Asymptotic behavior of a hierarchical system of learning automata

Thathachar, M. A. L. ; Ramachandran, K. M. (1985) Asymptotic behavior of a hierarchical system of learning automata Information Sciences, 35 (2). pp. 91-110. ISSN 0020-0255

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Official URL: http://www.sciencedirect.com/science/article/pii/0...

Related URL: http://dx.doi.org/10.1016/0020-0255(85)90043-X

Abstract

Learning automata arranged in a two-level hierarchy are considered. The automata operate in a stationary random environment and update their action probabilities according to the linear-reward-ε-penalty algorithm at each level. Unlike some hierarchical systems previously proposed, no information transfer exists from one level to another, and yet the hierarchy possesses good convergence properties. Using weak-convergence concepts it is shown that for large time and small values of parameters in the algorithm, the evolution of the optimal path probability can be represented by a diffusion whose parameters can be computed explicitly.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
ID Code:51351
Deposited On:28 Jul 2011 11:58
Last Modified:28 Jul 2011 11:58

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