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
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; firstname.lastname@example.org).
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.