Kernel-based actor-critic approach with applications
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 주백석 | - |
dc.contributor.author | 정근우 | - |
dc.contributor.author | 박주영 | - |
dc.date.accessioned | 2021-09-07T20:10:40Z | - |
dc.date.available | 2021-09-07T20:10:40Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2011 | - |
dc.identifier.issn | 1598-2645 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/114549 | - |
dc.description.abstract | Recently, actor-critic methods have drawn significant interests in the area of reinforcement learning, and several algorithms have been studied along the line of the actor-critic strategy. In this paper, we consider a new type of actor-critic algorithms employing the kernel methods, which have recently shown to be very effective tools in the various fields of machine learning, and have performed investigations on combining the actor-critic strategy together with kernel methods. More specifically, this paper studies actor-critic algorithms utilizing the kernel-based least-squares estimation and policy gradient, and in its critic’s part, the study uses a sliding-window-based kernel least-squares method, which leads to a fast and efficient value-function-estimation in a nonparametric setting. The applicability of the considered algorithms is illustrated via a robot locomotion problem and a tunnel ventilation control problem. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | 한국지능시스템학회 | - |
dc.title | Kernel-based actor-critic approach with applications | - |
dc.title.alternative | Kernel-based actor-critic approach with applications | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 박주영 | - |
dc.identifier.bibliographicCitation | International Journal of Fuzzy Logic and Intelligent systems, v.11, no.4, pp.267 - 274 | - |
dc.relation.isPartOf | International Journal of Fuzzy Logic and Intelligent systems | - |
dc.citation.title | International Journal of Fuzzy Logic and Intelligent systems | - |
dc.citation.volume | 11 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 267 | - |
dc.citation.endPage | 274 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART001611869 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | reinforcement learning | - |
dc.subject.keywordAuthor | actor-critic algorithm | - |
dc.subject.keywordAuthor | kernel methods | - |
dc.subject.keywordAuthor | least-squares | - |
dc.subject.keywordAuthor | sliding-windows | - |
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