Volltext-Downloads (blau) und Frontdoor-Views (grau)

Preprint: RL-X: A Deep Reinforcement Learning Library (not only) for RoboCup

  • This paper presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5x speedups compared to well-known frameworks likeThis paper presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5x speedups compared to well-known frameworks like Stable-Baselines3.show moreshow less

Export metadata

Additional Services

Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Document Type:Article (unreviewed)
Zitierlink: https://opus.hs-offenburg.de/8439
Bibliografische Angaben
Title (English):Preprint: RL-X: A Deep Reinforcement Learning Library (not only) for RoboCup
Author:Nico BohlingerStaff MemberGND, Klaus DorerStaff MemberORCiDGND
Year of Publication:2023
Date of first Publication:2023/10/20
First Page:1
Last Page:12
DOI:https://doi.org/10.48550/arXiv.2310.13396
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Collections of the Offenburg University:Bibliografie
Tag:Deep Reinforcement Learning
Formale Angaben
Relevance for "Jahresbericht über Forschungsleistungen":Keine Relevanz
Open Access: Open Access 
 Bronze 
Licence (German):License LogoUrheberrechtlich geschützt
ArXiv Id:http://arxiv.org/abs/2310.13396