MuJoCo#

MuJoCo stands for Multi-Joint dynamics with Contact. It is a physics engine for facilitating research and development in robotics, biomechanics, graphics and animation, and other areas where fast and accurate simulation is needed. There is physical contact between the robots and their environment - and MuJoCo attempts at getting realistic physics simulations for the possible physical contact dynamics by aiming for physical accuracy and computational efficiency.

The unique dependencies including the MuJoCo simlator for this set of environments can be installed via:

pip install gymnasium[mujoco]

As of October 2021, DeepMind has acquired MuJoCo and has open-sourced it in 2022, making it free for everyone. Using MuJoCo with Gymnasium requires the framework mujoco be installed (this dependency is installed with the above command). Instructions for installing the MuJoCo engine can be found on their website and GitHub repository.

For MuJoCo v3 environments and older the mujoco-py framework is required (pip install gymnasium[mujoco-py]) which can be found in the GitHub repository.

There are eleven MuJoCo environments (in roughly increasing complexity):

Robot

Short Description

CartPoles

InvertedPendulum

MuJuCo version of the CartPole Environment (with Continuous actions)

InvertedDoublePendulum

2 Pole variation of the CartPole Environment

Arms

Reacher

2d arm with the goal of reaching an object

Pusher

3d arm with the goal of pushing an object to a target location

2D Runners

HalfCheetah

2d quadruped with the goal of running

Hopper

2d monoped with the goal of goal of hopping

Walker2d

2d bidped with the goal of walking

Swimmers

Swimmer

3d robot with the goal of swimming

Quarduped

Ant

3d quadurped with the goal of running

Humanoid Bipeds

Humanoid

3d humanoid with the goal of running

HumanoidStandup

3d humanoid with the goal of standing up

All of these environments are stochastic in terms of their initial state, with a Gaussian noise added to a fixed initial state in order to add stochasticity. The state spaces for MuJoCo environments in Gymnasium consist of two parts that are flattened and concatenated together: the position of the body part and joints (mujoco.MjData.qpos) and their corresponding velocity (mujoco.MjData.qvel) (more information in the MuJoCo Physics State Documentation).

Among the Gymnasium environments, this set of environments can be considered as more difficult to solve by policy.

Environments can be configured by changing the xml_file argument and/or by tweaking the parameters of their classes.

Versions#

Gymnasium includes the following versions of the environments:

Version

Simulator

Notes

v5

mujoco=>2.3.3

Recommended (most features, the least bugs)

v4

mujoco=>2.1.3

Maintained for reproducibility

v3

mujoco-py

Deprecated, Kept for reproducibility (limited support)

v2

mujoco-py

Deprecated, Kept for reproducibility (limited support)

For more information, see the section “Version History” for each environment.

v1 and older are no longer included in Gymnasium.

Comparing training performance across versions#

The training performance of v2 and v3 is identical assuming the same/default arguments were used.

The training performance of v2/v3 and v4 is not directly comparable because of the change to the newer simulator, but the results for not Ant and not Humanoids are comperable (for more information see GitHub Comment #1 and GitHub Comment #2).

The Training performance of v4 and v5 is different because of the many changes in the environments, but the Half Cheetah and Swimmer exchibits identical behaviour, Pusher and Swimmer are close (for more information see GitHub Issue).

Exact reproducibility#

Note: The exact behavior of the MuJoCo simulator changes slightly between mujoco versions due to floating point operation ordering (more information of their Documentation), if exact reproducibility is need besides using the seed for expirements the same simulator version should be used.

Rendering Arguments#

The all MuJoCo Environments besides the general Gymnasium arguments, and environment specific arguments they also take the following arguments for configuring the renderer:

env = gymnasium.make("Ant-v5", render_mode="rgb_array", width=1280, height=720)

Parameter

Type

Default

Description

width

int

480

The width of the render window

height

int

480

The height of the render window

camera_id

int | None

None

The camera ID used for the render window

camera_name

str | None

None

The name of the camera used for the render window (mutally exclusive option with camera_id)

default_camera_config

dict[str, float | int] | None

None

The mjvCamera properties

max_geom

int

1000

Max number of geometrical objects to render (useful for 3rd-party environments)