Truly Adapting to Adversarial Constraints in Constrained MABs

arXiv:2602.14543v1 Announce Type: cross
Abstract: We study the constrained variant of the emph{multi-armed bandit} (MAB) problem, in which the learner aims not only at minimizing the total loss incurred during the learning dynamic, but also at controlling the violation of multiple emph{unknown} constraints, under both emph{full} and emph{bandit feedback}. We consider a non-stationary environment that subsumes both stochastic and adversarial models and where, at each round, both losses and constraints are drawn from distributions that may change arbitrarily over time. In such a setting, it is provably not possible to guarantee both sublinear regret and sublinear violation. Accordingly, prior work has mainly focused either on settings with stochastic constraints or on relaxing the benchmark with fully adversarial constraints (emph{e.g.}, via competitive ratios with respect to the optimum). We provide the first algorithms that achieve optimal rates of regret and emph{positive} constraint violation when the constraints are stochastic while the losses may vary arbitrarily, and that simultaneously yield guarantees that degrade smoothly with the degree of adversariality of the constraints. Specifically, under emph{full feedback} we propose an algorithm attaining $widetilde{mathcal{O}}(sqrt{T}+C)$ regret and $widetilde{mathcal{O}}(sqrt{T}+C)$ {positive} violation, where $C$ quantifies the amount of non-stationarity in the constraints. We then show how to extend these guarantees when only bandit feedback is available for the losses. Finally, when emph{bandit feedback} is available for the constraints, we design an algorithm achieving $widetilde{mathcal{O}}(sqrt{T}+C)$ {positive} violation and $widetilde{mathcal{O}}(sqrt{T}+Csqrt{T})$ regret.