Thathachar, MAL and Harit, Bhaskar R (1988) An Estimator Algorithm For Learning Automata With Changing Number Of Actions. In: International Journal of General Systems, 14 (2). pp. 169-184.Full text not available from this repository.
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:||Journal Article|
|Additional Information:||Copyright of this article belongs to Taylor and Francis Group.|
|Keywords:||Decision making under uncertainty;learning automata;random environments;estimator learning algorithms;variable action set;convergence of learning algorithms;ε-optimality.|
|Department/Centre:||Division of Electrical Sciences > Electrical Engineering|
|Date Deposited:||01 Sep 2010 04:24|
|Last Modified:||01 Sep 2010 04:24|
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