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On ε-optimality of the pursuit learning algorithm

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journal contribution
posted on 22.11.2013 by Ryan Martin, Omkar Tilak
Estimator algorithms in learning automata are useful tools for adaptive, real- time optimization in computer science and engineering applications. This pa- per investigates theoretical convergence properties for a special case of estimator algorithms|the pursuit learning algorithm. In this note, we identify and ll a gap in existing proofs of probabilistic convergence for pursuit learning. It is tradition to take the pursuit learning tuning parameter to be xed in practical applications, but our proof sheds light on the importance of a vanishing sequence of tuning parameters in a theoretical convergence analysis.

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Publisher Statement

This is a copy of an article published in the Journal of Applied Probability © 2012 Applied Probability Trust

Publisher

Applied Probability Trust

Language

en_US

issn

0021-9002

Issue date

01/09/2012

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