|Seminar on Theoretical Machine Learning|
|Topic:||Online Control with Adversarial Disturbances|
|Affiliation:||Online Control with Adversarial Disturbances|
|Date:||Monday, February 11|
|Time/Room:||12:15pm - 1:45pm/White Levy Room|
We study the control of a linear dynamical system with adversarial disturbances (as opposed to statistical noise). The objective we consider is one of regret: we desire an online control procedure that can do nearly as well as that of a procedure that has full knowledge of the disturbances in hindsight. Our main result is an efficient algorithm that provides nearly tight regret bounds for this problem. From a technical standpoint, this work generalizes upon previous work in that our model allows for adversarial noise in the dynamics and allows for general convex costs.