Adaptive Online Non-stochastic Control with Disturbance Action Controllers

This paper proposes an adaptive online non-stochastic control algorithm (AdaFTRL-C) that achieves sublinear policy regret bounds that adapt to the difficulty of the controlled environment, as measured by the gradients of the observed cost functions.