Reinforcement Learning Control for Discrete-time Systems over Fading Channels

Abstract

This paper studies the average cost minimization problem for a class of discrete-time systems suffering from fading channels and additive noise. An approximate generalized policy iteration algorithm is proposed to search for the optimal control policy for the considered problem without resorting to the system matrices. The estimation errors of the kernel matrices are proven to be small and the proposed model-free reinforcement learning algorithm is proven to be convergent. Some numerical examples are provided to illustrate the obtained results.