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Decentralized stochastic control theory studies decisions of agents that are acting collectively based on their local information to optimize a common cost function under stochastic uncertainty. It will be a prominent avenue of research for many years to come as modern control systems are increasingly decentralized and interconnected. In this talk, we establish the existence of optimal policies for decentralized stochastic optimal control problems. We first consider the static case and show the existence of optimal policies under certain regularity conditions on the observation channels by introducing a topology on the set of policies. Then we consider sequential dynamic teams and establish the existence of an optimal policy via the static reduction method of Witsenhausen. We apply our findings to the well-known counterexample of Witsenhausen.

Biography: Naci Saldi received the B.Sc. and M.S. degrees in Electrical and Electronics Engineering from Bilkent University in 2008 and 2010, respectively and the Ph.D. degree in Department of Mathematics and Statistics from Queen’s University in 2015. He was a postdoctoral researcher at the University of Illinois at Urbana-Champaign before joining the Department of Natural and Mathematical Sciences at Özyeğin University as an Assistant Professor. He is a co-author of the book Finite Approximations in Discrete-Time Stochastic Control, published by Springer. His research interests include stochastic and decentralized control, source coding, mean-field games, reinforcement learning, and applied probability.