Online Support Vector Regression based Value Function Approximation for Reinforcement Learning

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 212
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorLee, Dong-Hyun-
dc.contributor.authorQuang, Vo Van-
dc.contributor.authorJo, Sungho-
dc.contributor.authorLee, Ju-Jang-
dc.date.accessioned2013-03-27T23:30:17Z-
dc.date.available2013-03-27T23:30:17Z-
dc.date.created2012-02-06-
dc.date.issued2009-07-05-
dc.identifier.citationIEEE International Symposium on Industrial Electronics, IEEE ISIE 2009, v., no., pp.449 - 454-
dc.identifier.urihttp://hdl.handle.net/10203/162242-
dc.description.abstractThis paper proposes the online Support Vector Regression (SVR) based value function approximation method for Reinforcement Learning (RL). This approach conserves the Support Vector Machine (SVM)'s good property, the generalization which is a key issue of function approximation. Online SVR can do incremental learning and automatically track variation of environment with time-varying characteristics. Using the online SVR, we can obtain the fast and good estimation of value function and achieve RL objective efficiently. Throughout simulation tests, the feasibility and usefulness of the proposed approach is demonstrated by comparison with SARSA and Q-learning.-
dc.languageENG-
dc.publisherIEEE-
dc.titleOnline Support Vector Regression based Value Function Approximation for Reinforcement Learning-
dc.typeConference-
dc.identifier.scopusid2-s2.0-77950137292-
dc.type.rimsCONF-
dc.citation.beginningpage449-
dc.citation.endingpage454-
dc.citation.publicationnameIEEE International Symposium on Industrial Electronics, IEEE ISIE 2009-
dc.identifier.conferencecountrySouth Korea-
dc.identifier.conferencecountrySouth Korea-
dc.contributor.localauthorJo, Sungho-
dc.contributor.localauthorLee, Ju-Jang-
dc.contributor.nonIdAuthorLee, Dong-Hyun-
dc.contributor.nonIdAuthorQuang, Vo Van-
Appears in Collection
EE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0