A new approach for the design of reinforcement schemes for learning automata: stochastic estimator learning algorithms

Abstract

A new approach to the design of S-model ergodic learning automata is introduced. The new scheme uses a stochastic estimator and is able to operate in nonstationary environments with high accuracy and high adaptation rate. The estimator is always recently updated and, consequently, is able to be adapted to environmental changes. The performance of the stochastic estimator learning automation (SELA) is superior to that of the previous well-known S-model ergodic schemes. Furthermore, it is proved that SELA is absolutely expedient in every stationary S-model random environment.<>