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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(5): 618-622 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.201
 

Reinforcement Learning with Kernel Recursive Least-Squares Support Vector Machine

Hitesh Shah and M. Gopal
Abstract—A reinforcement learning system based on the kernel recursive least-squares algorithm for continuous state-space is proposed in this paper. A kernel recursive least-squares- support vector machine is used to realized a mapping from state-action pair to Q-value function. An online sparsification process that permits the addition of training sample into the Q-function approximation only if it is approximately linearly independent of the preceding training samples. Simulation result of two-link robot manipulator show that the proposed method has high learning efficiency – better accuracy measured in terms of mean square error, and lesser computation time compare to the least-squares support vector machine.

Index Terms—Kernel methods, least-squares support vector machine, recursive least squares, reinforcement learning.

The authors are with Department of Electrical Engineering, Indian Institute of Technology – Delhi, New Delhi, India (e-mail: iitd.hitesh@ gmail.com; mgopal@ee.iitd.ac.in).

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Cite:Hitesh Shah and M. Gopal, "Reinforcement Learning with Kernel Recursive Least-Squares Support Vector Machine," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 618-622, 2012.

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