Пример #1
0
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
Пример #2
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": 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)
Пример #3
0
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
Пример #4
0
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")
Пример #5
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": 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()}")
Пример #6
0
    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
Пример #7
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()}")
Пример #8
0
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. ")
Пример #9
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-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()}")
Пример #10
0
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")
Пример #11
0
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")
Пример #12
0
    def __init__(self, app: QtWidgets.QApplication,
                 sim_json_config_file_path: Path, **kwargs):
        super().__init__(app, Ui_SimulationConfigWindow, **kwargs)
        self.setting_list = []
        self.setting_dict = dict()
        self.dialogs = list()
        self.simulation_config = SimulationConfig()
        self.json_config_file_path: Path = sim_json_config_file_path
        self.model_info: Dict[str,
                              PydanticModelEntry] = self.fill_config_list()

        # =====================================================================================================
        # create save button and add to menu

        self.ui.actionSave = QtWidgets.QAction(self)
        self.ui.actionSave.setObjectName("actionSave")
        self.ui.actionSave.setText(
            QtCore.QCoreApplication.translate("SimulationConfigWindow",
                                              "Save"))
        self.ui.menuFile.addAction(self.ui.actionSave)
        self.ui.actionSave.triggered.connect(self.save_config)
Пример #13
0
class SimConfigWindow(BaseWindow):
    def __init__(self, app: QtWidgets.QApplication,
                 sim_json_config_file_path: Path, **kwargs):
        super().__init__(app, Ui_SimulationConfigWindow, **kwargs)
        self.setting_list = []
        self.setting_dict = dict()
        self.dialogs = list()
        self.simulation_config = SimulationConfig()
        self.json_config_file_path: Path = sim_json_config_file_path
        self.model_info: Dict[str,
                              PydanticModelEntry] = self.fill_config_list()

        # =====================================================================================================
        # create save button and add to menu

        self.ui.actionSave = QtWidgets.QAction(self)
        self.ui.actionSave.setObjectName("actionSave")
        self.ui.actionSave.setText(
            QtCore.QCoreApplication.translate("SimulationConfigWindow",
                                              "Save"))
        self.ui.menuFile.addAction(self.ui.actionSave)
        self.ui.actionSave.triggered.connect(self.save_config)

        # =====================================================================================================

    #  self.menubar.addAction(self.menuFile.menuAction())
    # self.text_change()
    #####
    # self.currentTextChanged.connect(self.onCurrentTextChanged)

    # def onCurrentTextChanged(self, text):
    #     print("\n text changed \n")

    def fill_config_list(self) -> Dict[str, Any]:
        model_info: Dict[str, Any] = dict()
        for key_name, entry in self.simulation_config.schema(
        )['properties'].items():
            if "type" not in entry:
                continue
            model_info[key_name] = entry[
                'title']  # PydanticModelEntry.parse_obj(entry)
            # pprint(entry)

        json_dict: dict = json.load(self.json_config_file_path.open('r'))
        for key_name, entry in json_dict.items():
            pass
            model_info[key_name] = entry
            # TODO update model_info, completed
            # print(key_name, entry)
        model_values = self.simulation_config.dict()
        # print("len:  ",len(model_info.items()))
        ##print(self.test_input_list)
        # print(self.setting_dict)

        for name, entry in model_values.items():
            # TODO do not populate if it is not of type [int, float, string, bool]
            if name in model_info.keys():
                entry = model_info[name]
            # type check -> present if type in ['int','float','string','bool']
            try:
                if isinstance(eval(str(entry)), dict):
                    continue
            except:
                pass
            self.add_entry_to_settings_gui(name=name, value=entry)
            # self.test_input_list.append([name,str(curr_value)])

            self.setting_dict[name] = entry
        # self.logger.debug()
        return model_info

    def add_entry_to_settings_gui(self, name: str, value: Union[str, int,
                                                                float, bool]):
        label = QtWidgets.QLabel()
        label.setText(name)
        input_field = QtWidgets.QLineEdit()  # QTextEdit()
        input_field.setText(str(value))
        input_field.setObjectName(name)
        self.setting_list.append(input_field)
        # input_field.textChanged.connect(self.on_change)
        # self.test_input_list.append(str(value))

        self.ui.formLayout.addRow(label, input_field)

    def set_listener(self):
        super(SimConfigWindow, self).set_listener()
        self.ui.pushButton_confirm.clicked.connect(self.pushButton_confirm)

    # def on_change(self,text):
    #     print("text changed: ", text)
    #     print(self.setting_list[1].text())
    #     print(self.setting_list[1].objectName())
    # print(self.setting_dict)

    def isfloat(self, value):
        try:
            float(value)
            return True
        except ValueError:
            return False

    def pushButton_confirm(self):
        self.auto_wire_window(ControlPanelWindow)
        # self.save_config()

    def save_config(self):
        """
        save config to file
        """

        sim_config_json = {}

        for widget in self.setting_list:
            # if isinstance(widget, QtWidgets.QLineEdit):
            # print(widget.objectName(),":", widget.text())
            if widget.text().isnumeric():
                sim_config_json[widget.objectName()] = int(widget.text())
            elif self.isfloat(widget.text()):
                sim_config_json[widget.objectName()] = float(widget.text())
            elif widget.text().lower() == "true":
                sim_config_json[widget.objectName()] = True
            elif widget.text().lower() == "false":
                sim_config_json[widget.objectName()] = False
            else:
                sim_config_json[widget.objectName()] = widget.text()

        json_dict: dict = json.load(self.json_config_file_path.open('r'))
        json_object = sim_config_json
        for key, entry in json_object.items():
            try:
                json_dict[key] = eval(entry)
            except:
                json_dict[key] = entry

        # current C:\Users\Zetian\Desktop\project\ROAR\ROAR_Desktop\ROAR_GUI
        # need    C:\Users\Zetian\Desktop\project\ROAR\ROAR_Sim\configurations
        # pathlib
        with open("../../ROAR_Sim/configurations/configuration.json",
                  "w") as outfile:
            outfile.write(json.dumps(json_dict, indent=2))

    def auto_wire_window(self, target_window):
        target_app = target_window(self.app)
        self.dialogs.append(target_app)
        target_app.show()
        self.hide()
        target_app.show()
        target_app.closeEvent = self.app_close_event

    def app_close_event(self, close_event):
        self.show()
Пример #14
0
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")