An estimator algorithm for learning automata with changing number of actions

Thathachar, M. A. L. ; Harita, Bhaskar R. (1988) An estimator algorithm for learning automata with changing number of actions International Journal of General Systems, 14 (2). pp. 169-184. ISSN 0308-1079

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Official URL: http://www.tandfonline.com/doi/abs/10.1080/0308107...

Related URL: http://dx.doi.org/10.1080/03081078808935002

Abstract

In many problems of decision making under uncertainty the system has to acquire knowledge of its environment and learn the optimal decision through its experience. Such problems may also involve the system having to arrive at the globally optimal decision, when at each instant only a subset of the entire set of possible alternatives is available. These problems can be successfully modelled and analysed by learning automata. In this paper an estimator learning algorithm, which maintains estimates of the reward characteristics of the random environment, is presented for an automaton with changing number of actions. A learning automaton using the new scheme is shown to be e-optimal. The simulation results demonstrate the fast convergence properties of the new algorithm. The results of this study can be extended to the design of other types of estimator algorithms with good convergence properties.

Item Type:Article
Source:Copyright of this article belongs to Taylor and Francis Group.
ID Code:51370
Deposited On:28 Jul 2011 11:58
Last Modified:28 Jul 2011 11:58

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