text stringlengths 1 93.6k |
|---|
next_state, reward, dones, info = env.step(action)
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state = next_state
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if env.step_count >= 150 and env.current_waypoint_index == 0:
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dones = True
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# Save route at the beginning of the episode
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if not saved_route:
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initial_heading = np.deg2rad(env.vehicle.get_transform().rotation.yaw)
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initial_vehicle_location = vector(env.vehicle.get_location())
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# Save the route to plot them later
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for way in env.route_waypoints:
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route_relative = get_displacement_vector(initial_vehicle_location,
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vector(way[0].transform.location),
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initial_heading)
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new_row = pd.DataFrame([['route', env.episode_idx, route_relative[0], route_relative[1]]],
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columns=["model_id", "episode", "route_x", "route_y"])
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df = pd.concat([df, new_row], ignore_index=True)
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saved_route = True
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vehicle_relative = get_displacement_vector(initial_vehicle_location, vector(env.vehicle.get_location()),
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initial_heading)
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waypoint_relative = get_displacement_vector(initial_vehicle_location,
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vector(env.current_waypoint.transform.location), initial_heading)
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if env.collision_state:
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collision_speed, collision_interval, cps, cpm = env.collision_speed, env.collision_interval, env.cps, env.cpm
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else:
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collision_speed, collision_interval, cps, cpm = 0, None, 0, 0
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new_row = pd.DataFrame(
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[[model_id, env.episode_idx, env.step_count, env.vehicle.control.throttle, env.vehicle.control.steer,
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vehicle_relative[0], vehicle_relative[1], reward,
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env.distance_traveled,
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env.vehicle.get_speed(), env.distance_from_center,
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np.rad2deg(env.vehicle.get_angle(env.current_waypoint)),
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waypoint_relative[0], waypoint_relative[1], None, None,
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env.routes_completed, collision_speed, collision_interval, cps, cpm
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]], columns=columns)
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df = pd.concat([df, new_row], ignore_index=True)
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if record_video:
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# Add frame
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rendered_frame = env.render(mode="rgb_array")
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video_recorder.add_frame(rendered_frame)
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if dones:
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state = env.reset()
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episode_idx += 1
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saved_route = False
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print("Episode ", episode_idx)
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# Release video
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if record_video:
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video_recorder.release()
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df.to_csv(csv_path, index=False)
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plot_eval([csv_path])
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summary_eval(csv_path)
|
if __name__ == "__main__":
|
model_ckpt = args["model"]
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algorithm_dict = {
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"PPO": PPO,
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"DDPG": DDPG,
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"SAC": SAC,
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"CLIP-SAC": CLIPRewardedSAC,
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"CLIP-PPO": CLIPRewardedPPO,
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}
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if CONFIG.algorithm not in algorithm_dict:
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raise ValueError("Invalid algorithm name")
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AlgorithmRL = algorithm_dict[CONFIG.algorithm]
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observation_space, encode_state_fn = create_encode_state_fn(CONFIG.state, CONFIG)
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action_space_type = 'continuous' if CONFIG.action_space_type != 'discrete' else 'discrete'
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eval_suffix = ''
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if args['density'] == 'empty':
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activate_traffic_flow = False
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tf_num = 0
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eval_suffix += 'empty'
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else:
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activate_traffic_flow = True
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if args['density'] == 'regular':
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tf_num = 20
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else:
|
tf_num = 40
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eval_suffix += 'dense'
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if args['town'] != 'Town02':
|
eval_suffix += args['town']
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env = CarlaRouteEnv(obs_res=CONFIG.obs_res, host=args["host"], port=args["port"],
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reward_fn=reward_functions[CONFIG.reward_fn], observation_space=observation_space,
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encode_state_fn=encode_state_fn, fps=args["fps"], action_smoothing=CONFIG.action_smoothing,
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eval=True, action_space_type=action_space_type, activate_spectator=True, activate_render=True,
|
activate_bev=True, activate_seg_bev=CONFIG.use_seg_bev, start_carla=True,
|
activate_traffic_flow=activate_traffic_flow, tf_num=tf_num, town=args["town"])
|
for wrapper_class_str in CONFIG.wrappers:
|
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