Reinforcement Learning with Kernel Recursive Least-Squares Support Vector Machine
Hitesh B. Shah, Mahesh Gopal · International Journal of Machine Learning and Computing · 2012
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-