Mostrar el registro sencillo del ítem
dc.contributor.author | Zhao, Yue | es_ES |
dc.contributor.author | Hernández-Orallo, José | es_ES |
dc.date.accessioned | 2024-10-18T18:02:21Z | |
dc.date.available | 2024-10-18T18:02:21Z | |
dc.date.issued | 2024-06 | es_ES |
dc.identifier.issn | 0952-813X | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/210585 | |
dc.description.abstract | [EN] The performance in multi-agent reinforcement learning (MARL) scenarios has usually been analysed in homogeneous teams with a few choices for the sociality regime (selfish, egalitarian, or altruistic). In this paper we analyse both homogeneous and heterogeneous teams in a variation of sociality regimes in the predator-prey game, using a novel normalisation of the weights so that the sum of all rewards is independent of the sociality regime. We find that the selfish regime is advantageous for both predator and prey teams, and for both homogeneous and heterogeneous teams. In particular, rewards are about 100% higher for the predator team when switching from the egalitarian to selfish regime and more than 400% higher from the altruistic regime. For the prey, the increase is around 40% and 100% respectively. The results are similar for homogeneous and heterogeneous situations. The takeaway message is that any study of homogeneous and heterogeneous cooperative-competitive multi-agent reinforcement learning teams should also take into account the sociality regimes before making conclusions on the preference of any algorithm. | es_ES |
dc.description.sponsorship | This work was funded by the EU (FEDER) and Spanish grant RTI2018-094403-B-C32 funded by MCIN/AEI/10.13039/501100011033 and by 'ERDF A way of making Europe', Generalitat Valenciana under CIPROM/2022/6 (FASSLOW) and IDIFEDER/2021/05 (CLUSTERIA), EU's Horizon 2020 research and innovation programme under grant agreement No. 952215 (TAILOR) and Spanish grant PID2021-122830OB-C42 (SFERA) funded by MCIN/AEI/10.13039/501100011033 and 'ERDF A way of making Europe' and China Scholarship Council (CSC) scholarship (No. 202006290201). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Taylor & Francis | es_ES |
dc.relation.ispartof | Journal of Experimental & Theoretical Artificial Intelligence | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Multi-agent | es_ES |
dc.subject | Reinforcement learning | es_ES |
dc.subject | Sociality | es_ES |
dc.subject | Cooperative-competitive game | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | The impact of sociality regimes on heterogeneous cooperative-competitive multi-agent reinforcement learning: a study with the predator-prey game | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1080/0952813X.2024.2361408 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094403-B-C32/ES/RAZONAMIENTO FORMAL PARA TECNOLOGIAS FACILITADORAS Y EMERGENTES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122830OB-C42/ES/METODOS FORMALES ESCALABLES PARA APLICACIONES REALES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/952215/EU/Integrating Reasoning, Learning and Optimization/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/CSC//202006290201/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//CIPROM%2F2022%2F6//Tecnologías de Aprendizaje y Razonamiento Rápido y Lento/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//IDIFEDER%2F2021%2F05/ | es_ES |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.description.bibliographicCitation | Zhao, Y.; Hernández-Orallo, J. (2024). The impact of sociality regimes on heterogeneous cooperative-competitive multi-agent reinforcement learning: a study with the predator-prey game. Journal of Experimental & Theoretical Artificial Intelligence. https://doi.org/10.1080/0952813X.2024.2361408 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1080/0952813X.2024.2361408 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | S\526381 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | China Scholarship Council | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |