Variable Hierarchical Structure Learning Automata Operating in a Stationary Random Environment

Abstract

The purpose of this paper is to construct a new hierarchical structure stochastic automata for applying to the problem in which the number of outputs and the contents of them are not known in advance.
The variable hierarchical structure stochastic automata (algorithm) is proposed, and the learning behaviors of it are shown, i.e., it is ε-optimal in any structure and that the probability of being selected the best output by it can be approached 1 as much as possible. In numerical simulation, it is known that this algorithm can be successfully used to make the mobile robot find the best path in the unknown structure maze.