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:

classmethod RacetrackEnv.default_config() → dict

Default environment configuration.

Can be overloaded in environment implementations, or by calling configure(). :return: a configuration dict

API

class highway_env.envs.racetrack_env.RacetrackEnv(config: dict = None, render_mode: str | None = None)

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

classmethod default_config() → dict

Default environment configuration.

Can be overloaded in environment implementations, or by calling configure(). :return: a configuration dict