Heuristic Q-learning soccer players: A new reinforcement learning approach to RoboCup simulation

dc.contributor.authorCELIBERTO JUNIOR, L. A.
dc.contributor.authorMATSMURA, J.
dc.contributor.authorReinaldo Bianchi
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-9097-827X
dc.date.accessioned2022-01-12T22:05:26Z
dc.date.available2022-01-12T22:05:26Z
dc.date.issued2007-12-03
dc.description.abstractThis paper describes the design and implementation of a 4 player RoboCup Simulation 2D team, which was build by adding Heuristic: Accelerated Reinforcement Learning capabilities to basic players of the well-known UvA Trilearn team. The implemented agents learn by using a recently proposed Heuristic Heinforcement Learning algorithm, the Heuristically Accelerated Q-Learning (HAQL), which allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q-Learning. A set of empirical evaluations was conducted in the RoboCup 2D Simulator, and experimental results obtained while playing with other teams shows that the approach adopted hero is very promising. © Springer-Verlag Berlin Heidelberg 2007.
dc.description.firstpage520
dc.description.lastpage529
dc.description.volume4874 LNAI
dc.identifier.citationCELIBERTO JUNIOR, L. A.; MATSMURA, J.; BIANCHI, R. Heuristic Q-learning soccer players: A new reinforcement learning approach to RoboCup simulation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), p. 520-529, Dec. 2007.
dc.identifier.doi10.1007/978-3-540-77002-2_44
dc.identifier.issn1611-3349
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4339
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsAcesso Restrito
dc.subject.otherlanguageCognitive robotics
dc.subject.otherlanguageReinforcement learning
dc.subject.otherlanguageRoboCup simulation 2D
dc.titleHeuristic Q-learning soccer players: A new reinforcement learning approach to RoboCup simulation
dc.typeArtigo de evento
fei.scopus.citations3
fei.scopus.eid2-s2.0-38349070325
fei.scopus.subjectEmpirical evaluations
fei.scopus.subjectLearning capabilities
fei.scopus.updated2024-02-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=38349070325&origin=inward
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