Car Racing#

../../../_images/car_racing.gif

This environment is part of the Box2D environments which contains general information about the environment.

Action Space

Box([-1. 0. 0.], 1.0, (3,), float32)

Observation Space

Box(0, 255, (96, 96, 3), uint8)

import

gymnasium.make("CarRacing-v2")

Description#

The easiest control task to learn from pixels - a top-down racing environment. The generated track is random every episode.

Some indicators are shown at the bottom of the window along with the state RGB buffer. From left to right: true speed, four ABS sensors, steering wheel position, and gyroscope. To play yourself (it’s rather fast for humans), type:

python gymnasium/envs/box2d/car_racing.py

Remember: it’s a powerful rear-wheel drive car - don’t press the accelerator and turn at the same time.

Action Space#

If continuous there are 3 actions :

  • 0: steering, -1 is full left, +1 is full right

  • 1: gas

  • 2: breaking

If discrete there are 5 actions:

  • 0: do nothing

  • 1: steer left

  • 2: steer right

  • 3: gas

  • 4: brake

Observation Space#

A top-down 96x96 RGB image of the car and race track.

Rewards#

The reward is -0.1 every frame and +1000/N for every track tile visited, where N is the total number of tiles visited in the track. For example, if you have finished in 732 frames, your reward is 1000 - 0.1*732 = 926.8 points.

Starting State#

The car starts at rest in the center of the road.

Episode Termination#

The episode finishes when all the tiles are visited. The car can also go outside the playfield - that is, far off the track, in which case it will receive -100 reward and die.

Arguments#

>>> import gymnasium as gym
>>> env = gym.make("CarRacing-v2", render_mode="rgb_array", lap_complete_percent=0.95, domain_randomize=False, continuous=False)
>>> env
<TimeLimit<OrderEnforcing<PassiveEnvChecker<CarRacing<CarRacing-v2>>>>>

  • lap_complete_percent=0.95 dictates the percentage of tiles that must be visited by the agent before a lap is considered complete.

  • domain_randomize=False enables the domain randomized variant of the environment. In this scenario, the background and track colours are different on every reset.

  • continuous=True converts the environment to use discrete action space. The discrete action space has 5 actions: [do nothing, left, right, gas, brake].

Reset Arguments#

Passing the option options["randomize"] = True will change the current colour of the environment on demand. Correspondingly, passing the option options["randomize"] = False will not change the current colour of the environment. domain_randomize must be True on init for this argument to work.

>>> import gymnasium as gym
>>> env = gym.make("CarRacing-v2", domain_randomize=True)

# normal reset, this changes the colour scheme by default
>>> obs, _ = env.reset()

# reset with colour scheme change
>>> randomize_obs, _ = env.reset(options={"randomize": True})

# reset with no colour scheme change
>>> non_random_obs, _ = env.reset(options={"randomize": False})

Version History#

  • v1: Change track completion logic and add domain randomization (0.24.0)

  • v0: Original version

References#

  • Chris Campbell (2014), http://www.iforce2d.net/b2dtut/top-down-car.

Credits#

Created by Oleg Klimov