def main(output_folder_path: Path):
# Set gym-carla environment
agent_config = AgentConfig.parse_file(
Path("configurations/agent_configuration.json"))
carla_config = CarlaConfig.parse_file(
Path("configurations/carla_configuration.json"))
params = {
"agent_config": agent_config,
"carla_config": carla_config,
"ego_agent_class": RLOccuMapE2ETrainingAgent,
"max_collision": 5,
}
env = gym.make('roar-occu-map-e2e-v0', params=params)
env.reset()
model_params: dict = {
"verbose": 1,
"env": env,
"render": True,
"tensorboard_log": (output_folder_path / "tensorboard").as_posix(),
"buffer_size": 10000,
"nb_rollout_steps": 100,
# "batch_size": 16,
"nb_eval_steps": 50
}
model, callbacks = setup(model_params, output_folder_path)
model = model.learn(total_timesteps=int(1e6),
callback=callbacks,
reset_num_timesteps=False)
def run(agent_class,
agent_config_file_path: Path,
carla_config_file_path: Path,
num_laps: int = 10) -> Tuple[float, int, int]:
"""
Run the agent along the track and produce a score based on certain metrics
Args:
num_laps: int number of laps that the agent should run
agent_class: the participant's agent
agent_config_file_path: agent configuration path
carla_config_file_path: carla configuration path
Returns:
float between 0 - 1 representing scores
"""
agent_config: AgentConfig = AgentConfig.parse_file(agent_config_file_path)
carla_config = CarlaConfig.parse_file(carla_config_file_path)
# hard code agent config such that it reflect competition requirements
agent_config.num_laps = num_laps
carla_runner = CarlaRunner(carla_settings=carla_config,
agent_settings=agent_config,
npc_agent_class=PurePursuitAgent,
competition_mode=True,
lap_count=num_laps)
try:
my_vehicle = carla_runner.set_carla_world()
agent = agent_class(vehicle=my_vehicle, agent_settings=agent_config)
carla_runner.start_game_loop(agent=agent, use_manual_control=False)
return compute_score(carla_runner)
except Exception as e:
print(f"something bad happened during initialization: {e}")
carla_runner.on_finish()
logging.error(f"{e}. Might be a good idea to restart Server")
return 0, 0, False
def run(agent_class, agent_config_file_path: Path,
carla_config_file_path: Path) -> Tuple[float, int, bool]:
"""
Run the agent along the track and produce a score based on certain metrics
Args:
agent_class: the participant's agent
agent_config_file_path: agent configuration path
carla_config_file_path: carla configuration path
Returns:
float between 0 - 1 representing scores
"""
agent_config = AgentConfig.parse_file(agent_config_file_path)
carla_config = CarlaConfig.parse_file(carla_config_file_path)
carla_runner = CarlaRunner(carla_settings=carla_config,
agent_settings=agent_config,
npc_agent_class=PurePursuitAgent,
competition_mode=True,
max_collision=3)
try:
my_vehicle = carla_runner.set_carla_world()
agent = agent_class(vehicle=my_vehicle, agent_settings=agent_config)
carla_runner.start_game_loop(agent=agent, use_manual_control=True)
return compute_score(carla_runner)
except Exception as e:
print(f"something bad happened during initialization: {e}")
carla_runner.on_finish()
logging.error(f"{e}. Might be a good idea to restart Server")
return 0, 0, False
def main():
agent_config = AgentConfig.parse_file(
Path("./ROAR_Sim/configurations/agent_configuration.json"))
carla_config = CarlaConfig.parse_file(
Path("./ROAR_Sim/configurations/configuration.json"))
carla_runner = CarlaRunner(carla_settings=carla_config,
agent_settings=agent_config,
npc_agent_class=PurePursuitAgent)
''' Data collection code. Currently unnecessary
# make csv file to store some data in
# we have current position x, y, z, current velocity x, y, z, next waypoint position x, y, z,
# next waypoint direction relative to the current position of the car x, y, z, steering, and throttle
csvNotes = "{}\n{}\n".format("we have current car position x, y, z, current car velocity x, y, z, next waypoint position x, y, z,",
"next waypoint direction relative to the current position of the car x, y, z, steering, and throttle")
csvHeader = "px, py, pz, vx, vy, vz, wpx, wpy, wpz, wvx, wvy, wvz, steering, throttle\n"
with open("tmp/pid_data.csv", "w") as f:
f.write(csvNotes)
f.write(csvHeader)
'''
try:
my_vehicle = carla_runner.set_carla_world()
# agent = PIDAgent(vehicle=my_vehicle, agent_settings=agent_config)
agent = LQRAgent(vehicle=my_vehicle, agent_settings=agent_config)
carla_runner.start_game_loop(agent=agent, use_manual_control=False)
except Exception as e:
logging.error(f"Something bad happened during initialization: {e}")
carla_runner.on_finish()
logging.error(f"{e}. Might be a good idea to restart Server")
def main(output_folder_path:Path):
# Set gym-carla environment
agent_config = AgentConfig.parse_file(Path("configurations/agent_configuration.json"))
carla_config = CarlaConfig.parse_file(Path("configurations/carla_configuration.json"))
params = {
"agent_config": agent_config,
"carla_config": carla_config,
"ego_agent_class": RLPIDAgent,
"max_collision": 5
}
env = gym.make('roar-pid-v0', params=params)
env.reset()
model_params: dict = {
"verbose": 1,
"render": True,
"tensorboard_log": (output_folder_path / "tensorboard").as_posix()
}
latest_model_path = find_latest_model(output_folder_path)
if latest_model_path is None:
model = DDPG(LnMlpPolicy, env=env, **model_params) # full tensorboard log can take up space quickly
else:
model = DDPG.load(latest_model_path, env=env, **model_params)
model.render = True
model.tensorboard_log = (output_folder_path / "tensorboard").as_posix()
logging_callback = LoggingCallback(model=model)
checkpoint_callback = CheckpointCallback(save_freq=1000, verbose=2, save_path=(output_folder_path / "checkpoints").as_posix())
event_callback = EveryNTimesteps(n_steps=100, callback=checkpoint_callback)
callbacks = CallbackList([checkpoint_callback, event_callback, logging_callback])
model = model.learn(total_timesteps=int(1e10), callback=callbacks, reset_num_timesteps=False)
model.save(f"pid_ddpg_{datetime.now()}")
def __init__(self):
# Set gym-carla environment
agent_config = AgentConfig.parse_file(
Path("configurations/agent_configuration.json"))
carla_config = CarlaConfig.parse_file(
Path("configurations/carla_configuration.json"))
params = {
"agent_config": agent_config,
"carla_config": carla_config,
"ego_agent_class": RLOccuMapE2ETrainingAgent,
"max_collision": 5,
}
super().__init__(params)
# action space = next waypoint
self.view_size = 200
self.max_steering_angle = 1
self.action_space = gym.spaces.Box(
low=np.array([0.4, -self.max_steering_angle]),
high=np.array([1, self.max_steering_angle]),
dtype=np.float32) # throttle, steering
self.observation_space = gym.spaces.Box(low=0,
high=1,
shape=(self.view_size,
self.view_size, 1),
dtype=np.uint8)
self.debug_info: OrderedDict = OrderedDict()
self.prev_location: Optional[Location] = None
self.prev_next_waypoint: Optional[Location] = None
self.dist_diff = 0
def main(output_folder_path: Path):
# Set gym-carla environment
agent_config = AgentConfig.parse_file(
Path("configurations/agent_configuration.json"))
carla_config = CarlaConfig.parse_file(
Path("configurations/carla_configuration.json"))
params = {
"agent_config": agent_config,
"carla_config": carla_config,
"ego_agent_class": RLLocalPlannerAgent,
"max_collision": 5,
}
env = gym.make('roar-local-planner-v0', params=params)
env.reset()
model_params: dict = {
"verbose": 1,
"render": True,
"env": env,
"n_cpu_tf_sess": None,
"buffer_size": 1000,
"nb_train_steps": 50,
"nb_rollout_steps": 100,
# "nb_eval_steps": 50,
"batch_size": 32,
}
latest_model_path = find_latest_model(Path(output_folder_path))
if latest_model_path is None:
model = DDPG(CnnPolicy, **model_params)
else:
model = DDPG.load(latest_model_path, **model_params)
tensorboard_dir = (output_folder_path / "tensorboard")
ckpt_dir = (output_folder_path / "checkpoints")
tensorboard_dir.mkdir(parents=True, exist_ok=True)
ckpt_dir.mkdir(parents=True, exist_ok=True)
model.tensorboard_log = tensorboard_dir.as_posix()
model.render = True
logging_callback = LoggingCallback(model=model)
checkpoint_callback = CheckpointCallback(save_freq=1000,
verbose=2,
save_path=ckpt_dir.as_posix())
event_callback = EveryNTimesteps(n_steps=100, callback=checkpoint_callback)
callbacks = CallbackList(
[checkpoint_callback, event_callback, logging_callback])
model = model.learn(total_timesteps=int(1e10),
callback=callbacks,
reset_num_timesteps=False)
model.save(f"local_planner_ddpg_{datetime.now()}")
def main():
try:
agent_config = AgentConfig.parse_file(Path("./ROAR_Jetson/configurations/agent_configuration.json"))
jetson_config = JetsonConfig.parse_file(Path("./ROAR_Jetson/configurations/configuration.json"))
try:
prepare(jetson_config=jetson_config)
except Exception as e:
logging.error(f"Ignoring Error during setup: {e}")
agent = PIDAgent(vehicle=Vehicle(), agent_settings=agent_config, should_init_default_cam=False)
jetson_runner = JetsonRunner(agent=agent, jetson_config=jetson_config)
jetson_runner.start_game_loop(use_manual_control=False)
except Exception as e:
print(f"Something bad happened {e}")
def main():
agent_config = AgentConfig.parse_file(Path("../ROAR/configurations/carla_configuration.json"))
carla_config = CarlaConfig.parse_file(Path("../ROAR_Sim/configurations/carla_configuration.json"))
carla_runner = CarlaRunner(carla_settings=carla_config, agent_settings=agent_config,
npc_agent_class=PurePursuitAgent)
try:
my_vehicle = carla_runner.set_carla_world()
agent = PointCloudMapRecordingAgent(vehicle=my_vehicle, agent_settings=agent_config)
carla_runner.start_game_loop(agent=agent, use_manual_control=False)
except Exception as e:
print("Ending abnormally: ", e)
carla_runner.on_finish()
logging.error(f"Hint: Might be a good idea to restart Server. ")
def main():
try:
agent_config = AgentConfig.parse_file(
Path("./ROAR_Jetson/configurations/agent_configuration.json"))
jetson_config = JetsonConfig.parse_file(
Path("./ROAR_Jetson/configurations/configuration.json"))
prepare(jetson_config=jetson_config)
agent = ForwardOnlyAgent(vehicle=Vehicle(),
agent_settings=agent_config)
jetson_runner = JetsonRunner(agent=agent, jetson_config=jetson_config)
jetson_runner.start_game_loop(use_manual_control=True)
except Exception as e:
print(f"Something bad happened {e}")
def main(output_folder_path: Path):
# Set gym-carla environment
agent_config = AgentConfig.parse_file(
Path("configurations/agent_configuration.json"))
carla_config = CarlaConfig.parse_file(
Path("configurations/carla_configuration.json"))
params = {
"agent_config": agent_config,
"carla_config": carla_config,
"ego_agent_class": RLLocalPlannerAgent,
"max_collision": 5,
}
env = gym.make('roar-local-planner-v1', params=params)
env.reset()
tensorboard_dir, ckpt_dir = prep_dir(output_folder_path)
model_params: dict = {
"verbose": 1,
"render": True,
"env": env,
"n_cpu_tf_sess": 2,
"buffer_size": 10,
"random_exploration": 0.1,
"tensorboard_log": tensorboard_dir.as_posix(),
}
latest_model_path = find_latest_model(Path(output_folder_path))
if latest_model_path is None:
model = DDPG(
LnMlpPolicy,
**model_params) # full tensorboard log can take up space quickly
else:
model = DDPG.load(latest_model_path, **model_params)
logging_callback = LoggingCallback(model=model)
checkpoint_callback = CheckpointCallback(save_freq=1000,
verbose=2,
save_path=ckpt_dir.as_posix())
event_callback = EveryNTimesteps(n_steps=100, callback=checkpoint_callback)
callbacks = CallbackList(
[checkpoint_callback, event_callback, logging_callback])
model = model.learn(total_timesteps=int(1e10),
callback=callbacks,
reset_num_timesteps=False)
model.save(f"local_planner_v1_ddpg_{datetime.now()}")
def main():
agent_config = AgentConfig.parse_file(
Path("./ROAR_Sim/configurations/agent_configuration.json"))
carla_config = CarlaConfig.parse_file(
Path("./ROAR_Sim/configurations/configuration.json"))
carla_runner = CarlaRunner(carla_settings=carla_config,
agent_settings=agent_config,
npc_agent_class=PurePursuitAgent)
try:
my_vehicle = carla_runner.set_carla_world()
agent = PIDAgent(vehicle=my_vehicle, agent_settings=agent_config)
carla_runner.start_game_loop(agent=agent, use_manual_control=False)
except Exception as e:
logging.error(f"Something bad happened during initialization: {e}")
carla_runner.on_finish()
logging.error(f"{e}. Might be a good idea to restart Server")
def main():
"""Starts game loop"""
agent_config = AgentConfig.parse_file(
Path("./ROAR_Sim/configurations/agent_configuration.json"))
carla_config = CarlaConfig.parse_file(
Path("./ROAR_Sim/configurations/configuration.json"))
carla_runner = CarlaRunner(carla_settings=carla_config,
agent_settings=agent_config,
npc_agent_class=PurePursuitAgent)
try:
my_vehicle = carla_runner.set_carla_world()
#agent = PIDAgent(vehicle=my_vehicle, agent_settings=agent_config)
#agent = OccupancyMapAgent(vehicle=my_vehicle, agent_settings=agent_config)
#agent = PurePursuitAgent(vehicle=my_vehicle, agent_settings=agent_config)
#agent = JAM1Agent_old(vehicle=my_vehicle, agent_settings=agent_config) # *** roll controller
agent = PIDRollAgent(
vehicle=my_vehicle,
agent_settings=agent_config) # *** roll controller
#agent = JAM1Agent(vehicle=my_vehicle, agent_settings=agent_config)
#agent = JAM2Agent(vehicle=my_vehicle, agent_settings=agent_config)
#agent = JAM3Agent_old(vehicle=my_vehicle, agent_settings=agent_config) # *** bstanley
#agent = JAM3Agent(vehicle=my_vehicle, agent_settings=agent_config)
# *** use to record new waypoints ***
# waypointrecord = agent.bstanley_controller.blat_stanley_controller.waypointrecord
# np.save("James_waypoints", np.array(waypointrecord))
#agent = RecordingAgent(vehicle=my_vehicle, agent_settings=agent_config)
#carla_runner.start_game_loop(agent=agent, use_manual_control=True)#*******True for manual control, False auto
carla_runner.start_game_loop(
agent=agent, use_manual_control=False
) # *******True for manual control, False auto
except Exception as e:
logging.error(f"Something bad happened during initialization: {e}")
carla_runner.on_finish()
logging.error(f"{e}. Might be a good idea to restart Server")
logging.getLogger("matplotlib").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
parser = argparse.ArgumentParser()
parser.add_argument("-auto", action='store_true', help="Enable auto control")
parser.add_argument("-m", "--mode", choices=choices, help="AR or VR [WARNING not implemented yet!]", default="vr")
parser.add_argument("-r", "--reconnect", action='store_true', help="Scan QR code to attach phone to PC")
parser.add_argument("-u", "--use_unity", action='store_true',
help="Use unity as rendering and control service")
parser.add_argument("-g", "--use_glove", help="use glove based controller by supplying its ip address!")
args = parser.parse_args()
try:
agent_config_file_path = Path("ROAR/configurations/iOS/iOS_agent_configuration.json")
ios_config_file_path = Path("ROAR_iOS/configurations/ios_config.json")
agent_config = AgentConfig.parse_file(agent_config_file_path)
ios_config: iOSConfig = iOSConfig.parse_file(ios_config_file_path)
ios_config.ar_mode = True if args.mode == "ar" else False
if args.use_glove:
try:
is_glove_online(args.use_glove, port=81)
ios_config.glove_ip_addr = args.use_glove
ios_config.should_use_glove = True
except requests.exceptions.ConnectTimeout as e:
print(f"ERROR. Cannot find Glove at that ip address {args.use_glove}. Shutting down...")
exit(0)
else:
ios_config.should_use_glove = False
success = False
if args.reconnect:
def main():
"""Starts game loop"""
carla_config = CarlaConfig.parse_file(Path("./ROAR_Sim/configurations/configuration.json"))
agent_config = AgentConfig.parse_file(Path("./ROAR_Sim/configurations/agent_configuration.json"))
"""
Pit Stop:
Use different kinds of 'set' functions at PitStop to tune/fix your own car!
"""
pitstop = PitStop(carla_config, agent_config)
pitstop.set_carla_version(version = "0.9.9")
pitstop.set_carla_sync_mode(False)
pitstop.set_autopilot_mode(True)
pitstop.set_car_color(CarlaCarColor(r = 255,g = 200,b = 00,a = 255))
pitstop.set_num_laps(num=1)
pitstop.set_output_data_folder_path("./data/output")
pitstop.set_output_data_file_name(time.strftime("%Y%m%d-%H%M%S-") + "map-waypoints")
pitstop.set_max_speed(speed = 200)
pitstop.set_target_speed(speed = 30)
print(agent_config.target_speed, " target speed")
pitstop.set_steering_boundary(boundary = (-1.0, 1.0))
pitstop.set_throttle_boundary(boundary = (0, 0.5))
pitstop.set_waypoints_look_ahead_values(values={
"60": 5,
"80": 10,
"120": 20,
"180": 50})
pid_value = {
"longitudinal_controller": {
"40": {
"Kp": 0.8,
"Kd": 0.2,
"Ki": 0
},
"60": {
"Kp": 0.5,
"Kd": 0.2,
"Ki": 0
},
"150": {
"Kp": 0.2,
"Kd": 0.1,
"Ki": 0.1
}
},
"latitudinal_controller": {
"60": {
"Kp": 0.8,
"Kd": 0.1,
"Ki": 0.1
},
"100": {
"Kp": 0.6,
"Kd": 0.2,
"Ki": 0.1
},
"150": {
"Kp": 0.5,
"Kd": 0.2,
"Ki": 0.1
}
}
}
pitstop.set_pid_values(pid_value)
"""Passing configurations to Carla and Agent"""
carla_runner = CarlaRunner(carla_settings=carla_config, # ROAR Academy: fine
agent_settings=agent_config, # ROAR Academy: fine
npc_agent_class=PurePursuitAgent)
try:
my_vehicle = carla_runner.set_carla_world()
agent = PIDAgent(vehicle=my_vehicle, agent_settings=agent_config)
# agent = WaypointGeneratigAgent(vehicle=my_vehicle, agent_settings=agent_config)
carla_runner.start_game_loop(agent=agent, use_manual_control=False) # for PIDAgent
# carla_runner.start_game_loop(agent=agent, use_manual_control=True) # for WaypointGeneratingAgent
except Exception as e:
logging.error(f"Something bad happened during initialization: {e}")
carla_runner.on_finish()
logging.error(f"{e}. Might be a good idea to restart Server")