Improving Reinforcement Learning by using Case-Based Heuristics
The original publication is available at www.springerlink.com
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2009 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/18069 |
| Acceso en línea: | http://hdl.handle.net/10261/18069 |
| Access Level: | acceso abierto |
| Palabra clave: | Case-based reasoning CBR Reinforcement learning Case-based heuristically accelerated reinforcement learning Multiagent learning |
| id |
ES_4d359bb4fe826b90bd6db692aa1e7c39 |
|---|---|
| oai_identifier_str |
oai:digital.csic.es:10261/18069 |
| network_acronym_str |
ES |
| network_name_str |
España |
| repository_id_str |
|
| spelling |
Improving Reinforcement Learning by using Case-Based HeuristicsBianchi, ReinaldoRos Espinoza, RaquelLópez de Mántaras, RamónCase-based reasoningCBRReinforcement learningCase-based heuristically accelerated reinforcement learningMultiagent learningThe original publication is available at www.springerlink.comThis work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and Reinforcement Learning (RL) techniques. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. CB-HARL is a subset of RL that makes use of a heuristic function derived from a case base, in a Case Based Reasoning manner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Q–Learning is also proposed. Empirical evaluations were conducted in a simulator for the RoboCup Four-Legged Soccer Competition, and results obtained shows that using CB-HARL, the agents learn faster than using either RL or HARL methods.This work has been partially funded by the FI grant and the BE grant from the AGAUR, the 2005-SGR-00093 project, supported by the Generalitat de Catalunya, the MID-CBR project grant TIN 2006-15140-C03-01 and FEDER funds. Reinaldo Bianchi is supported by CNPq grant 201591/2007-3 and FAPESP grant 2009/01610-1.Peer reviewedSpringer Nature200920092009info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501189739 bytesapplication/pdfhttp://hdl.handle.net/10261/18069reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés10.1007/978-3-642-02998-1_7info:eu-repo/semantics/openAccessoai:digital.csic.es:10261/180692026-05-22T06:33:51Z |
| dc.title.none.fl_str_mv |
Improving Reinforcement Learning by using Case-Based Heuristics |
| title |
Improving Reinforcement Learning by using Case-Based Heuristics |
| spellingShingle |
Improving Reinforcement Learning by using Case-Based Heuristics Bianchi, Reinaldo Case-based reasoning CBR Reinforcement learning Case-based heuristically accelerated reinforcement learning Multiagent learning |
| title_short |
Improving Reinforcement Learning by using Case-Based Heuristics |
| title_full |
Improving Reinforcement Learning by using Case-Based Heuristics |
| title_fullStr |
Improving Reinforcement Learning by using Case-Based Heuristics |
| title_full_unstemmed |
Improving Reinforcement Learning by using Case-Based Heuristics |
| title_sort |
Improving Reinforcement Learning by using Case-Based Heuristics |
| dc.creator.none.fl_str_mv |
Bianchi, Reinaldo Ros Espinoza, Raquel López de Mántaras, Ramón |
| author |
Bianchi, Reinaldo |
| author_facet |
Bianchi, Reinaldo Ros Espinoza, Raquel López de Mántaras, Ramón |
| author_role |
author |
| author2 |
Ros Espinoza, Raquel López de Mántaras, Ramón |
| author2_role |
author author |
| dc.subject.none.fl_str_mv |
Case-based reasoning CBR Reinforcement learning Case-based heuristically accelerated reinforcement learning Multiagent learning |
| topic |
Case-based reasoning CBR Reinforcement learning Case-based heuristically accelerated reinforcement learning Multiagent learning |
| description |
The original publication is available at www.springerlink.com |
| publishDate |
2009 |
| dc.date.none.fl_str_mv |
2009 2009 2009 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 |
| format |
article |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10261/18069 |
| url |
http://hdl.handle.net/10261/18069 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
10.1007/978-3-642-02998-1_7 |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
189739 bytes application/pdf |
| dc.publisher.none.fl_str_mv |
Springer Nature |
| publisher.none.fl_str_mv |
Springer Nature |
| dc.source.none.fl_str_mv |
reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC instname:Consejo Superior de Investigaciones Científicas (CSIC) |
| instname_str |
Consejo Superior de Investigaciones Científicas (CSIC) |
| reponame_str |
DIGITAL.CSIC. Repositorio Institucional del CSIC |
| collection |
DIGITAL.CSIC. Repositorio Institucional del CSIC |
| repository.name.fl_str_mv |
|
| repository.mail.fl_str_mv |
|
| _version_ |
1869407677035053056 |
| score |
15,812429 |