rsl-rl-lib 4.0.1
Fast and simple RL algorithms implemented in PyTorch
Requires Python: >=3.9
Keywords: reinforcement-learning, robotics

# RSL-RL

A fast and simple implementation of learning algorithms for robotics. For an overview of the library please have a look at https://arxiv.org/pdf/2509.10771.

Environment repositories using the framework:

* **`Isaac Lab`** (built on top of NVIDIA Isaac Sim): https://github.com/isaac-sim/IsaacLab
* **`Legged Gym`** (built on top of NVIDIA Isaac Gym): https://leggedrobotics.github.io/legged_gym/
* **`MuJoCo Playground`** (built on top of MuJoCo MJX and Warp): https://github.com/google-deepmind/mujoco_playground/
* **`mjlab`** (built on top of MuJoCo Warp): https://github.com/mujocolab/mjlab

The library currently supports **PPO** and **Student-Teacher Distillation** with additional features from our research. These include:

* [Random Network Distillation (RND)](https://proceedings.mlr.press/v229/schwarke23a.html) - Encourages exploration by adding
  a curiosity driven intrinsic reward.
* [Symmetry-based Augmentation](https://arxiv.org/abs/2403.04359) - Makes the learned behaviors more symmetrical.

We welcome contributions from the community. Please check our contribution guidelines for more
information.

**Maintainer**: Mayank Mittal and Clemens Schwarke <br/>
**Affiliation**: Robotic Systems Lab, ETH Zurich & NVIDIA <br/>
**Contact**: cschwarke@ethz.ch


## Setup

The package can be installed via PyPI with:

```bash
pip install rsl-rl-lib
```

or by cloning this repository and installing it with:

```bash
git clone https://github.com/leggedrobotics/rsl_rl
cd rsl_rl
pip install -e .
```

The package supports the following logging frameworks which can be configured through `logger`:

* Tensorboard: https://www.tensorflow.org/tensorboard/
* Weights & Biases: https://wandb.ai/site
* Neptune: https://docs.neptune.ai/

For a demo configuration of PPO, please check the [example_config.yaml](config/example_config.yaml) file.


## Contribution Guidelines

For documentation, we adopt the [Google Style Guide](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) for docstrings. Please make sure that your code is well-documented and follows the guidelines.

We use the following tools for maintaining code quality:

- [pre-commit](https://pre-commit.com/): Runs a list of formatters and linters over the codebase.
- [ruff](https://github.com/astral-sh/ruff): An extremely fast Python linter and code formatter, written in Rust.

Please check [here](https://pre-commit.com/#install) for instructions to set these up. To run over the entire repository, please execute the following command in the terminal:

```bash
# for installation (only once)
pre-commit install
# for running
pre-commit run --all-files
```

## Citing

If you use this library for your research, please cite the following work:

```text
@article{schwarke2025rslrl,
  title={RSL-RL: A Learning Library for Robotics Research},
  author={Schwarke, Clemens and Mittal, Mayank and Rudin, Nikita and Hoeller, David and Hutter, Marco},
  journal={arXiv preprint arXiv:2509.10771},
  year={2025}
}
```

If you use the library with curiosity-driven exploration (random network distillation), please cite:

```text
@InProceedings{schwarke2023curiosity,
  title = 	 {Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks},
  author =       {Schwarke, Clemens and Klemm, Victor and Boon, Matthijs van der and Bjelonic, Marko and Hutter, Marco},
  booktitle = 	 {Proceedings of The 7th Conference on Robot Learning},
  pages = 	 {2594--2610},
  year = 	 {2023},
  volume = 	 {229},
  series = 	 {Proceedings of Machine Learning Research},
  publisher =    {PMLR},
  url = 	 {https://proceedings.mlr.press/v229/schwarke23a.html},
}
```

If you use the library with symmetry augmentation, please cite:

```text
@InProceedings{mittal2024symmetry,
  author={Mittal, Mayank and Rudin, Nikita and Klemm, Victor and Allshire, Arthur and Hutter, Marco},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  title={Symmetry Considerations for Learning Task Symmetric Robot Policies},
  year={2024},
  pages={7433-7439},
  doi={10.1109/ICRA57147.2024.10611493}
}
```