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Author Jae Young Lee ♦ Jin Bae Park ♦ Yoon Ho Choi
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Other branches of engineering
Subject Keyword Convergence ♦ Heuristic algorithms ♦ DC motors ♦ Equations ♦ Trajectory ♦ Least squares approximation
Abstract In this paper, a novel generalized value iteration (VI) technique is presented which is a reinforcement learning (RL) scheme for solving online the continuous-time (CT) discounted linear quadratic regulation (LQR) problems without exactly knowing the system matrix A. In the proposed method, a discounted value function is considered, which is a general setting in RL frameworks, but not fully considered in RL for CT dynamical systems. Moreover, a stepwise-varying learning rate is introduced for the fast and safe convergence. In relation to this learning rate, we also discuss the locations of the poles of the closed-loop system and monotone convergence to the optimal solution. The results from these discussions give the conditions on the stability and monotone convergence of the existing VI methods.
Description Author affiliation: Department of Electrical and Electronic Engineering, Yonsei University, Shinchon-Dong, Seodaemum-Gu, Seoul 120-749, Korea (Jae Young Lee; Jin Bae Park) || Department of Electronic Engineering, Kyonggi University, Suwon, Kyonggi-Do 443-760, Korea (Yoon Ho Choi)
ISBN 9781424477456
ISSN 07431546
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-12-15
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424477463
Size (in Bytes) 180.77 kB
Page Count 6
Starting Page 4637
Ending Page 4642

Source: IEEE Xplore Digital Library