Racetrack#
A continuous control environment, where the he agent has to follow the tracks while avoiding collisions with other vehicles.
Credits and many thanks to @supperted825 for the idea and initial implementation.
Usage#
env = gym.make("racetrack-v0")
Default configuration#
{
"observation": {
"type": "OccupancyGrid",
"features": ['presence', 'on_road'],
"grid_size": [[-18, 18], [-18, 18]],
"grid_step": [3, 3],
"as_image": False,
"align_to_vehicle_axes": True
},
"action": {
"type": "ContinuousAction",
"longitudinal": False,
"lateral": True
},
"simulation_frequency": 15,
"policy_frequency": 5,
"duration": 300,
"collision_reward": -1,
"lane_centering_cost": 4,
"action_reward": -0.3,
"controlled_vehicles": 1,
"other_vehicles": 1,
"screen_width": 600,
"screen_height": 600,
"centering_position": [0.5, 0.5],
"scaling": 7,
"show_trajectories": False,
"render_agent": True,
"offscreen_rendering": False
}
More specifically, it is defined in:
API#
- class highway_env.envs.racetrack_env.RacetrackEnv(config: dict | None = None, render_mode: str | None = None)[source]#
A continuous control environment.
The agent needs to learn two skills: - follow the tracks - avoid collisions with other vehicles
Credits and many thanks to @supperted825 for the idea and initial implementation. See https://github.com/eleurent/highway-env/issues/231