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Fooling Algorithms in Non-Stationary Bandits using Belief Inertia
We study worst-case dynamic regret of specific multi-armed bandit algorithms on piecewise-stationary instances with at most one breakpoint. Our constructions exploit belief inertia: observations collected before a change can make an algorithm slow to revise the empirical ordering on which its decisi
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