Heuristic Q-learning soccer players: A new reinforcement learning approach to RoboCup simulation
dc.contributor.author | CELIBERTO JUNIOR, L. A. | |
dc.contributor.author | MATSMURA, J. | |
dc.contributor.author | Reinaldo Bianchi | |
dc.contributor.authorOrcid | https://orcid.org/0000-0001-9097-827X | |
dc.date.accessioned | 2022-01-12T22:05:26Z | |
dc.date.available | 2022-01-12T22:05:26Z | |
dc.date.issued | 2007-12-03 | |
dc.description.abstract | This 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.firstpage | 520 | |
dc.description.lastpage | 529 | |
dc.description.volume | 4874 LNAI | |
dc.identifier.citation | CELIBERTO 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.doi | 10.1007/978-3-540-77002-2_44 | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.uri | https://repositorio.fei.edu.br/handle/FEI/4339 | |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.rights | Acesso Restrito | |
dc.subject.otherlanguage | Cognitive robotics | |
dc.subject.otherlanguage | Reinforcement learning | |
dc.subject.otherlanguage | RoboCup simulation 2D | |
dc.title | Heuristic Q-learning soccer players: A new reinforcement learning approach to RoboCup simulation | |
dc.type | Artigo de evento | |
fei.scopus.citations | 3 | |
fei.scopus.eid | 2-s2.0-38349070325 | |
fei.scopus.subject | Empirical evaluations | |
fei.scopus.subject | Learning capabilities | |
fei.scopus.updated | 2024-09-01 | |
fei.scopus.url | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=38349070325&origin=inward |