{"repo":"Toni-SM/skrl","free":true,"listed":false,"github":"https://github.com/Toni-SM/skrl","clone":"git clone https://github.com/Toni-SM/skrl.git","description":"Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with support for Gymnasium/Gym, NVIDIA Isaac Lab, MuJoCo Playground and other environments","language":"Python","stars":1089,"topics":["reinforcement-learning","python","deep-learning","gym","gymnasium","machine-learning","robotics","jax","isaaclab","brax"],"license":"MIT","category":"machine-learning","readme_excerpt":"     SKRL - Reinforcement Learning library Documentation: https://skrl.readthedocs.io Description : skrl is an open-source modular library for Reinforcement Learning written in Python (implemented in PyTorch, JAX and NVIDIA Warp) and designed with a focus on modularity, readability, simplicity, and transparency of algorithm implementation. In addition to supporting OpenAI Gym, Farama Gymnasium and PettingZoo, ManiSkill, among other environment interfaces, it allows loading and configuring NVIDIA Isaac Lab and MuJoCo Playground 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. Refer to the documentation for details and examples: https://skrl.readthedocs.io 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:","default_branch":null,"files":null,"tree":[],"storefront":"/r/Toni-SM","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Toni-SM/skrl/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}