arXiv Machine Learning
Aug 28

Safety by Design: Realized-Cost Constraints for Contextual Bandits with Continuous Actions

The paper introduces a new approach to safety in contextual bandits with continuous actions, focusing on high‑probability constraints on the realized cost rather than expected cost. It presents the High‑Probability Constrained UCB algorithm, which balances reward exploration with conservative safety estimation, and provides theoretical regret guarantees for linear models and extensions to general function classes. Experiments demonstrate that this realized‑cost safety framework significantly reduces safety violations compared to expected‑cost constrained methods.

By Spyros Dragazis, Aldo Pacchiano