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dc.contributor.authorAlegre, Lucas Nunespt_BR
dc.contributor.authorBazzan, Ana Lucia Cetertichpt_BR
dc.contributor.authorSilva, Bruno Carreiro dapt_BR
dc.date.accessioned2023-04-07T03:26:12Zpt_BR
dc.date.issued2021pt_BR
dc.identifier.issn2376-5992pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/256805pt_BR
dc.description.abstractIn reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent. More precisely, we study both the effects of changing the context in which an agent learns (e.g., a change in flow rates experienced by it), as well as the effects of reducing agent observability of the true environment state. Partial observability may cause distinct states (in which distinct actions are optimal) to be seen as the same by the traffic signal agents. This, in turn, may lead to sub-optimal performance. We show that the lack of suitable sensors to provide a representative observation of the real state seems to affect the performance more drastically than the changes to the underlying traffic patterns.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofPeerJ Computer Science. New York : PeerJ, 2021. Vol. 7, (mar. 2021), 20 p.pt_BR
dc.rightsOpen Accessen
dc.subjectMultiagent systemsen
dc.subjectSistemas multiagentespt_BR
dc.subjectAprendizado por reforçopt_BR
dc.subjectReinforcement learningen
dc.subjectTraffic signal controlen
dc.subjectInformatica : Transportespt_BR
dc.subjectNon-stationarityen
dc.titleQuantifying the impact of non-stationarity in reinforcement learning-based traffic signal controlpt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001143089pt_BR
dc.type.originEstrangeiropt_BR


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