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.
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](https://mujoco.org) and [GitHub repository](https://github.com/deepmind/mujoco).
MuJoCo `v3` environments and older, which relied on the `mujoco-py` framework, were migrated to the `gymnasium-robotics` package starting with `gymnasium` v1.2. For information on using these older versions, please refer to the `gymnasium-robotics` documentation. The original `mujoco-py` framework can be found in its [GitHub repository](https://github.com/openai/mujoco-py/tree/master/mujoco_py).
The dependencies for the old environment versions can be installed via:
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](https://mujoco.readthedocs.io/en/stable/computation/index.html#physics-state)).
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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:
The training performance of `v2`/`v3` and `v4` are not directly comparable because of the change to the newer simulator, but the results for not Ant and not Humanoids are comparable (for more information see [GitHub Comment #1](https://github.com/openai/gym/pull/2595#issuecomment-1099152505) and [GitHub Comment #2](https://github.com/openai/gym/pull/2762#issuecomment-1135362092)).
The Training performance of `v4` and `v5` is different because of the many changes in the environments, but the Half Cheetah and Swimmer exhibits identical behaviour, Pusher and Swimmer are close (for more information see [GitHub Issue](https://github.com/Farama-Foundation/Gymnasium/issues/821)).
Note: The exact behavior of the MuJoCo simulator changes slightly between `mujoco` versions due to floating point operation ordering (more information of their [Documentation]( https://mujoco.readthedocs.io/en/stable/computation/index.html#reproducibility)), if exact reproducibility is need besides using the `seed` for experiments the same simulator version should be used.
All of the MuJoCo Environments besides the general Gymnasium arguments, and environment specific arguments they also take the following arguments for configuring the renderer:
| `render_mode` | **str** | `None` | The modality of the render result. Must be one of `human`, `rgb_array`, `depth_array`, or `rgbd_tuple`. Note that `human` does not return a rendered image, but renders directly to the window |
| `max_geom` | **int** | `1000` | Max number of geometrical objects to render (useful for 3rd-party environments) |
| `visual_options` | **Dict[int, bool]** | `{}` | A dictionary with [mjVisual](https://mujoco.readthedocs.io/en/stable/overview.html#mjvisual) flags and value pairs, example `{mujoco.mjtVisFlag.mjVIS_CONTACTPOINT: True, mujoco.mjtVisFlag.mjVIS_CONTACTFORCE: True}` (show contact points and forces). |
The MuJoCo simulator renders images with OpenGL and can use 3 different back ends "glfw" (default), "egl", "omesa", which can be selected by setting an [environment variable](https://en.wikipedia.org/wiki/Environment_variable).
