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import logging, warnings |
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import numpy as np |
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from ROAR_Sim.configurations.configuration import Configuration as CarlaConfig |
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from ROAR.configurations.configuration import Configuration as AgentConfig |
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from pathlib import Path |
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from ROAR.agent_module.pure_pursuit_agent \ |
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import PurePursuitAgent |
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from ROAR_Sim.carla_client.carla_runner import CarlaRunner |
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from typing import Tuple |
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from prettytable import PrettyTable |
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from ROAR.agent_module.michael_pid_agent import PIDAgent |
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def compute_score(carla_runner: CarlaRunner) -> Tuple[float, int, int]: |
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""" |
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Calculates the score of the vehicle upon completion of the track based on certain metrics |
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Args: |
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carla_runner (): |
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Returns: |
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time_elapsed: |
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num_collision: number of collisions during simulation |
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laps_completed: Number of laps completed |
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""" |
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time_elapsed: float = carla_runner.end_simulation_time - carla_runner.start_simulation_time |
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num_collision: int = carla_runner.agent_collision_counter |
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laps_completed = 0 if carla_runner.completed_lap_count < 0 else carla_runner.completed_lap_count |
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return time_elapsed, num_collision, laps_completed |
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def run(agent_class, agent_config_file_path: Path, carla_config_file_path: Path, |
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num_laps: int = 10) -> Tuple[float, int, int]: |
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""" |
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Run the agent along the track and produce a score based on certain metrics |
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Args: |
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num_laps: int number of laps that the agent should run |
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agent_class: the participant's agent |
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agent_config_file_path: agent configuration path |
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carla_config_file_path: carla configuration path |
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Returns: |
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float between 0 - 1 representing scores |
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""" |
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agent_config: AgentConfig = AgentConfig.parse_file(agent_config_file_path) |
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carla_config = CarlaConfig.parse_file(carla_config_file_path) |
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agent_config.num_laps = num_laps |
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carla_runner = CarlaRunner(carla_settings=carla_config, |
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agent_settings=agent_config, |
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npc_agent_class=PurePursuitAgent, |
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competition_mode=True, |
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lap_count=num_laps) |
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try: |
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my_vehicle = carla_runner.set_carla_world() |
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agent = agent_class(vehicle=my_vehicle, agent_settings=agent_config) |
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carla_runner.start_game_loop(agent=agent, use_manual_control=False) |
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return compute_score(carla_runner) |
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except Exception as e: |
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print(f"something bad happened during initialization: {e}") |
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carla_runner.on_finish() |
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logging.error(f"{e}. Might be a good idea to restart Server") |
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return 0, 0, False |
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def suppress_warnings(): |
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logging.basicConfig(format='%(levelname)s - %(asctime)s - %(name)s ' |
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'- %(message)s', |
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level=logging.INFO) |
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logging.getLogger("matplotlib").setLevel(logging.WARNING) |
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warnings.simplefilter("ignore") |
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np.set_printoptions(suppress=True) |
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def main(): |
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suppress_warnings() |
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agent_class = PIDAgent |
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num_trials = 10 |
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total_score_array = [] |
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num_laps = 1 |
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table = PrettyTable() |
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table.field_names = ["time_elapsed (sec)", "num_collisions", "laps completed"] |
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for i in range(num_trials): |
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print(f"runner_competition_evaluator -> Trial: {i}") |
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scores = run(agent_class=agent_class, |
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agent_config_file_path=Path("./ROAR/configurations/carla/carla_agent_configuration.json"), |
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carla_config_file_path=Path("./ROAR_Sim/configurations/configuration.json"), |
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num_laps=num_laps) |
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total_score_array.append(scores[0]) |
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table.add_row(scores) |
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table.add_row((f"AVG for {num_trials} trials = {np.average(total_score_array)}", "N/A", "N/A")) |
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print(table) |
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if __name__ == "__main__": |
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main() |
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