Reinforcement Learning and Kullback-Leibler Stochastic Optimal Control for Complex Networks

Principal Investigator: Sean Meyn

Sponsor: NSF

Start Date: September 15, 2019

End Date: August 31, 2022

Amount: $380,000

Abstract

Natural and man-made networked systems are all around us. The power grid and the Internet are two examples of apparently complex interconnected systems, in which millions of “agents” are eager to extract value in the form of energy or bandwidth. While these systems are complex when measured in graph-theoretic terms, the behavior of communication and energy systems appears simple and highly predictable to the end users (in most of the world). This success is due in part to distributed control loops that manage system-wide supply-demand balance. An example of distributed control in the Internet is TCP/IP, and automatic generation control (AGC) in most electric power grids. While distributed control protocols are highly developed and widely accepted in communication applications, this is less true in other networked systems such as electric power and natural gas distribution.

This project aims to advance control theory for complex interconnected systems. The application focus is on power systems, but the control techniques are general and are likely to have far broader impact.

More Information: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935389&HistoricalAwards=false