HLA: A Novel Hybrid Model based on Fixed Structure and Variable Structure Learning Automata

Abstract

Learning Automata (LAs) are adaptive decision-making models designed to find an appropriate action in unknown environments. LAs can be classified into two classes: variable structure and fixed structure. To the best of our knowledge, there is no hybrid model based on both of these classes. In this paper, we propose a model that brings together the benefits of both classes of LAs. In the proposed model, called an HLA, the action switching phase of a fixed structure learning automaton is fused with a variable structure learning automaton. Several computer simulations are conducted to study the performance of the proposed model with respect to the total number of rewards and action switching in addition to the convergence rate. The proposed model is compared to both variable structure and fixed structure learning automata, and in most cases, the numerical results demonstrate its superiority. In order to show the applicability of the HLA, a novel adaptive dropout mechanism in deep neural networks was suggested. The results of the simulations show that the proposed mechanism performs better than the simple dropout mechanism with respect to network accuracy.