{"id":19855131,"url":"https://github.com/leggedrobotics/rsl_rl","last_synced_at":"2026-02-28T15:07:06.906Z","repository":{"id":40626429,"uuid":"418503284","full_name":"leggedrobotics/rsl_rl","owner":"leggedrobotics","description":"A fast and simple implementation of learning algorithms for robotics.","archived":false,"fork":false,"pushed_at":"2026-02-17T13:33:14.000Z","size":238,"stargazers_count":2234,"open_issues_count":13,"forks_count":519,"subscribers_count":37,"default_branch":"main","last_synced_at":"2026-02-17T18:27:01.036Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://pypi.org/project/rsl-rl-lib/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/leggedrobotics.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2021-10-18T13:00:35.000Z","updated_at":"2026-02-17T16:33:06.000Z","dependencies_parsed_at":"2024-03-24T20:24:43.480Z","dependency_job_id":"a1360b6e-96e2-49f3-8123-338aff1922c3","html_url":"https://github.com/leggedrobotics/rsl_rl","commit_stats":{"total_commits":16,"total_committers":6,"mean_commits":"2.6666666666666665","dds":0.5,"last_synced_commit":"73fd7c621bf63104a8a7eb0c168df16c0ee65908"},"previous_names":[],"tags_count":25,"template":false,"template_full_name":null,"purl":"pkg:github/leggedrobotics/rsl_rl","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leggedrobotics%2Frsl_rl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leggedrobotics%2Frsl_rl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leggedrobotics%2Frsl_rl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leggedrobotics%2Frsl_rl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/leggedrobotics","download_url":"https://codeload.github.com/leggedrobotics/rsl_rl/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leggedrobotics%2Frsl_rl/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29939114,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-28T13:49:17.081Z","status":"ssl_error","status_checked_at":"2026-02-28T13:48:50.396Z","response_time":90,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-12T14:11:47.986Z","updated_at":"2026-02-28T15:07:06.878Z","avatar_url":"https://github.com/leggedrobotics.png","language":"Python","funding_links":[],"categories":["🛠 Tools \u0026 Libraries","Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL)","Python"],"sub_categories":["RL Frameworks","RL/DRL Algorithm Implementations and Software Frameworks"],"readme":"# RSL RL\n\nFast and simple implementation of RL algorithms, designed to run fully on GPU.\nThis code is an evolution of `rl-pytorch` provided with NVIDIA's Isaac GYM.\n\nEnvironment repositories using the framework:\n\n* **`Isaac Lab`** (built on top of NVIDIA Isaac Sim): https://github.com/isaac-sim/IsaacLab\n* **`Legged-Gym`** (built on top of NVIDIA Isaac Gym): https://leggedrobotics.github.io/legged_gym/\n\nThe main branch supports **PPO** and **Student-Teacher Distillation** with additional features from our research. These include:\n\n* [Random Network Distillation (RND)](https://proceedings.mlr.press/v229/schwarke23a.html) - Encourages exploration by adding\n  a curiosity driven intrinsic reward.\n* [Symmetry-based Augmentation](https://arxiv.org/abs/2403.04359) - Makes the learned behaviors more symmetrical.\n\nWe welcome contributions from the community. Please check our contribution guidelines for more\ninformation.\n\n**Maintainer**: Mayank Mittal and Clemens Schwarke \u003cbr/\u003e\n**Affiliation**: Robotic Systems Lab, ETH Zurich \u0026 NVIDIA \u003cbr/\u003e\n**Contact**: cschwarke@ethz.ch\n\n\u003e **Note:** The `algorithms` branch supports additional algorithms (SAC, DDPG, DSAC, and more). However, it isn't currently actively maintained.\n\n\n## Setup\n\nThe package can be installed via PyPI with:\n\n```bash\npip install rsl-rl-lib\n```\n\nor by cloning this repository and installing it with:\n\n```bash\ngit clone https://github.com/leggedrobotics/rsl_rl\ncd rsl_rl\npip install -e .\n```\n\nThe package supports the following logging frameworks which can be configured through `logger`:\n\n* Tensorboard: https://www.tensorflow.org/tensorboard/\n* Weights \u0026 Biases: https://wandb.ai/site\n* Neptune: https://docs.neptune.ai/\n\nFor a demo configuration of PPO, please check the [dummy_config.yaml](config/dummy_config.yaml) file.\n\n\n## Contribution Guidelines\n\nFor 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.\n\nWe use the following tools for maintaining code quality:\n\n- [pre-commit](https://pre-commit.com/): Runs a list of formatters and linters over the codebase.\n- [black](https://black.readthedocs.io/en/stable/): The uncompromising code formatter.\n- [flake8](https://flake8.pycqa.org/en/latest/): A wrapper around PyFlakes, pycodestyle, and McCabe complexity checker.\n\nPlease 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:\n\n```bash\n# for installation (only once)\npre-commit install\n# for running\npre-commit run --all-files\n```\n\n## Citing\n\n**We are working on writing a white paper for this library.** Until then, please cite the following work\nif you use this library for your research:\n\n```text\n@InProceedings{rudin2022learning,\n  title = \t {Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning},\n  author =       {Rudin, Nikita and Hoeller, David and Reist, Philipp and Hutter, Marco},\n  booktitle = \t {Proceedings of the 5th Conference on Robot Learning},\n  pages = \t {91--100},\n  year = \t {2022},\n  volume = \t {164},\n  series = \t {Proceedings of Machine Learning Research},\n  publisher =    {PMLR},\n  url = \t {https://proceedings.mlr.press/v164/rudin22a.html},\n}\n```\n\nIf you use the library with curiosity-driven exploration (random network distillation), please cite:\n\n```text\n@InProceedings{schwarke2023curiosity,\n  title = \t {Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks},\n  author =       {Schwarke, Clemens and Klemm, Victor and Boon, Matthijs van der and Bjelonic, Marko and Hutter, Marco},\n  booktitle = \t {Proceedings of The 7th Conference on Robot Learning},\n  pages = \t {2594--2610},\n  year = \t {2023},\n  volume = \t {229},\n  series = \t {Proceedings of Machine Learning Research},\n  publisher =    {PMLR},\n  url = \t {https://proceedings.mlr.press/v229/schwarke23a.html},\n}\n```\n\nIf you use the library with symmetry augmentation, please cite:\n\n```text\n@InProceedings{mittal2024symmetry,\n  author={Mittal, Mayank and Rudin, Nikita and Klemm, Victor and Allshire, Arthur and Hutter, Marco},\n  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},\n  title={Symmetry Considerations for Learning Task Symmetric Robot Policies},\n  year={2024},\n  pages={7433-7439},\n  doi={10.1109/ICRA57147.2024.10611493}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleggedrobotics%2Frsl_rl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fleggedrobotics%2Frsl_rl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleggedrobotics%2Frsl_rl/lists"}