skrl: Modular and Flexible Library for Reinforcement Learning

Antonio Serrano-Muñoz; Dimitrios Chrysostomou; Simon Bøgh; Nestor Arana-Arexolaleiba

skrl: Modular and Flexible Library for Reinforcement Learning

Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

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Abstract

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 Omniverse Isaac 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 be found at https://skrl.readthedocs.io and its source code is available on GitHub at https://github.com/Toni-SM/skrl.

Originalsprog	Engelsk
Tidsskrift	Journal of Machine Learning Research
Vol/bind	24
Udgave nummer	254
Sider (fra-til)	1-9
Antal sider	9
ISSN	1533-7928
Status	Udgivet - 15 aug. 2023

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JMLR 2023 - skrl_ModularandFlexibleLibraryforReinforcement LearningForlagets udgivne version, 316 KBLicens: CC BY 4.0

https://jmlr.org/papers/v24/23-0112.htmlLicens: CC BY 4.0

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Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

R2P2: Networking for Research and Development of Human Interactive and Sensitive Robotics taking advantage of Additive Manufacturing
Chrysostomou, D. (PI (principal investigator)), LI, C. (Projektdeltager), Arexolaleiba, N. A. (Projektdeltager) & Madsen, O. (Projektdeltager)
01/01/2020 → 31/12/2022
Projekter: Projekt › Forskning

Citationsformater

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title = "skrl: Modular and Flexible Library for Reinforcement Learning",

abstract = "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 Omniverse Isaac 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 be found at https://skrl.readthedocs.io and its source code is available on GitHub at https://github.com/Toni-SM/skrl.",

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AU - Serrano-Muñoz, Antonio

AU - Chrysostomou, Dimitrios

AU - Bøgh, Simon

AU - Arana-Arexolaleiba, Nestor

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AB - 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 Omniverse Isaac 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 be found at https://skrl.readthedocs.io and its source code is available on GitHub at https://github.com/Toni-SM/skrl.

KW - Reinforcement Learning (RL)

KW - skrl

KW - robot learning

M3 - Journal article

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skrl: Modular and Flexible Library for Reinforcement Learning

Abstract

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R2P2: Networking for Research and Development of Human Interactive and Sensitive Robotics taking advantage of Additive Manufacturing

Citationsformater