Non-Stochastic Control with Bandit Feedback

Part of Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020)

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Paula Gradu, John Hallman, Elad Hazan


<p>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.</p>