sumo_headless=True,
seed=seed,
# envision_record_data_replay_path="./data_replay",
)
for episode in episodes(n=num_episodes):
agent = agent_spec.build_agent()
observations = env.reset()
episode.record_scenario(env.scenario_log)
dones = {"__all__": False}
while not dones["__all__"]:
agent_obs = observations[AGENT_ID]
agent_action = agent.act(agent_obs)
observations, rewards, dones, infos = env.step({AGENT_ID: agent_action})
episode.record_step(observations, rewards, dones, infos)
env.close()
if __name__ == "__main__":
parser = default_argument_parser("single-agent-example")
args = parser.parse_args()
main(
scenarios=args.scenarios,
headless=args.headless,
num_episodes=args.episodes,
seed=args.seed,
)
seed=seed,
)
for episode in episodes(n=num_episodes):
agent = agent_spec.build_agent()
observations = env.reset()
episode.record_scenario(env.scenario_log)
dones = {"__all__": False}
while not dones["__all__"]:
agent_obs = observations[AGENT_ID]
agent_action = agent.act(agent_obs)
observations, rewards, dones, infos = env.step(
{AGENT_ID: agent_action})
episode.record_step(observations, rewards, dones, infos)
env.close()
if __name__ == "__main__":
parser = default_argument_parser("human-in-the-loop-example")
args = parser.parse_args()
main(
scenarios=args.scenarios,
sim_name=args.sim_name,
headless=args.headless,
num_episodes=args.episodes,
seed=args.seed,
)
for episode in episodes(n=num_episodes):
agent = open_agent_spec.build_agent()
observations = env.reset()
episode.record_scenario(env.scenario_log)
dones = {"__all__": False}
while not dones["__all__"]:
agent_obs = observations[AGENT_ID]
agent_action = agent.act(agent_obs)
observations, rewards, dones, infos = env.step(
{AGENT_ID: agent_action})
episode.record_step(observations, rewards, dones, infos)
del agent
env.close()
if __name__ == "__main__":
parser = default_argument_parser("OpEn-trajectory-optimizer-example")
args = parser.parse_args()
main(
scenarios=args.scenarios,
sim_name=args.sim_name,
headless=args.headless,
num_episodes=args.episodes,
seed=args.seed,
)
# 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()
if __name__ == "__main__":
parser = default_argument_parser("history-vehicles-replacement-example")
args = parser.parse_args()
main(
scenarios=args.scenarios,
headless=args.headless,
seed=args.seed,
)
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())
# TODO: save observations for imitation learning
if __name__ == "__main__":
parser = default_argument_parser("observation-collection-example")
args = parser.parse_args()
main(
scenarios=args.scenarios,
headless=args.headless,
seed=args.seed,
)
sumo_headless=True,
visdom=False,
seed=seed,
timestep_sec=0.1,
)
if max_episode_steps is None:
max_episode_steps = 1000
for episode in episodes(n=num_episodes):
env.reset()
episode.record_scenario(env.scenario_log)
for _ in range(max_episode_steps):
env.step({})
episode.record_step({}, {}, {}, {})
env.close()
if __name__ == "__main__":
parser = default_argument_parser("egoless-example")
args = parser.parse_args()
main(
scenarios=args.scenarios,
headless=args.headless,
num_episodes=args.episodes,
seed=args.seed,
)
agents = {
agent_id: agent_spec.build_agent()
for agent_id, agent_spec in agent_specs.items()
}
observations = env.reset()
episode.record_scenario(env.scenario_log)
dones = {"__all__": False}
while not dones["__all__"]:
actions = {
agent_id: agents[agent_id].act(agent_obs)
for agent_id, agent_obs in observations.items()
}
observations, rewards, dones, infos = env.step(actions)
episode.record_step(observations, rewards, dones, infos)
env.close()
if __name__ == "__main__":
parser = default_argument_parser("multi-agent-example")
args = parser.parse_args()
main(
scenarios=args.scenarios,
headless=args.headless,
num_episodes=args.episodes,
seed=args.seed,
)
seed,
):
ray.init()
ray.wait([
train.remote(
training_scenarios,
evaluation_scenarios,
headless,
num_episodes,
seed,
)
])
if __name__ == "__main__":
parser = default_argument_parser("pytorch-example")
parser.add_argument(
"--evaluation-scenario",
default="scenarios/loop",
help="The scenario to use for evaluation.",
type=str,
)
args = parser.parse_args()
main(
training_scenarios=args.scenarios,
evaluation_scenarios=[args.evaluation_scenario],
headless=args.headless,
num_episodes=args.num_episodes,
seed=args.seed,
)
# envision_record_data_replay_path="./data_replay",
)
for episode in episodes(n=num_episodes):
agent = agent_spec.build_agent()
observations = env.reset()
episode.record_scenario(env.scenario_log)
dones = {"__all__": False}
while not dones["__all__"]:
agent_obs = observations[AGENT_ID]
agent_action = agent.act(agent_obs)
observations, rewards, dones, infos = env.step(
{AGENT_ID: agent_action})
episode.record_step(observations, rewards, dones, infos)
env.close()
if __name__ == "__main__":
parser = default_argument_parser("trajectory-tracking-agent-example")
args = parser.parse_args()
main(
scenarios=args.scenarios,
sim_name=args.sim_name,
headless=args.headless,
num_episodes=args.episodes,
seed=args.seed,
)
# )
parser.add_argument("--headless",
default=True,
action="store_true",
help="Turn on headless mode")
parser.add_argument("--seed",
type=int,
default=1234,
help="Seed for scenario generation")
# parser.add_argument(
# "--log_dir",
# default="./log/results",
# type=str,
# help="Path to store RLlib log and checkpoints, default is ./log/results",
# )
# parser.add_argument("--config_file", "-f", type=str, required=True)
# parser.add_argument("--restore_path", type=str, default=None)
# parser.add_argument("--num_workers", type=int, default=1, help="RLlib num workers")
# parser.add_argument("--cluster", action="store_true")
# parser.add_argument(
# "--num_episodes", type=int, default=1000, help="num of episode"
# )
return parser.parse_args()
if __name__ == "__main__":
parser = default_argument_parser("Benchmark server")
args = parser.parse_args()
main(scenarios=args.scenarios, headless=args.headless, seed=args.seed)
if episode.sim_time - episode_sim_time_frame_with_visible_object > 0.5:
break
else:
episode_sim_time_frame_with_visible_object = episode.sim_time
# remove scenes less than 6 seconds
if episode.sim_time - episode_sim_time_epoch < 9.99:
rm(f"{OUTPUT_DIR}/frames/scene-{scene_idx:04d}/")
rm(f'{OUTPUT_DIR}/annotations/scene-{scene_idx:04d}_instances_ann.csv'
)
rm(f'{OUTPUT_DIR}/ego_poses/scene-{scene_idx:04d}_ego_pose.csv')
time.sleep(2)
if scene_idx >= end_scene_idx:
break
env.close()
if __name__ == "__main__":
parser = default_argument_parser("data-collector-agent")
args = parser.parse_args()
main(
scenarios=args.scenarios,
sim_name=args.sim_name,
headless=args.headless,
num_episodes=args.episodes,
seed=args.seed,
)
