Kernel-based actor-critic approach with applications
- Authors
- 주백석; 정근우; 박주영
- Issue Date
- 2011
- Publisher
- 한국지능시스템학회
- Keywords
- reinforcement learning; actor-critic algorithm; kernel methods; least-squares; sliding-windows
- Citation
- International Journal of Fuzzy Logic and Intelligent systems, v.11, no.4, pp.267 - 274
- Journal Title
- International Journal of Fuzzy Logic and Intelligent systems
- Volume
- 11
- Number
- 4
- Start Page
- 267
- End Page
- 274
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/2721
- ISSN
- 1598-2645
- 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.
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