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

Year: 2013

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1309.3745

Authors: Kulkarni, Ankur A., Coleman, Todd P.

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Defense Advanced Research Projects Agency

Awards: G1513559549, CIF: Medium: Collaborative Research: Toward a General Theory of Information Transfer via Timing, Emerging Frontiers of Science of Information

OpenAlex ID: W4300520191

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