A theory for online control of dynamical systems

Abstract: We will discuss an emerging paradigm in differentiable reinforcement learning called “online nonstochastic control”. The new approach applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and robust control. Time permitting we will discuss recent extensions and insights into nonlinear control, iterative planning, and model free reinforcement learning.

No background is needed for this talk, and an introductory textbook draft appears here.

This theory was, and continues to be, developed here in Princeton with numerous collaborators, including Naman Agarwal, Brian Bullins, Karan Singh, Max Simchowitz, Xinyi Chen, Ani Majumdar, Sham Kakade, Edgar Minasyan, Paula Gradu, Jennifer Sun, and many others.

Prof. Elad Hazan - Bio

Prof. Elad Hazan

Elad Hazan is a professor of computer science at Princeton University and the co-founder and director of Google AI Princeton. His research focuses on the design and analysis of algorithms for basic problems in machine learning and optimization. Amongst his contributions are the co-invention of the AdaGrad algorithm for deep learning, and the first sublinear-time algorithms for convex optimization. He is the recipient of the Bell Labs prize, the IBM Goldberg best paper award twice, in 2012 and 2008, a European Research Council grant, a Marie Curie fellowship and twice the Google Research Award. He served on the steering committee of the Association for Computational Learning and has been program chair for COLT 2015.