Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications
Online ISSN : 2188-4749
Print ISSN : 2188-4730
The 50th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (Nov. 2018, KYOTO)
Conditional Optimality of Learning Automata with 2-state Bayesian Estimators
Motoshi HaraNoriyo KanayamaToru WatanabeSatoru KatoHiroyuki Kamaya
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2019 Volume 2019 Pages 223-228

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Abstract

A novel learning automaton with 2 state Bayesian estimators, which is based on β-type learning automaton, is considered. The β-type learning automaton with multiple Bayesian estimators is our original work and the scheme of it has been proven to be conditionally optimal under some environments. Furthermore it was shown that β-type learning automaton exhibits superior learning behavior compared to conventional learning automata through several experimental results. However, the β-type one is inferior from the viewpoints of memory usage and computational efficiency. So, the β-type learning automaton which consists of 2-state Bayesian estimators as minimum resources has been proposed in our previous work. In this study, we show the sufficient condition for conditional optimality of the β-type LA which consists of 2-state Bayesian estimators.

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© 2019 ISCIE Symposium on Stochastic Systems Theory and Its Applications
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