Reinforcement learning control of unknown dynamic systems

The paper is concerned with the application of reinforcement learning techniques to the stochastic control problem, and in particular presents a method based on learning automata for designing controllers for the control of unknown complex dynamic systems. The work is focused on the design of a learning automation using subsets of control actions to reduce the number of actions during a learning procedure. The subsets of actions can be expanded or contracted according to action probabilities which are reset from time to time so as to achieve a global selection over the action set. Two reinforcement schemes are investigated alongside the variable subsets of control actions. A reference performance index and an approach to quantification and normalisation of the performance index are proposed in association with the two schemes to evaluate environment responses during the learning procedure. The method has been used to achieve learning control for an unknown nonlinear turbogenerator system.