A learning controller model for on-line optimization of a class of stochastic control systems

In this paper, a learning controller model is applied to the on-line optimization of a general class of discrete stochastic control systems. Stochastic properties of the plant are unknown to the designer. Using the plant's output, the controller iteratively applys a reinforcement learning algorithm to learn the optimum control policy while minimizing an expected performance index. A technique that accelerates learning and improves performance is discussed. Computer simulation results of the successful application of the learning controller model to several different cases are presented.