumoTrafficSimulation(time_resolution=TIMESTEP_SEC),
envision=envision,
timestep_sec=TIMESTEP_SEC,
)
step_through_episodes(agent_spec, smarts, scenarios_iterator)
smarts.destroy()
data_replay_path = Path(data_replay_path)
data_replay_run_paths = [x for x in data_replay_path.iterdir() if x.is_dir()]
assert len(data_replay_run_paths) == 1
jsonl_paths = list(data_replay_run_paths[0].glob("*.jsonl"))
assert len(jsonl_paths) == 1
assert original_sent_data.qsize() > 0
# 2. Inspect replay data
# Mock WebSocketApp so we can inspect the websocket frames being sent
FakeWebSocketApp, new_sent_data = fake_websocket_app_class()
monkeypatch.setattr(websocket, "WebSocketApp", FakeWebSocketApp)
assert new_sent_data.qsize() == 0
# Now read data replay
Envision.read_and_send(jsonl_paths[0], timestep_sec=TIMESTEP_SEC)
# Verify the new data matches the original data
assert original_sent_data.qsize() == new_sent_data.qsize()
for _ in range(new_sent_data.qsize()):
assert original_sent_data.get() == new_sent_data.get()
def __init__(
self,
scenarios: Sequence[str],
agent_specs,
visdom=False,
headless=False,
timestep_sec=0.1,
seed=42,
num_external_sumo_clients=0,
sumo_headless=True,
sumo_port=None,
sumo_auto_start=True,
endless_traffic=True,
envision_endpoint=None,
envision_record_data_replay_path=None,
):
self._log = logging.getLogger(self.__class__.__name__)
smarts.core.seed(seed)
self._visdom_obs_queue = None
if visdom:
self._log.debug("Running with visdom")
self._visdom_obs_queue = build_visdom_watcher_queue()
self._agent_specs = agent_specs
self._dones_registered = 0
self._scenarios_iterator = Scenario.scenario_variations(
scenarios,
list(agent_specs.keys()),
)
agent_interfaces = {
agent_id: agent.interface
for agent_id, agent in agent_specs.items()
}
envision = None
if not headless:
envision = Envision(endpoint=envision_endpoint,
output_dir=envision_record_data_replay_path)
self._smarts = SMARTS(
agent_interfaces=agent_interfaces,
traffic_sim=SumoTrafficSimulation(
headless=sumo_headless,
time_resolution=timestep_sec,
num_external_sumo_clients=num_external_sumo_clients,
sumo_port=sumo_port,
auto_start=sumo_auto_start,
endless_traffic=endless_traffic,
),
envision=envision,
timestep_sec=timestep_sec,
)
def main(scenarios: Sequence[str], headless: bool, seed: int):
agent_spec = AgentSpec(
interface=AgentInterface.from_type(AgentType.Laner,
max_episode_steps=None),
agent_builder=None,
observation_adapter=None,
)
smarts = SMARTS(
agent_interfaces={},
traffic_sim=SumoTrafficSimulation(headless=headless, auto_start=True),
envision=None if headless else Envision(),
)
scenarios_iterator = Scenario.scenario_variations(
scenarios,
list([]),
)
scenario = next(scenarios_iterator)
obs = smarts.reset(scenario)
collected_data = {}
_record_data(smarts.elapsed_sim_time, obs, collected_data)
# could also include "motorcycle" or "truck" in this set if desired
vehicle_types = frozenset({"car"})
while True:
smarts.step({})
current_vehicles = smarts.vehicle_index.social_vehicle_ids(
vehicle_types=vehicle_types)
if collected_data and not current_vehicles:
print("no more vehicles. exiting...")
break
smarts.attach_sensors_to_vehicles(agent_spec, current_vehicles)
obs, _, _, dones = smarts.observe_from(current_vehicles)
_record_data(smarts.elapsed_sim_time, obs, collected_data)
# an example of how we might save the data per car
for car, data in collected_data.items():
outfile = f"data_{scenario.name}_{scenario.traffic_history.name}_{car}.pkl"
with open(outfile, "wb") as of:
pickle.dump(data, of)
smarts.destroy()
def main(scenarios, headless, seed):
scenarios_iterator = Scenario.scenario_variations(scenarios, [])
smarts = SMARTS(
agent_interfaces={},
traffic_sim=None,
envision=None if headless else Envision(),
)
for _ in scenarios:
scenario = next(scenarios_iterator)
agent_missions = scenario.discover_missions_of_traffic_histories()
for agent_id, mission in agent_missions.items():
agent_spec = AgentSpec(
interface=AgentInterface.from_type(AgentType.LanerWithSpeed,
max_episode_steps=None),
agent_builder=KeepLaneAgent,
agent_params=scenario.traffic_history_target_speed,
)
agent = agent_spec.build_agent()
# Take control of vehicle with corresponding agent_id
smarts.switch_ego_agent({agent_id: agent_spec.interface})
# tell the traffic history provider to start traffic
# at the point when this agent enters...
traffic_history_provider = smarts.get_provider_by_type(
TrafficHistoryProvider)
assert traffic_history_provider
traffic_history_provider.start_time = mission.start_time
# agent vehicle will enter right away...
modified_mission = replace(mission, start_time=0.0)
scenario.set_ego_missions({agent_id: modified_mission})
observations = smarts.reset(scenario)
dones = {agent_id: False}
while not dones.get(agent_id, True):
agent_obs = observations[agent_id]
agent_action = agent.act(agent_obs)
observations, rewards, dones, infos = smarts.step(
{agent_id: agent_action})
smarts.destroy()
def main(scenarios, headless, seed):
agent_spec = AgentSpec(
interface=AgentInterface.from_type(AgentType.Laner,
max_episode_steps=None),
agent_builder=None,
observation_adapter=None,
)
smarts = SMARTS(
agent_interfaces={},
traffic_sim=SumoTrafficSimulation(headless=headless, auto_start=True),
envision=None if headless else Envision(),
)
scenarios_iterator = Scenario.scenario_variations(
scenarios,
list([]),
)
smarts.reset(next(scenarios_iterator))
prev_vehicles = set()
done_vehicles = set()
for _ in range(5000):
smarts.step({})
current_vehicles = smarts.vehicle_index.social_vehicle_ids()
# We explicitly watch for which agent/vehicles left the simulation here
# since we don't have a "done criteria" that detects when a vehicle's
# traffic history has played itself out.
done_vehicles = prev_vehicles - current_vehicles
prev_vehicles = current_vehicles
smarts.attach_sensors_to_vehicles(agent_spec, current_vehicles)
obs, _, _, dones = smarts.observe_from(current_vehicles)
# The `dones` returned above should be empty for traffic histories
# where all vehicles are assumed to stay on the road and not collide.
# TODO: add the following assert once the maps are accurate enough that
# we don't have any agents accidentally go off-road.
# assert not done
for v in done_vehicles:
dones[f"Agent-{v}"] = True
# TODO: save observations for imitation learning
smarts.destroy()
def setup_smarts(
self,
headless: bool = True,
seed: int = 42,
time_ratio: float = 1.0,
sumo_traffic: bool = False,
):
"""Do the setup of the underlying SMARTS instance."""
assert not self._smarts
if not self._state_publisher:
raise RuntimeError("must call setup_ros() first.")
self._zoo_module = rospy.get_param("~zoo_module", "zoo")
headless = rospy.get_param("~headless", headless)
seed = rospy.get_param("~seed", seed)
time_ratio = rospy.get_param("~time_ratio", time_ratio)
assert time_ratio > 0.0
self._time_ratio = time_ratio
traffic_sim = None
if rospy.get_param("~sumo_traffic", sumo_traffic):
from smarts.core.sumo_traffic_simulation import SumoTrafficSimulation
# Note that Sumo uses a fixed timestep, so if we have a highly-variable step rate,
# we may want to set time_resolution to a mutiple of the target_freq?
time_resolution = 1.0 / self._target_freq if self._target_freq else None
traffic_sim = SumoTrafficSimulation(
headless=headless,
time_resolution=time_resolution,
)
self._smarts = SMARTS(
agent_interfaces={},
traffic_sim=traffic_sim,
fixed_timestep_sec=None,
envision=None if headless else Envision(),
external_provider=True,
)
assert self._smarts.external_provider
self._last_step_time = None
def main(scenarios, headless, seed):
scenarios_iterator = Scenario.scenario_variations(scenarios, [])
smarts = SMARTS(
agent_interfaces={},
traffic_sim=SumoTrafficSimulation(headless=True, auto_start=True),
envision=Envision(),
)
for _ in scenarios:
scenario = next(scenarios_iterator)
agent_missions = scenario.discover_missions_of_traffic_histories()
for agent_id, mission in agent_missions.items():
agent_spec = AgentSpec(
interface=AgentInterface.from_type(AgentType.Laner,
max_episode_steps=None),
agent_builder=KeepLaneAgent,
)
agent = agent_spec.build_agent()
smarts.switch_ego_agent({agent_id: agent_spec.interface})
# required: get traffic_history_provider and set time offset
traffic_history_provider = smarts.get_provider_by_type(
TrafficHistoryProvider)
assert traffic_history_provider
traffic_history_provider.set_start_time(mission.start_time)
modified_mission = replace(mission, start_time=0.0)
scenario.set_ego_missions({agent_id: modified_mission})
observations = smarts.reset(scenario)
dones = {agent_id: False}
while not dones[agent_id]:
agent_obs = observations[agent_id]
agent_action = agent.act(agent_obs)
observations, rewards, dones, infos = smarts.step(
{agent_id: agent_action})
smarts.destroy()
def main(
script: str,
scenarios: Sequence[str],
headless: bool,
envision_record_data_replay_path: str,
seed: int,
vehicles_to_replace_randomly: int,
min_timestep_count: int,
positional_radius: int,
episodes: int,
):
assert episodes > 0
logger = logging.getLogger(script)
logger.setLevel(logging.INFO)
logger.debug("initializing SMARTS")
envision_client = None
if not headless or envision_record_data_replay_path:
envision_client = Envision(output_dir=envision_record_data_replay_path)
smarts = SMARTS(
agent_interfaces={},
traffic_sim=None,
envision=envision_client,
)
random_seed(seed)
scenarios_iterator = Scenario.scenario_variations(scenarios, [])
scenario = next(scenarios_iterator)
for episode in range(episodes):
logger.info(f"starting episode {episode}...")
def should_trigger(ctx: Dict[str, Any]) -> bool:
return ctx["elapsed_sim_time"] > 2
def on_trigger(ctx: Dict[str, Any]):
# Define agent specs to be assigned
agent_spec = AgentSpec(
interface=AgentInterface(waypoints=True, action=ActionSpaceType.Lane),
agent_builder=BasicAgent,
)
# Select a random sample from candidates
k = ctx.get("vehicles_to_replace_randomly", 0)
if k <= 0:
logger.warning(
"default (0) or negative value specified for replacement. Replacing all valid vehicle candidates."
)
sample = ctx["vehicle_candidates"]
else:
logger.info(
f"Choosing {k} vehicles randomly from {len(ctx['vehicle_candidates'])} valid vehicle candidates."
)
sample = random.sample(ctx["vehicle_candidates"], k)
assert len(sample) != 0
for veh_id in sample:
# Map selected vehicles to agent ids & specs
agent_id = f"agent-{veh_id}"
ctx["agents"][agent_id] = agent_spec.build_agent()
# Create missions based on current state and traffic history
positional, traverse = scenario.create_dynamic_traffic_history_mission(
veh_id, ctx["elapsed_sim_time"], ctx["positional_radius"]
)
# Take control of vehicles immediately
try:
# Try to assign a PositionalGoal at the last recorded timestep
smarts.add_agent_and_switch_control(
veh_id, agent_id, agent_spec.interface, positional
)
except PlanningError:
logger.warning(
f"Unable to create PositionalGoal for vehicle {veh_id}, falling back to TraverseGoal"
)
smarts.add_agent_and_switch_control(
veh_id, agent_id, agent_spec.interface, traverse
)
# Create a table of vehicle trajectory lengths, filtering out non-moving vehicles
vehicle_candidates = []
for v_id in (str(id) for id in scenario.traffic_history.all_vehicle_ids()):
traj = list(scenario.traffic_history.vehicle_trajectory(v_id))
# Find moving vehicles with more than the minimum number of timesteps
if [row for row in traj if row.speed != 0] and len(
traj
) >= min_timestep_count:
vehicle_candidates.append(v_id)
assert len(vehicle_candidates) > 0
k = vehicles_to_replace_randomly
if k > len(vehicle_candidates):
logger.warning(
f"vehicles_to_replace_randomly={k} is greater than the number of vehicle candidates ({len(vehicle_candidates)})."
)
k = len(vehicle_candidates)
# Initialize trigger and define initial context
context = {
"agents": {},
"elapsed_sim_time": 0.0,
"vehicle_candidates": vehicle_candidates,
"vehicles_to_replace_randomly": k,
"positional_radius": positional_radius,
}
trigger = Trigger(should_trigger, on_trigger)
dones = {}
observations = smarts.reset(scenario)
while not dones or not all(dones.values()):
# Update context
context["elapsed_sim_time"] = smarts.elapsed_sim_time
# Step trigger to further update context
trigger.update(context)
# Get agents from current context
agents = context["agents"]
# Step simulation
actions = {
agent_id: agents[agent_id].act(agent_obs)
for agent_id, agent_obs in observations.items()
}
logger.debug(
f"stepping @ sim_time={smarts.elapsed_sim_time} for agents={list(observations.keys())}..."
)
observations, rewards, dones, infos = smarts.step(actions)
for agent_id in agents.keys():
if dones.get(agent_id, False):
if not observations[agent_id].events.reached_goal:
logger.warning(
f"agent_id={agent_id} exited @ sim_time={smarts.elapsed_sim_time}"
)
logger.warning(f" ... with {observations[agent_id].events}")
else:
logger.info(
f"agent_id={agent_id} reached goal @ sim_time={smarts.elapsed_sim_time}"
)
logger.debug(f" ... with {observations[agent_id].events}")
del observations[agent_id]
smarts.destroy()
def __init__(
self,
scenarios: Sequence[str],
agent_specs: Dict[str, AgentSpec],
sim_name: Optional[str] = None,
shuffle_scenarios: bool = True,
headless: bool = True,
visdom: bool = False,
fixed_timestep_sec: Optional[float] = None,
seed: int = 42,
num_external_sumo_clients: int = 0,
sumo_headless: bool = True,
sumo_port: Optional[str] = None,
sumo_auto_start: bool = True,
endless_traffic: bool = True,
envision_endpoint: Optional[str] = None,
envision_record_data_replay_path: Optional[str] = None,
zoo_addrs: Optional[str] = None,
timestep_sec: Optional[
float
] = None, # for backwards compatibility (deprecated)
):
"""
Args:
scenarios (Sequence[str]): A list of scenario directories that
will be simulated.
agent_specs (Dict[str, AgentSpec]): Specification of the agents
that will run in the environment.
sim_name (Optional[str], optional): Simulation name. Defaults to
None.
shuffle_scenarios (bool, optional): If true, order of scenarios
will be randomized, else it will be maintained. Defaults to
True.
headless (bool, optional): If True, disables visualization in
Envision. Defaults to False.
visdom (bool, optional): If True, enables visualization of observed
RGB images in Visdom. Defaults to False.
fixed_timestep_sec (Optional[float], optional): Step duration for
all components of the simulation. May be None if time deltas
are externally-driven. Defaults to None.
seed (int, optional): Random number generator seed. Defaults to 42.
num_external_sumo_clients (int, optional): Number of SUMO clients
beyond SMARTS. Defaults to 0.
sumo_headless (bool, optional): If True, disables visualization in
SUMO GUI. Defaults to True.
sumo_port (Optional[str], optional): SUMO port. Defaults to None.
sumo_auto_start (bool, optional): Automatic starting of SUMO.
Defaults to True.
endless_traffic (bool, optional): SUMO's endless traffic setting.
Defaults to True.
envision_endpoint (Optional[str], optional): Envision's uri.
Defaults to None.
envision_record_data_replay_path (Optional[str], optional):
Envision's data replay output directory. Defaults to None.
zoo_addrs (Optional[str], optional): List of (ip, port) tuples of
zoo server, used to instantiate remote social agents. Defaults
to None.
timestep_sec (Optional[float], optional): [description]. Defaults
to None.
"""
self._log = logging.getLogger(self.__class__.__name__)
self.seed(seed)
if timestep_sec and not fixed_timestep_sec:
warnings.warn(
"timestep_sec has been deprecated in favor of fixed_timestep_sec. Please update your code.",
category=DeprecationWarning,
)
if not fixed_timestep_sec:
fixed_timestep_sec = timestep_sec or 0.1
self._agent_specs = agent_specs
self._dones_registered = 0
self._scenarios_iterator = Scenario.scenario_variations(
scenarios,
list(agent_specs.keys()),
shuffle_scenarios,
)
agent_interfaces = {
agent_id: agent.interface for agent_id, agent in agent_specs.items()
}
envision_client = None
if not headless or envision_record_data_replay_path:
envision_client = Envision(
endpoint=envision_endpoint,
sim_name=sim_name,
output_dir=envision_record_data_replay_path,
headless=headless,
)
visdom_client = None
if visdom:
visdom_client = VisdomClient()
all_sumo = Scenario.supports_traffic_simulation(scenarios)
traffic_sim = None
if not all_sumo:
# We currently only support the Native SUMO Traffic Provider and Social Agents for SUMO maps
if zoo_addrs:
warnings.warn("`zoo_addrs` can only be used with SUMO scenarios")
zoo_addrs = None
warnings.warn(
"We currently only support the Native SUMO Traffic Provider and Social Agents for SUMO maps."
"All scenarios passed need to be of SUMO, to enable SUMO Traffic Simulation and Social Agents."
)
pass
else:
from smarts.core.sumo_traffic_simulation import SumoTrafficSimulation
traffic_sim = SumoTrafficSimulation(
headless=sumo_headless,
time_resolution=fixed_timestep_sec,
num_external_sumo_clients=num_external_sumo_clients,
sumo_port=sumo_port,
auto_start=sumo_auto_start,
endless_traffic=endless_traffic,
)
zoo_addrs = zoo_addrs
self._smarts = SMARTS(
agent_interfaces=agent_interfaces,
traffic_sim=traffic_sim,
envision=envision_client,
visdom=visdom_client,
fixed_timestep_sec=fixed_timestep_sec,
zoo_addrs=zoo_addrs,
)
def __init__(
self,
scenarios: Sequence[str],
agent_specs: Dict[str, AgentSpec],
sim_name=None,
shuffle_scenarios=True,
headless=False,
visdom=False,
fixed_timestep_sec=None,
seed=42,
num_external_sumo_clients=0,
sumo_headless=True,
sumo_port=None,
sumo_auto_start=True,
endless_traffic=True,
envision_endpoint=None,
envision_record_data_replay_path=None,
zoo_addrs=None,
timestep_sec=None, # for backwards compatibility (deprecated)
):
self._log = logging.getLogger(self.__class__.__name__)
self.seed(seed)
if timestep_sec and not fixed_timestep_sec:
warnings.warn(
"timestep_sec has been deprecated in favor of fixed_timestep_sec. Please update your code.",
category=DeprecationWarning,
)
if not fixed_timestep_sec:
fixed_timestep_sec = timestep_sec or 0.1
self._agent_specs = agent_specs
self._dones_registered = 0
self._scenarios_iterator = Scenario.scenario_variations(
scenarios,
list(agent_specs.keys()),
shuffle_scenarios,
)
agent_interfaces = {
agent_id: agent.interface
for agent_id, agent in agent_specs.items()
}
envision_client = None
if not headless or envision_record_data_replay_path:
envision_client = Envision(
endpoint=envision_endpoint,
sim_name=sim_name,
output_dir=envision_record_data_replay_path,
headless=headless,
)
visdom_client = None
if visdom:
visdom_client = VisdomClient()
all_sumo = Scenario.supports_traffic_simulation(scenarios)
traffic_sim = None
if not all_sumo:
# We currently only support the Native SUMO Traffic Provider and Social Agents for SUMO maps
if zoo_addrs:
warnings.warn(
"`zoo_addrs` can only be used with SUMO scenarios")
zoo_addrs = None
warnings.warn(
"We currently only support the Native SUMO Traffic Provider and Social Agents for SUMO maps."
"All scenarios passed need to be of SUMO, to enable SUMO Traffic Simulation and Social Agents."
)
pass
else:
from smarts.core.sumo_traffic_simulation import SumoTrafficSimulation
traffic_sim = SumoTrafficSimulation(
headless=sumo_headless,
time_resolution=fixed_timestep_sec,
num_external_sumo_clients=num_external_sumo_clients,
sumo_port=sumo_port,
auto_start=sumo_auto_start,
endless_traffic=endless_traffic,
)
zoo_addrs = zoo_addrs
self._smarts = SMARTS(
agent_interfaces=agent_interfaces,
traffic_sim=traffic_sim,
envision=envision_client,
visdom=visdom_client,
fixed_timestep_sec=fixed_timestep_sec,
zoo_addrs=zoo_addrs,
)
def main(script: str, scenarios: Sequence[str], headless: bool, seed: int):
logger = logging.getLogger(script)
logger.setLevel(logging.INFO)
agent_spec = AgentSpec(
interface=AgentInterface.from_type(AgentType.Laner, max_episode_steps=None),
agent_builder=None,
observation_adapter=None,
)
smarts = SMARTS(
agent_interfaces={},
traffic_sim=SumoTrafficSimulation(headless=headless, auto_start=True),
envision=None if headless else Envision(),
)
scenario_list = Scenario.get_scenario_list(scenarios)
scenarios_iterator = Scenario.variations_for_all_scenario_roots(scenario_list, [])
for scenario in scenarios_iterator:
obs = smarts.reset(scenario)
collected_data = {}
_record_data(smarts.elapsed_sim_time, obs, collected_data)
# could also include "motorcycle" or "truck" in this set if desired
vehicle_types = frozenset({"car"})
# filter off-road vehicles from observations
vehicles_off_road = set()
while True:
smarts.step({})
current_vehicles = smarts.vehicle_index.social_vehicle_ids(
vehicle_types=vehicle_types
)
if collected_data and not current_vehicles:
print("no more vehicles. exiting...")
break
for veh_id in current_vehicles:
try:
smarts.attach_sensors_to_vehicles(agent_spec.interface, {veh_id})
except ControllerOutOfLaneException:
logger.warning(f"{veh_id} out of lane, skipped attaching sensors")
vehicles_off_road.add(veh_id)
valid_vehicles = {v for v in current_vehicles if v not in vehicles_off_road}
obs, _, _, dones = smarts.observe_from(valid_vehicles)
_record_data(smarts.elapsed_sim_time, obs, collected_data)
# an example of how we might save the data per car
observation_folder = "collected_observations"
if not os.path.exists(observation_folder):
os.makedirs(observation_folder)
for car, data in collected_data.items():
outfile = f"{observation_folder}/{scenario.name}_{scenario.traffic_history.name}_{car}.pkl"
with open(outfile, "wb") as of:
pickle.dump(data, of)
smarts.destroy()
def __init__(self,
scenarios: Sequence[str],
agent_specs: Dict,
shuffle_scenarios=True,
headless=False,
visdom=False,
timestep_sec=0.1,
seed=42,
num_external_sumo_clients=0,
sumo_headless=True,
sumo_port=None,
sumo_auto_start=True,
endless_traffic=True,
envision_endpoint=None,
envision_record_data_replay_path=None,
zoo_addrs=None):
self._metircs = Metric(1)
self.has_connection = False
self._log = logging.getLogger(self.__class__.__name__)
smarts.core.seed(seed) # Set seed for np and random module.
self._agent_specs = agent_specs
self._dones_registered = 0
# Setup ego.
self._ego = agent_specs[EGO_ID].build_agent()
# Setup sceanrios for benchmark.
self._scenarios_iterator = Scenario.scenario_variations(
scenarios,
list(agent_specs.keys()),
shuffle_scenarios,
)
# Setup envision and visdom.
envision_client = None
if not headless:
envision_client = Envision(
endpoint=envision_endpoint,
output_dir=envision_record_data_replay_path)
visdom_client = None
if visdom:
visdom_client = VisdomClient()
# Setup SMARTS
agent_interfaces = {
agent_id: agent.interface
for agent_id, agent in agent_specs.items()
}
self._smarts = SMARTS(
agent_interfaces=agent_interfaces,
traffic_sim=SumoTrafficSimulation(
headless=sumo_headless,
time_resolution=timestep_sec,
num_external_sumo_clients=num_external_sumo_clients,
sumo_port=sumo_port,
auto_start=sumo_auto_start,
endless_traffic=endless_traffic,
),
envision=envision_client,
visdom=visdom_client,
timestep_sec=timestep_sec,
zoo_addrs=zoo_addrs)
def main(
script: str,
scenarios: Sequence[str],
headless: bool,
seed: int,
vehicles_to_replace: int,
episodes: int,
):
assert vehicles_to_replace > 0
assert episodes > 0
logger = logging.getLogger(script)
logger.setLevel(logging.INFO)
logger.debug("initializing SMARTS")
smarts = SMARTS(
agent_interfaces={},
traffic_sim=None,
envision=None if headless else Envision(),
)
random_seed(seed)
traffic_history_provider = smarts.get_provider_by_type(
TrafficHistoryProvider)
assert traffic_history_provider
scenario_list = Scenario.get_scenario_list(scenarios)
scenarios_iterator = Scenario.variations_for_all_scenario_roots(
scenario_list, [])
for scenario in scenarios_iterator:
logger.debug("working on scenario {}".format(scenario.name))
veh_missions = scenario.discover_missions_of_traffic_histories()
if not veh_missions:
logger.warning("no vehicle missions found for scenario {}.".format(
scenario.name))
continue
veh_start_times = {
v_id: mission.start_time
for v_id, mission in veh_missions.items()
}
k = vehicles_to_replace
if k > len(veh_missions):
logger.warning(
"vehicles_to_replace={} is greater than the number of vehicle missions ({})."
.format(vehicles_to_replace, len(veh_missions)))
k = len(veh_missions)
# XXX replace with AgentSpec appropriate for IL model
agent_spec = AgentSpec(
interface=AgentInterface.from_type(AgentType.Imitation),
agent_builder=ReplayCheckerAgent,
agent_params=smarts.fixed_timestep_sec,
)
for episode in range(episodes):
logger.info(f"starting episode {episode}...")
agentid_to_vehid = {}
agent_interfaces = {}
# Build the Agents for the to-be-hijacked vehicles
# and gather their missions
agents = {}
dones = {}
ego_missions = {}
sample = {}
if scenario.traffic_history.dataset_source == "Waymo":
# For Waymo, we only hijack the vehicle that was autonomous in the dataset
waymo_ego_id = scenario.traffic_history.ego_vehicle_id
if waymo_ego_id is not None:
assert (
k == 1
), f"do not specify -k > 1 when just hijacking Waymo ego vehicle (it was {k})"
veh_id = str(waymo_ego_id)
sample = {veh_id}
else:
logger.warning(
f"Waymo ego vehicle id not mentioned in the dataset. Hijacking a random vehicle."
)
if not sample:
# For other datasets, hijack a sample of the recorded vehicles
# Pick k vehicle missions to hijack with agent
# and figure out which one starts the earliest
sample = scenario.traffic_history.random_overlapping_sample(
veh_start_times, k)
if len(sample) < k:
logger.warning(
f"Unable to choose {k} overlapping missions. allowing non-overlapping."
)
leftover = set(veh_start_times.keys()) - sample
sample.update(set(random.sample(leftover, k - len(sample))))
agent_spec.interface.max_episode_steps = max([
scenario.traffic_history.vehicle_final_exit_time(veh_id) / 0.1
for veh_id in sample
])
history_start_time = None
logger.info(f"chose vehicles: {sample}")
for veh_id in sample:
agent_id = f"ego-agent-IL-{veh_id}"
agentid_to_vehid[agent_id] = veh_id
agent_interfaces[agent_id] = agent_spec.interface
if (not history_start_time
or veh_start_times[veh_id] < history_start_time):
history_start_time = veh_start_times[veh_id]
for agent_id in agent_interfaces.keys():
agent = agent_spec.build_agent()
veh_id = agentid_to_vehid[agent_id]
agent.load_data_for_vehicle(veh_id, scenario,
history_start_time)
agents[agent_id] = agent
dones[agent_id] = False
mission = veh_missions[veh_id]
ego_missions[agent_id] = replace(
mission,
start_time=mission.start_time - history_start_time)
# Tell the traffic history provider to start traffic
# at the point when the earliest agent enters...
traffic_history_provider.start_time = history_start_time
# and all the other agents to offset their missions by this much too
scenario.set_ego_missions(ego_missions)
logger.info(f"offsetting sim_time by: {history_start_time}")
# Take control of vehicles with corresponding agent_ids
smarts.switch_ego_agents(agent_interfaces)
# Finally start the simulation loop...
logger.info(f"starting simulation loop...")
observations = smarts.reset(scenario)
while not all(done for done in dones.values()):
actions = {
agent_id: agents[agent_id].act(agent_obs)
for agent_id, agent_obs in observations.items()
}
logger.debug("stepping @ sim_time={} for agents={}...".format(
smarts.elapsed_sim_time, list(observations.keys())))
observations, rewards, dones, infos = smarts.step(actions)
for agent_id in agents.keys():
if dones.get(agent_id, False):
if not observations[agent_id].events.reached_goal:
logger.warning(
"agent_id={} exited @ sim_time={}".format(
agent_id, smarts.elapsed_sim_time))
logger.warning(" ... with {}".format(
observations[agent_id].events))
else:
logger.info(
"agent_id={} reached goal @ sim_time={}".
format(agent_id, smarts.elapsed_sim_time))
logger.debug(" ... with {}".format(
observations[agent_id].events))
del observations[agent_id]
smarts.destroy()
def __init__(self, all_args):
self.all_args = all_args
self._dones_registered = 0
self.neighbor_num = all_args.neighbor_num
self.rews_mode = all_args.rews_mode
self.n_agents = all_args.num_agents
self.use_proximity = all_args.use_proximity
self.use_discrete = all_args.use_discrete # default True
self.use_centralized_V = all_args.use_centralized_V
self.scenarios = [(all_args.scenario_path + all_args.scenario_name)]
self.agent_ids = ["Agent %i" % i for i in range(self.n_agents)]
self.obs_space_dict = self.get_obs_space_dict()
self.obs_dim = self.get_obs_dim()
# ! TODO:
self.share_obs_dim = self.get_state_dim(
) if self.use_centralized_V else self.get_obs_dim()
self.observation_space = [
gym.spaces.Box(low=-1e10, high=1e10, shape=(self.obs_dim, ))
] * self.n_agents
self.share_observation_space = [
gym.spaces.Box(low=-1e10, high=1e10, shape=(self.share_obs_dim, ))
] * self.n_agents
if self.use_discrete:
self.act_dim = 4
self.action_space = [gym.spaces.Discrete(self.act_dim)
] * self.n_agents
self.agent_type = AgentType.Vulner_with_proximity if self.use_proximity else AgentType.Vulner
else:
# TODO Add continous action space
self.agent_type = AgentType.VulnerCon_with_proximity if self.use_proximity else AgentType.VulnerCon
raise NotImplementedError
self._agent_specs = {
agent_id: AgentSpec(
interface=AgentInterface.from_type(
self.agent_type, max_episode_steps=all_args.horizon),
observation_adapter=self.get_obs_adapter(),
reward_adapter=self.get_rew_adapter(self.rews_mode,
self.neighbor_num),
action_adapter=self.get_act_adapter(),
)
for agent_id in self.agent_ids
}
self._scenarios_iterator = Scenario.scenario_variations(
self.scenarios,
list(self._agent_specs.keys()),
all_args.shuffle_scenarios,
)
self.agent_interfaces = {
agent_id: agent.interface
for agent_id, agent in self._agent_specs.items()
}
self.envision_client = None
if not all_args.headless:
self.envision_client = Envision(
endpoint=all_args.envision_endpoint,
output_dir=all_args.envision_record_data_replay_path)
self.visdom_client = None
if all_args.visdom:
self.visdom_client = VisdomClient()
self._smarts = SMARTS(
agent_interfaces=self.agent_interfaces,
traffic_sim=SumoTrafficSimulation(
headless=all_args.sumo_headless,
time_resolution=all_args.timestep_sec,
num_external_sumo_clients=all_args.num_external_sumo_clients,
sumo_port=all_args.sumo_port,
auto_start=all_args.sumo_auto_start,
endless_traffic=all_args.endless_traffic,
),
envision=self.envision_client,
visdom=self.visdom_client,
timestep_sec=all_args.timestep_sec,
zoo_workers=all_args.zoo_workers,
auth_key=all_args.auth_key,
)