Online Control with Adversarial Disturbances
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:111-119, 2019.
We study the control of linear dynamical systems with adversarial disturbances, as opposed to statistical noise. We present an efficient algorithm that achieves nearly-tight regret bounds in this setting. Our result generalizes upon previous work in two main aspects: the algorithm can accommodate adversarial noise in the dynamics, and can handle general convex costs.