DECAF: Deep Case-based Policy Inference for Knowledge Transfer in Reinforcement Learning

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dc.contributor.authorGLATT, RUBEN
dc.contributor.authorSILVA, FELIPE LENO DA
dc.contributor.authorReinaldo Bianchi
dc.contributor.authorCOSTA, ANNA HELENA REALI
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-9097-827X
dc.date.accessioned2021-11-24T00:35:41Z
dc.date.available2021-11-24T00:35:41Z
dc.date.issued2020-10-15
dc.description.abstractHaving the ability to solve increasingly complex problems using Reinforcement Learning (RL) has prompted researchers to start developing a greater interest in systematic approaches to retain and reuse knowledge over a variety of tasks. With Case-based Reasoning (CBR) there exists a general methodology that provides a framework for knowledge transfer which has been underrepresented in the RL literature so far. We for- mulate a terminology for the CBR framework targeted towards RL researchers with the goal of facilitating communication between the respective research communities. Based on this framework, we propose the Deep Case-based Policy Inference (DECAF) algorithm to accelerate learning by building a library of cases and reusing them if they are similar to a new task when training a new policy. DECAF guides the train- ing by dynamically selecting and blending policies according to their usefulness for the current target task, reusing previously learned policies for a more effective exploration but still enabling the adaptation to particularities of the new task. We show an empirical evaluation in the Atari game playing domain depicting the benefits of our algorithm with regards to sample efficiency, robustness against negative transfer, and performance increase when compared to state-of-the-art methods.
dc.description.firstpage113420
dc.description.volume1
dc.identifier.citationGLATT, R.; S., F. L. DA; BIANCHI, R. A. DA C.; COSTA, A. H R. DECAF: Deep Case-based Policy Inference for Knowledge Transfer in Reinforcement Learning. EXPERT SYSTEMS WITH APPLICATIONS, v. 1, p. 113420, 2020.
dc.identifier.doi10.1016/j.eswa.2020.113420
dc.identifier.issn0957-4174
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3471
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONS
dc.rightsAcesso Restrito
dc.rights.licenseCreative Commons "Este é um artigo publicado em acesso aberto sob uma licença Creative Commons (CC BY-NC-ND 4.0). Fonte: https://www.sciencedirect.com/science/article/pii/S095741742030244X?via%3Dihub. Acesso em: 23 nov. 2021.
dc.subjectDeep Reinforcement Learning
dc.subjectCase-based Reasoning
dc.subjectTransfer Learning
dc.subjectKnowledge discovery
dc.subjectKnowledge management
dc.subjectNeural networks
dc.titleDECAF: Deep Case-based Policy Inference for Knowledge Transfer in Reinforcement Learningpt_BR
dc.typeArtigopt_BR
fei.scopus.citations19
fei.scopus.eid2-s2.0-85083423326
fei.scopus.updated2024-03-04
fei.source.urlhttps://www.sciencedirect.com/science/article/pii/S095741742030244X?via%3Dihub
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