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arXiv · 1309.3745

An Optimizer's Approach to Stochastic Control Problems with Nonclassical Information Structures

Ankur A. Kulkarni·Todd P. Coleman
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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.

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Ankur A. Kulkarni, Todd P. Coleman. 2013-09-15. An Optimizer's Approach to Stochastic Control Problems with Nonclassical Information Structures. https://arxiv.org/abs/1309.3745

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