Reinforcement Learning with Kernel Recursive Least-Squares Support Vector Machine

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.