Non-Stochastic Control with Bandit Feedback
Machine Learning
2020-08-14 v1 Optimization and Control
Machine Learning
Abstract
We study the problem of controlling a linear dynamical system with adversarial perturbations where the only feedback available to the controller is the scalar loss, and the loss function itself is unknown. For this problem, with either a known or unknown system, we give an efficient sublinear regret algorithm. The main algorithmic difficulty is the dependence of the loss on past controls. To overcome this issue, we propose an efficient algorithm for the general setting of bandit convex optimization for loss functions with memory, which may be of independent interest.
Cite
@article{arxiv.2008.05523,
title = {Non-Stochastic Control with Bandit Feedback},
author = {Paula Gradu and John Hallman and Elad Hazan},
journal= {arXiv preprint arXiv:2008.05523},
year = {2020}
}