Publication
Convergence of the Reinforcement Learning Mechanism Applied to the Channel Detection Sequence Problem
| dc.contributor.author | Mendes, Andre C. | |
| dc.date.accessioned | 2022-04-05T08:31:28Z | |
| dc.date.available | 2022-04-05T08:31:28Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | The use of mechanisms based on artificial intelligence techniques to perform dynamic learning has received much attention recently and has been applied in solving many problems. However, the convergence analysis of these mechanisms does not always receive the same attention. In this paper, the convergence of the mechanism using reinforcement learning to determine the channel detection sequence in a multi-channel, multi-user radio network is discussed and, through simulations, recommendations are presented for the proper choice of the learning parameter set to improve the overall reward. Then, applying the related set of parameters to the problem, the mechanism is compared to other intuitive sorting mechanisms. | pt_PT |
| dc.description.sponsorship | This work has been conducted under the project “BIOMA – Bioeconomy integrated solutions for the mobilization of the Agri-food market” (POCI-01-0247-FEDER-046112), by “BIOMA” Consortium, and financed by European Regional Development Fund (FEDER), through the Incentive System to Research and Technological development, within the Portugal2020 Competitiveness and Internationalization Operational Program. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Mendes, Andre C. (2021). Convergence of the reinforcement learning mechanism applied to the channel detection sequence problem. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 401-416. ISBN 978-3-030-91884-2 | pt_PT |
| dc.identifier.doi | 10.1007/978-3-030-91885-9_30 | pt_PT |
| dc.identifier.isbn | 978-3-030-91884-2 | |
| dc.identifier.uri | http://hdl.handle.net/10198/25330 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer Nature | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Artificial intelligence | pt_PT |
| dc.subject | Reinforcement learning | pt_PT |
| dc.subject | Convergence analysis | pt_PT |
| dc.subject | Channel detection sequence problem | pt_PT |
| dc.title | Convergence of the Reinforcement Learning Mechanism Applied to the Channel Detection Sequence Problem | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 416 | pt_PT |
| oaire.citation.startPage | 401 | pt_PT |
| oaire.citation.title | Optimization, learning algorithms and applications: first International Conference, OL2A 2021 | pt_PT |
| oaire.citation.volume | 1488 | pt_PT |
| person.familyName | Mendes | |
| person.givenName | Andre C. | |
| person.identifier.ciencia-id | 6718-7AE9-0953 | |
| person.identifier.orcid | 0000-0001-6390-1250 | |
| person.identifier.scopus-author-id | 54920397900 | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | 469e664f-f0ce-4ccf-a00d-21c086996765 | |
| relation.isAuthorOfPublication.latestForDiscovery | 469e664f-f0ce-4ccf-a00d-21c086996765 |
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