Abstract: Uncertainties place a key challenge in many sequential decision making problems for dynamical systems, such as energy systems, transportation, building operation, networking, etc. The uncertainties include both unknown system dynamics and volatile external disturbances. In this talk, I will present our recent progress in formally advancing the systematic design of real-time decision making in networked systems, focusing on the challenges raised by uncertainties. We firstly present our recently developed scalable multiagent reinforcement learning algorithms which only use local sensing and communication yet learn nearly-optimal localized policies for the global network. Then we present our online optimal control algorithms with time-varying cost functions and rigorously show how to use prediction effectively to reach a nearly-optimal online performance with fast computation. We will also discuss several extensions raised by real applications.

Bio: Na Li is a Gordon McKay professor in Electrical Engineering and Applied Mathematics at Harvard University. She received her Bachelor degree in Mathematics from Zhejiang University in 2007 and Ph.D. degree in Control and Dynamical systems from California Institute of Technology in 2013. She was a postdoctoral associate at Massachusetts Institute of Technology 2013-2014. Her research lies in control, learning, and optimization of networked systems, including theory development, algorithm design, and applications to real-world cyber-physical societal system. She received NSF career award (2016), AFSOR Young Investigator Award (2017), ONR Young Investigator Award(2019), Donald P. Eckman Award (2019), McDonald Mentoring Award (2020), along with some other awards.