SKRL

skrl is an open-source, modular reinforcement-learning library implemented in PyTorch, JAX and NVIDIA Warp, designed for readable, transparent algorithms, with support for Gymnasium, Gym, PettingZoo, Isaac Lab and MuJoCo Playground environments.

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Visit website skrl.readthedocs.io

skrl is an open-source, modular library for reinforcement learning implemented in PyTorch, JAX and NVIDIA Warp. It is designed around modularity, readability, simplicity and transparency of algorithm implementation rather than being the fastest possible library.

Besides OpenAI Gym, Farama Gymnasium, PettingZoo and ManiSkill, it can load and configure NVIDIA Isaac Lab and MuJoCo Playground environments, and it supports training agents simultaneously across scopes, subsets of the available environments that may or may not share resources, within the same run. The project is under active development, and its documentation is hosted separately on Read the Docs.

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