A general framework for reinforcement learning in cognitive architectures

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorArtificial Intelligence and Cognitive Architectures Hub (H.IAAC)-
Autor(es): dc.creatorMorais, Gustavo-
Autor(es): dc.creatorYuji, Eduardo-
Autor(es): dc.creatorCosta, Paula-
Autor(es): dc.creatorSimões, Alexandre-
Autor(es): dc.creatorGudwin, Ricardo-
Autor(es): dc.creatorColombini, Esther-
Data de aceite: dc.date.accessioned2025-08-21T15:38:15Z-
Data de disponibilização: dc.date.available2025-08-21T15:38:15Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-06-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.cogsys.2025.101354-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/298348-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/298348-
Descrição: dc.descriptionRecent advancements in reinforcement learning (RL), particularly deep RL, show the capacity of this paradigm to perform varied and complex tasks. However, a series of exploration, generalization, and adaptation challenges hold RL back from operating in more general contexts. In this paper, we explore integrating techniques originating from cognitive research into existing RL algorithms by defining a general framework to standardize interoperation between arbitrary cognitive modules and arbitrary RL techniques. We show the potential of hybrid approaches through a comparative experiment that integrates an episodic memory encoder with a well-known deep RL algorithm. Furthermore, we show that built-in RL algorithms with different cognitive modules can fit our framework, as well as remotely run algorithms. Hence, we propose a way forward for RL in the form of innovative solutions that integrate research in cognitive systems with recent RL techniques.-
Descrição: dc.descriptionInstitute of Computing University of Campinas, Av. Albert Einstein, 1251 - Cidade Universitária-
Descrição: dc.descriptionSchool of Electrical and Computer Engineering University of Campinas, Av. Albert Einstein, N° 400 - Cidade Universitária-
Descrição: dc.descriptionDepartment of Control and Automation Engineering São Paulo State University, Av. Três de Março, 511 - Alto da Boa Vista-
Descrição: dc.descriptionArtificial Intelligence and Cognitive Architectures Hub (H.IAAC), Av. Albert Einstein, 1251 - Cidade Universitária-
Descrição: dc.descriptionDepartment of Control and Automation Engineering São Paulo State University, Av. Três de Março, 511 - Alto da Boa Vista-
Idioma: dc.languageen-
Relação: dc.relationCognitive Systems Research-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCognitive architectures-
Palavras-chave: dc.subjectReinforcement learning-
Título: dc.titleA general framework for reinforcement learning in cognitive architectures-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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