SKRL - Reinforcement Learning library

skrl is an open-source modular library for Reinforcement Learning written in Python (using PyTorch) and designed with a focus on readability, simplicity, and transparency of algorithm implementation. In addition to supporting the OpenAI Gym / Farama Gymnasium and DeepMind and other environment interfaces, it allows loading and configuring NVIDIA Isaac Gym, NVIDIA Isaac Orbit and NVIDIA Omniverse Isaac Gym 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

Please, visit the documentation for usage details and examples

https://skrl.readthedocs.io/en/latest/

Note: This project is under active continuous development. Please make sure you always have the latest version. Visit the develop branch or its documentation to access the latest updates to be released.

Citing this library

To cite this library in publications, please use the following reference:

@article{serrano2022skrl,
  title={skrl: Modular and Flexible Library for Reinforcement Learning},
  author={Serrano-Mu{\~n}oz, Antonio and Arana-Arexolaleiba, Nestor and Chrysostomou, Dimitrios and B{\o}gh, Simon},
  journal={arXiv preprint arXiv:2202.03825},
  year={2022}
}