skrl 1.4.3
Modular and flexible library for reinforcement learning on PyTorch and JAX
License: MIT License
Requires Python: >=3.8
Keywords: reinforcement-learning, machine-learning, reinforcement, machine, learning, rl

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<h2 align="center" style="border-bottom: 0 !important;">SKRL - Reinforcement Learning library</h2>
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**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 [PettingZoo](https://pettingzoo.farama.org), Google [DeepMind](https://github.com/deepmind/dm_env) and [Brax](https://github.com/google/brax), among other environment interfaces, it allows loading and configuring NVIDIA [Isaac Lab](https://isaac-sim.github.io/IsaacLab/index.html) (as well as [Isaac Gym](https://developer.nvidia.com/isaac-gym/) and [Omniverse Isaac Gym](https://github.com/isaac-sim/OmniIsaacGymEnvs)) 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.

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### Please, visit the documentation for usage details and examples

<strong>https://skrl.readthedocs.io</strong>

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> **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.

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### 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}
}
```