A Survey of Domain-Specific Architectures for Reinforcement Learning
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https://doi.org/10.48693/241
https://doi.org/10.48693/241
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| DC Element | Wert | Sprache |
|---|---|---|
| dc.creator | Rothmann, Marc | - |
| dc.creator | Porrmann, Mario | - |
| dc.date.accessioned | 2023-01-31T09:20:50Z | - |
| dc.date.available | 2023-01-31T09:20:50Z | - |
| dc.date.issued | 2022-01-26 | - |
| dc.identifier.citation | M. Rothmann and M. Porrmann: A Survey of Domain-Specific Architectures for Reinforcement Learning. In: IEEE Access, vol. 10, pp. 13753-13767, 2022 | ger |
| dc.identifier.uri | https://doi.org/10.48693/241 | - |
| dc.identifier.uri | https://osnadocs.ub.uni-osnabrueck.de/handle/ds-202301318164 | - |
| dc.description.abstract | Reinforcement learning algorithms have been very successful at solving sequential decision-making problems in many different problem domains. However, their training is often time-consuming, with training times ranging from multiple hours to weeks. The development of domain-specific architectures for reinforcement learning promises faster computation times, decreased experiment turn-around time, and improved energy efficiency. This paper presents a review of hardware architectures for the acceleration of reinforcement learning algorithms. FPGA-based implementations are the focus of this work, but GPU-based approaches are considered as well. Both tabular and deep reinforcement learning algorithms are included in this survey. The techniques employed in different implementations are highlighted and compared. Finally, possible areas for future work are suggested, based on the preceding discussion of existing architectures. | eng |
| dc.relation | https://doi.org/10.1109/ACCESS.2022.3146518 | ger |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Reinforcement learning | eng |
| dc.subject | Computer architecture | eng |
| dc.subject | Training | eng |
| dc.subject | Neural networks | eng |
| dc.subject | Optimization | eng |
| dc.subject | Graphics processing units | eng |
| dc.subject | Q-learning | eng |
| dc.subject | Domain-specific architectures | eng |
| dc.subject | machine learning | eng |
| dc.subject | deep learning | eng |
| dc.subject | reconfigurable architectures | eng |
| dc.subject | FPGA | eng |
| dc.subject.ddc | 004 - Informatik | ger |
| dc.title | A Survey of Domain-Specific Architectures for Reinforcement Learning | eng |
| dc.type | Einzelbeitrag in einer wissenschaftlichen Zeitschrift [Article] | ger |
| orcid.creator | https://orcid.org/0000-0003-2886-8197 | - |
| orcid.creator | https://orcid.org/0000-0003-1005-5753 | - |
| dc.identifier.doi | 10.1109/ACCESS.2022.3146518 | - |
| Enthalten in den Sammlungen: | FB06 - Hochschulschriften Open-Access-Publikationsfonds | |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| IEEEAccess_Rothmann_etal_2022.pdf | Article | 1,21 MB | Adobe PDF | IEEEAccess_Rothmann_etal_2022.pdf  Öffnen/Anzeigen |
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