Control Meets Learning Seminar

Monitoring and control for complex network systems are accelerated by the recent revolutions in sensing, computation, communication, and actuation technologies that boost the development and implementation of data-driven decision making. In this talk, we will focus on real-time distributed decision-making algorithms for networked systems. The first part will be on scalable multiagent reinforcement learning algorithms and the second part will be on the model free control methods for power systems based on continuous time zeroth order optimization methods. We will show that exploiting network structure or underlying physical dynamics will facilitate the design of scalable real-time learning and control methods.