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.…
Document Type: | Article (unreviewed) |
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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 BohlingerGND, 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): | Urheberrechtlich geschützt |
ArXiv Id: | http://arxiv.org/abs/2310.13396 |