Absorbing stochastic estimator learning automata for S-model stationary environments

Authors:
G. I. Papadimitriou;A. S. Pomportsis;S. Kiritsi;E. Talahoupi
Affiliations:
Department of Informaties, Aristotle University, Box 888, 54006 Thessaloniki, Greece;Department of Informaties, Aristotle University, Box 888, 54006 Thessaloniki, Greece;Department of Informaties, Aristotle University, Box 888, 54006 Thessaloniki, Greece;Department of Informaties, Aristotle University, Box 888, 54006 Thessaloniki, Greece
Venue:
Information Sciences—Informatics and Computer Science: An International Journal
Year:
2002

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Abstract

An S-model absorbing learning automaton (LA) which is based on the use of a stochastic estimator is introduced. According to the proposed stochastic estimator scheme, the estimates of the mean rewards of actions are computed stochastically. Actions that have not been selected many times have the opportunity to be estimated as optimal, to increase their choice probabilities, and consequently, to be selected. In this way, the automaton's accuracy and speed of convergence are significantly improved.