Probabilistic Control Design using an Information Geometric Framework

This paper is motivated by the problem of controlling a single system using a large number of independent simple systems that act together with the same goal, but without synchronization or communication between them. The technique used here follows the probabilistic control formulation developed by Kárný. Iterative algorithms are developed, using an information geometric viewpoint, to perform the optimization over discrete probability distributions. The probability mass functions are parametrized either as mixtures or exponential distributions, for which a natural gradient method is applied.