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Published December 2009 | Published
Book Section - Chapter Open

Learning approaches to the Witsenhausen counterexample from a view of potential games

Abstract

Since Witsenhausen put forward his remarkable counterexample in 1968, there have been many attempts to develop efficient methods for solving this non-convex functional optimization problem. However there are few methods designed from game theoretic perspectives. In this paper, after discretizing the Witsenhausen counterexample and re-writing the formulation in analytical expressions, we use fading memory JSFP with inertia, one learning approach in games, to search for better controllers from a view of potential games. We achieve a better solution than the previously known best one. Moreover, we show that the learning approaches are simple and automated and they are easy to extend for solving general functional optimization problems.

Additional Information

© 2009 IEEE. This paper is a short version of Na Li's bachelor thesis work which was finished when she visited Prof. Jeff Shamma's lab at UCLA in 2007.

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