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.