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Collaborative Reinforcement Learning of Energy Contracts Negotiation Strategies

2019Conference object Open access
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dc.contributor.authorPinto, Tiago
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.contributor.authorSantos, Carlos
dc.date.accessioned2021-09-22T14:47:29Z
dc.date.available2021-09-22T14:47:29Z
dc.date.issued2019
dc.description.abstractThis paper presents the application of collaborative reinforcement learning models to enable the distributed learning of energy contracts negotiation strategies. The learning model combines the learning process on the best negotiation strategies to apply against each opponent, in each context, from multiple learning sources. The diverse learning sources are the learning processes of several agents, which learn the same problem under different perspectives. By combining the different independent learning processes, it is possible to gather the diverse knowledge and reach a final decision on the most suitable negotiation strategy to be applied. The reinforcement learning process is based on the application of the Q-Learning algorithm; and the continuous combination of the different learning results applies and compares several collaborative learning algorithms, namely BEST-Q, Average (AVE)-Q; Particle Swarm Optimization (PSO)-Q, and Weighted Strategy Sharing (WSS)-Q. Results show that the collaborative learning process enables players’ to correctly identify the negotiation strategy to apply in each moment, context and against each opponent.pt_PT
dc.description.sponsorshipThis work has been developed under the MAS-SOCIETY project - PTDC/EEI-EEE/28954/2017 and has received funding from UID/EEA/00760/2019, funded by FEDER Funds through COMPETE and by National Funds through FCT.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-030-24299-2_17pt_PT
dc.identifier.isbn978-3-030-24299-2
dc.identifier.urihttp://hdl.handle.net/10400.22/18485
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relationMulti-Agent Systems SemantiC Interoperability for simulation and dEcision supporT in complex energY systems
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-030-24299-2_17pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectCollaborative reinforcement learningpt_PT
dc.subjectElectricity marketspt_PT
dc.subjectEnergy contracts negotiationpt_PT
dc.subjectNegotiation strategiespt_PT
dc.subjectQ-Learningpt_PT
dc.titleCollaborative Reinforcement Learning of Energy Contracts Negotiation Strategiespt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleMulti-Agent Systems SemantiC Interoperability for simulation and dEcision supporT in complex energY systems
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FEEI-EEE%2F28954%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F00760%2F2019/PT
oaire.citation.endPage210pt_PT
oaire.citation.startPage202pt_PT
oaire.citation.titleComputer and Information Sciencept_PT
oaire.citation.volume1047pt_PT
oaire.fundingStream9471 - RIDTI
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePinto
person.familyNamePraça
person.familyNameVale
person.givenNameTiago
person.givenNameIsabel
person.givenNameZita
person.identifierR-000-T7J
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person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridK-8430-2014
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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