（17-7-02）基于强化学习的自动驾驶系统：收集CARLA环境中的专家驾驶数据

 if __name__=='__main__':

    
     argparser = ArgumentParser()
    
     argparser.add_argument('--world-port', type=int, default=config.WORLD_PORT)
    
     argparser.add_argument('--host', type=str, default=config.WORLD_HOST)
    
     argparser.add_argument('--weather', type=str, default='ClearNoon',
    
                        choices=['ClearNoon', 'ClearSunset', 'CloudyNoon', 'CloudySunset',
    
                                 'WetNoon', 'WetSunset', 'MidRainyNoon', 'MidRainSunset',
    
                                 'WetCloudyNoon', 'WetCloudySunset', 'HardRainNoon',
    
                                 'HardRainSunset', 'SoftRainNoon', 'SoftRainSunset'],
    
                                 help='Weather preset')
    
     argparser.add_argument('--cam_height', type=int, default=config.CAM_HEIGHT, help="Camera height")
    
     argparser.add_argument('--cam_width', type=int, default=config.CAM_WIDTH, help="Camera width")
    
     argparser.add_argument('--fov', type=int, default=config.CAM_FOV, help="Camera field of view")
    
     argparser.add_argument('--nb_episodes', type=int, default=90, help="Number of episodes to run")
    
     argparser.add_argument('--tick', type=float, default=config.TICK, help="Sensor tick length")
    
  
    
     argparser.add_argument('--out_folder', type=str, default=config.EXPERT_DATA_FOLDER, help="Output folder")
    
     argparser.add_argument('--model', type=str, default=config.AE_MODEL, help='model',
    
                        choices=['Autoencoder', 'AutoencoderSEM', 'VAE'])
    
     argparser.add_argument('--autoencoder_model', type=str, help="Autoencoder model path", default=config.AE_PRETRAINED)
    
     argparser.add_argument('--from_ep', type=int, default=0, help="Start episode number (for resuming)")
    
     argparser.add_argument('-no_exo_vehicles', help="Use exo vehicles", action='store_false')
    
  
    
     args = argparser.parse_args()
    
  
    
     if args.model=='AutoencoderSEM':
    
     autoencoder = AutoencoderSEM.load_from_checkpoint(args.autoencoder_model)
    
     elif args.model=='VAE':
    
     autoencoder = VAE.load_from_checkpoint(args.autoencoder_model)
    
     elif args.model=='Autoencoder':
    
     autoencoder = Autoencoder.load_from_checkpoint(args.autoencoder_model)
    
     else:
    
     raise ValueError(f"Unknown model {args.model}")
    
  
    
     autoencoder.freeze()
    
     autoencoder.eval()
    
  
    
     out_folder = os.path.join(args.out_folder, args.weather)
    
  
    
     os.makedirs(out_folder, exist_ok=True)
    
  
    
     env = CarlaEnv(autoencoder, args.world_port, args.host, config.TRAIN_MAP, args.weather,
    
              args.cam_height, args.cam_width, args.fov, args.tick, 1000, exo_vehicles=args.no_exo_vehicles)
    
     
    
     spawn_points = env.map.get_spawn_points()
    
     possible_routes = Route.get_possibilities(config.TRAIN_MAP)
    
     routes = [[possible_routes[0]().end.location],
    
         [possible_routes[1]().end.location],
    
         [possible_routes[2]().end.location]]
    
     
    
     for i in range(len(routes)):
    
     os.makedirs(os.path.join(out_folder, f'Route_{i}'), exist_ok=True)
    
  
    
     try:    
    
  
    
     for episode in range(args.from_ep, args.nb_episodes):
    
         print("Episode {}".format(episode))
    
  
    
         route_id = episode % len(routes)
    
  
    
         route = routes[route_id]
    
  
    
         obs = env.reset(route_id)
    
  
    
         env.traffic_manager.ignore_lights_percentage(env.ego_vehicle, 100)
    
         env.traffic_manager.random_left_lanechange_percentage(env.ego_vehicle, 0)
    
         env.traffic_manager.random_right_lanechange_percentage(env.ego_vehicle, 0)
    
         env.traffic_manager.auto_lane_change(env.ego_vehicle, False)
    
         env.traffic_manager.set_desired_speed(env.ego_vehicle, 35.)
    
         env.traffic_manager.distance_to_leading_vehicle(env.ego_vehicle, 4.0)
    
  
    
         env.traffic_manager.set_path(env.ego_vehicle, route)
    
  
    
         env.ego_vehicle.set_autopilot(True)
    
  
    
         done = False
    
         data = []
    
         while not done:
    
             control = env.ego_vehicle.get_control()
    
             action = [control.steer, 0.0]
    
             if control.throttle > control.brake:
    
                 action[1] = control.throttle
    
             else:
    
                 action[1] = -control.brake
    
             obs_t1, reward, done, info = env.step(action)
    
  
    
             if info["speed"] < 1e-6 and action[1] <= 0.0: #Avoid getting stuck when learning
    
                 action[1] = -0.05
    
  
    
             data.append([obs, action, reward, obs_t1, done, info])
    
  
    
             obs = obs_t1
    
  
    
         out_file = os.path.join(out_folder, f"Route_{route_id}", f"{episode}.pkl")
    
         with open(out_file, 'wb') as f:
    
             pickle.dump(data, f)
    
         print(f"End of episode {episode} - {len(data)} steps")
    
     finally:
    
     env.reset_settings()