Car Racing


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)




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/

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.


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.


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

  • 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


  • Chris Campbell (2014),


Created by Oleg Klimov