[](https://pypi.org/project/skrl)
[<img src="https://img.shields.io/badge/%F0%9F%A4%97%20models-hugging%20face-F8D521">](https://huggingface.co/skrl)
<br>
[](https://github.com/Toni-SM/skrl)
<span> </span>
[](https://skrl.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/Toni-SM/skrl/actions/workflows/python-test.yml)
[](https://github.com/Toni-SM/skrl/actions/workflows/pre-commit.yml)
<br>
<p align="center">
<a href="https://skrl.readthedocs.io">
<img width="300rem" src="https://raw.githubusercontent.com/Toni-SM/skrl/main/docs/source/_static/data/logo-light-mode.png">
</a>
</p>
<h2 align="center" style="border-bottom: 0 !important;">SKRL - Reinforcement Learning library</h2>
<br>
**skrl** is an open-source modular library for Reinforcement Learning written in Python (on top of [PyTorch](https://pytorch.org/) and [JAX](https://jax.readthedocs.io)) and designed with a focus on modularity, readability, simplicity, and transparency of algorithm implementation. In addition to supporting the OpenAI [Gym](https://www.gymlibrary.dev) / Farama [Gymnasium](https://gymnasium.farama.org) and [DeepMind](https://github.com/deepmind/dm_env) and other environment interfaces, it allows loading and configuring [NVIDIA Isaac Gym](https://developer.nvidia.com/isaac-gym/), [NVIDIA Omniverse Isaac Gym](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_gym_isaac_gym.html) and [NVIDIA Isaac Lab](https://isaac-sim.github.io/IsaacLab/index.html) environments, enabling agents' simultaneous training by scopes (subsets of environments among all available environments), which may or may not share resources, in the same run.
<br>
### Please, visit the documentation for usage details and examples
<strong>https://skrl.readthedocs.io</strong>
<br>
> **Note:** This project is under **active continuous development**. Please make sure you always have the latest version. Visit the [develop](https://github.com/Toni-SM/skrl/tree/develop) branch or its [documentation](https://skrl.readthedocs.io/en/develop) to access the latest updates to be released.
<br>
### Citing this library
To cite this library in publications, please use the following reference:
```bibtex
@article{serrano2023skrl,
author = {Antonio Serrano-Muñoz and Dimitrios Chrysostomou and Simon Bøgh and Nestor Arana-Arexolaleiba},
title = {skrl: Modular and Flexible Library for Reinforcement Learning},
journal = {Journal of Machine Learning Research},
year = {2023},
volume = {24},
number = {254},
pages = {1--9},
url = {http://jmlr.org/papers/v24/23-0112.html}
}
```
Raw data
{
"_id": null,
"home_page": null,
"name": "skrl",
"maintainer": "Toni-SM",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": null,
"keywords": "reinforcement-learning, machine-learning, reinforcement, machine, learning, rl",
"author": "Toni-SM",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/6e/c3/6d9e8d771c85621a0a355510e1a54d0a6f8ded4b27afb9a3ba1aafc06f39/skrl-1.2.0.tar.gz",
"platform": null,
"description": "[](https://pypi.org/project/skrl)\n[<img src=\"https://img.shields.io/badge/%F0%9F%A4%97%20models-hugging%20face-F8D521\">](https://huggingface.co/skrl)\n\n<br>\n[](https://github.com/Toni-SM/skrl)\n<span> </span>\n[](https://skrl.readthedocs.io/en/latest/?badge=latest)\n[](https://github.com/Toni-SM/skrl/actions/workflows/python-test.yml)\n[](https://github.com/Toni-SM/skrl/actions/workflows/pre-commit.yml)\n\n<br>\n<p align=\"center\">\n <a href=\"https://skrl.readthedocs.io\">\n <img width=\"300rem\" src=\"https://raw.githubusercontent.com/Toni-SM/skrl/main/docs/source/_static/data/logo-light-mode.png\">\n </a>\n</p>\n<h2 align=\"center\" style=\"border-bottom: 0 !important;\">SKRL - Reinforcement Learning library</h2>\n<br>\n\n**skrl** is an open-source modular library for Reinforcement Learning written in Python (on top of [PyTorch](https://pytorch.org/) and [JAX](https://jax.readthedocs.io)) and designed with a focus on modularity, readability, simplicity, and transparency of algorithm implementation. In addition to supporting the OpenAI [Gym](https://www.gymlibrary.dev) / Farama [Gymnasium](https://gymnasium.farama.org) and [DeepMind](https://github.com/deepmind/dm_env) and other environment interfaces, it allows loading and configuring [NVIDIA Isaac Gym](https://developer.nvidia.com/isaac-gym/), [NVIDIA Omniverse Isaac Gym](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_gym_isaac_gym.html) and [NVIDIA Isaac Lab](https://isaac-sim.github.io/IsaacLab/index.html) environments, enabling agents' simultaneous training by scopes (subsets of environments among all available environments), which may or may not share resources, in the same run.\n\n<br>\n\n### Please, visit the documentation for usage details and examples\n\n<strong>https://skrl.readthedocs.io</strong>\n\n<br>\n\n> **Note:** This project is under **active continuous development**. Please make sure you always have the latest version. Visit the [develop](https://github.com/Toni-SM/skrl/tree/develop) branch or its [documentation](https://skrl.readthedocs.io/en/develop) to access the latest updates to be released.\n\n<br>\n\n### Citing this library\n\nTo cite this library in publications, please use the following reference:\n\n```bibtex\n@article{serrano2023skrl,\n author = {Antonio Serrano-Mu\u00f1oz and Dimitrios Chrysostomou and Simon B\u00f8gh and Nestor Arana-Arexolaleiba},\n title = {skrl: Modular and Flexible Library for Reinforcement Learning},\n journal = {Journal of Machine Learning Research},\n year = {2023},\n volume = {24},\n number = {254},\n pages = {1--9},\n url = {http://jmlr.org/papers/v24/23-0112.html}\n}\n```\n",
"bugtrack_url": null,
"license": "MIT License",
"summary": "Modular and flexible library for reinforcement learning on PyTorch and JAX",
"version": "1.2.0",
"project_urls": {
"Bug Reports": "https://github.com/Toni-SM/skrl/issues",
"Discussions": "https://github.com/Toni-SM/skrl/discussions",
"Documentation": "https://skrl.readthedocs.io",
"Homepage": "https://github.com/Toni-SM/skrl",
"Say Thanks!": "https://github.com/Toni-SM",
"Source": "https://github.com/Toni-SM/skrl"
},
"split_keywords": [
"reinforcement-learning",
" machine-learning",
" reinforcement",
" machine",
" learning",
" rl"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "0f67313a7c45bfd1929730286f455f3ecf9d15e199687693b4cc2ec553566ba5",
"md5": "8e9198aba461f4d6ca0dde687a0b7ed3",
"sha256": "f0cfe71f63ed1c3b7680724100a8e3e8679a8cc8b88b2e3aeac0934931d7b63c"
},
"downloads": -1,
"filename": "skrl-1.2.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "8e9198aba461f4d6ca0dde687a0b7ed3",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 351086,
"upload_time": "2024-06-24T03:35:50",
"upload_time_iso_8601": "2024-06-24T03:35:50.322231Z",
"url": "https://files.pythonhosted.org/packages/0f/67/313a7c45bfd1929730286f455f3ecf9d15e199687693b4cc2ec553566ba5/skrl-1.2.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "6ec36d9e8d771c85621a0a355510e1a54d0a6f8ded4b27afb9a3ba1aafc06f39",
"md5": "49b758e63b3eec02e63afd51b483bb1e",
"sha256": "97ae42015fa382c695d759716f9f0b5b04fc9f2bed9ab7ab34e12138793b5d20"
},
"downloads": -1,
"filename": "skrl-1.2.0.tar.gz",
"has_sig": false,
"md5_digest": "49b758e63b3eec02e63afd51b483bb1e",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 170827,
"upload_time": "2024-06-24T03:35:51",
"upload_time_iso_8601": "2024-06-24T03:35:51.805233Z",
"url": "https://files.pythonhosted.org/packages/6e/c3/6d9e8d771c85621a0a355510e1a54d0a6f8ded4b27afb9a3ba1aafc06f39/skrl-1.2.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-06-24 03:35:51",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "Toni-SM",
"github_project": "skrl",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "skrl"
}
JSON
