From 678b96e9672b06230280732a0efed1dc743195e5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Antonio=20Serrano=20Mu=C3=B1oz?= Date: Fri, 21 Jun 2024 18:24:19 -0400 Subject: [PATCH] Update README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 65b7b391..728dfcf0 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@

SKRL - Reinforcement Learning library


-**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 Isaac Orbit](https://isaac-orbit.github.io/orbit/index.html) and [NVIDIA Omniverse Isaac Gym](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_gym_isaac_gym.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. +**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.