Duální řízení Markovských řetězců

Abstract

This thesis is engaged in design of optimal decision strategy of simple Markov decision process, whose internals aren't known exactly. The optimal strategy is designed using Bayesian statistics. Thus, the optimization procedure is taken as consisting of two interconnected parts, strategy design and active learning (acquiring and using new informations about the system). This control method is reffered to as a dual control.