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

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Title An Optimizer's Approach to Stochastic Control Problems With Nonclassical Information Structures
 
Creator KULKARNI, AA
COLEMAN, TP
 
Subject SYSTEMS
DESIGN
TEAMS
Optimal control
stochastic systems
optimization
information theory
decentralized control
networked control systems
 
Description We present a general optimization-based framework for stochastic control problems with nonclassical information structures. We cast these problems equivalently as optimization problems 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 ("Stochastic teams with nonclassical information revisited: When is an affine law optimal?", IEEE Trans. Automatic Control, 1987) 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.
 
Publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
 
Date 2016-01-15T09:06:36Z
2016-01-15T09:06:36Z
2015
 
Type Article
 
Identifier IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 60(4)937-949
0018-9286
1558-2523
http://dx.doi.org/10.1109/TAC.2014.2362596
http://dspace.library.iitb.ac.in/jspui/handle/100/18233
 
Language en