An Optimizer's Approach to Stochastic Control Problems with Nonclassical Information Structures
Abstract
We present an optimization-based approach to stochastic control problems with nonclassical information structures. We cast these problems equivalently as optimization prob- lems on joint distributions. The resulting problems are necessarily nonconvex. Our approach to solving them is through convex relaxation. We solve the instance solved by Bansal and Basar with a particular application of this approach that uses the data processing inequality for constructing the convex relaxation. Using certain f-divergences, we obtain a new, larger set of inverse optimal cost functions for such problems. Insights are obtained on the relation between the structure of cost functions and of convex relaxations for inverse optimal control.
Cite
@article{arxiv.1309.3745,
title = {An Optimizer's Approach to Stochastic Control Problems with Nonclassical Information Structures},
author = {Ankur A. Kulkarni and Todd P. Coleman},
journal= {arXiv preprint arXiv:1309.3745},
year = {2013}
}
Comments
Under review with the IEEE Transactions on Automatic Control