SKRL: a modular reinforcement learning library with support for NVIDIA Omniverse Isaac Gym

Dear community,

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 and DeepMind environment interfaces, it allows loading and configuring NVIDIA Isaac Gym 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

The current version 0.6.0 is now available (it is under active development. Bug detection and/or correction, feature requests and everything else are more than welcome: Open a new issue on GitHub! ). Please refresh your browser (Ctrl + Shift + R or Ctrl + F5) if the documentation is not displayed correctly

This new version has focused on supporting the training and evaluation of reinforcement learning algorithms in NVIDIA Omniverse Isaac Gym


  • Omniverse Isaac Gym environment loader
  • Wrap an Omniverse Isaac Gym environment
  • Save the best models during training
  • Omniverse Isaac Gym examples