Discretized estimator learning automata

The improvements gained by rendering the various estimator learning algorithms discrete are investigated. This is done by restricting the probability of selecting an action to a finite discrete subset of (0, 1). This modification is proven to be epsilon -optimal in all stationary environments. Various discretized estimator algorithms (DEAs) are constructed. Subsequently, members of the family of DEAs are shown to be epsilon -optimal by deriving two sufficient conditions required for the epsilon -optimality-the properties of monotonicity and moderation. A conjecture about the necessity of these conditions for epsilon -optimality is presented. Experimental results indicate that the discrete modifications improve the performance of the algorithms so that the automata constitute fast-converging and accurate learning automata.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>