Using transfer learning to speed-up Reinforcement Learning: A cased-based approach

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dc.contributor.authorCELIBERTO JUNIOR, L. A.
dc.contributor.authorMATSUURA, J. P.
dc.contributor.authorMANTARAS, R. L.
dc.contributor.authorReinaldo Bianchi
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-9097-827X
dc.date.accessioned2022-01-12T22:03:54Z
dc.date.available2022-01-12T22:03:54Z
dc.date.issued2010-10-28
dc.description.abstractReinforcement Learning (RL) is a well-known technique for the solution of problems where agents need to act with success in an unknown environment, learning through trial and error. However, this technique is not efficient enough to be used in applications with real world demands due to the time that the agent needs to learn. This paper investigates the use of Transfer Learning (TL) between agents to speed up the well-known Q-learning Reinforcement Learning algorithm. The new approach presented here allows the use of cases in a case base as heuristics to speed up the Q-learning algorithm, combining Case-Based Reasoning (CBR) and Heuristically Accelerated Reinforcement Learning (HARL) techniques. A set of empirical evaluations were conducted in the Mountain Car Problem Domain, where the actions learned during the solution of the 2D version of the problem can be used to speed up the learning of the policies for its 3D version. The experiments were made comparing the Q-learning Reinforcement Learning algorithm, the HAQL Heuristic Accelerated Reinforcement Learning (HARL) algorithm and the TL-HAQL algorithm, proposed here. The results show that the use of a case-base for transfer learning can lead to a significant improvement in the performance of the agent, making it learn faster than using either RL or HARL methods alone. © 2010 IEEE.
dc.description.firstpage55
dc.description.lastpage60
dc.identifier.citationCELIBERTO JUNIOR, L. A.; MATSUURA, J. P.; MANTARAS, R. L.; BIANCHI, R. Using transfer learning to speed-up Reinforcement Learning: A cased-based approach. Proceedings - 2010 Latin American Robotics Symposium and Intelligent Robotics Meeting, LARS 2010, p. 55-60, Oct. 2010.
dc.identifier.doi10.1109/LARS.2010.24
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4235
dc.relation.ispartofProceedings - 2010 Latin American Robotics Symposium and Intelligent Robotics Meeting, LARS 2010
dc.rightsAcesso Restrito
dc.subject.otherlanguageCase Based Reasoning
dc.subject.otherlanguageMachine Learning
dc.subject.otherlanguageReinforcement Learning
dc.titleUsing transfer learning to speed-up Reinforcement Learning: A cased-based approach
dc.typeArtigo de evento
fei.scopus.citations21
fei.scopus.eid2-s2.0-79952092446
fei.scopus.subjectCase base
fei.scopus.subjectCBr
fei.scopus.subjectEmpirical evaluations
fei.scopus.subjectMachine-learning
fei.scopus.subjectNew approaches
fei.scopus.subjectProblem domain
fei.scopus.subjectQ-learning
fei.scopus.subjectQ-learning algorithms
fei.scopus.subjectSpeed-ups
fei.scopus.subjectTransfer learning
fei.scopus.subjectTrial and error
fei.scopus.subjectUnknown environments
fei.scopus.updated2024-07-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79952092446&origin=inward
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