skrl: Modular and Flexible Library for Reinforcement Learning

  1. Antonio Serrano-Muñoz 1
  2. Dimitrios Chrysostomou 2
  3. Simon Bøgh 2
  4. Nestor Arana-Arexolaleiba 12
  1. 1 Universidad de Mondragón/Mondragon Unibertsitatea
    info

    Universidad de Mondragón/Mondragon Unibertsitatea

    Mondragón, España

    ROR https://ror.org/00wvqgd19

  2. 2 Aalborg University
    info

    Aalborg University

    Aalborg, Dinamarca

    ROR https://ror.org/04m5j1k67

Revista:
Journal of Machine Learning Research

ISSN: 1532-4435

Ano de publicación: 2022

Volume: 23

Tipo: Artigo

DOI: 10.48550/ARXIV.2202.03825 GOOGLE SCHOLAR lock_openAcceso aberto editor

Outras publicacións en: Journal of Machine Learning Research

Obxectivos de Desenvolvemento Sustentable

Resumo

skrl is an open-source modular library for reinforcement learning written in Python and designed with a focus on readability, simplicity, and transparency of algorithm implementations. In addition to supporting environments that use the traditional interfaces from OpenAI Gym / Farama Gymnasium, DeepMind and others, it provides the facility to load, configure, and operate NVIDIA Isaac Gym, Isaac Orbit, and OmniverseIsaac Gym environments. Furthermore, it enables the simultaneous training of several agents with customizable scopes (subsets of environments among all available ones), which may or may not share resources, in the same run. The library’s documentation can befound at https://skrl.readthedocs.io and its source code is available on GitHub athttps://github.com/Toni-SM/skrl

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Información de financiamento

We would like to express our gratitude for the funding and support received from NVIDIA under a collaboration agreement with the Mondragon Unibertsitatea.

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