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Nonpreemptive queueing breaks standard myopic priority rules because optimal decisions fundamentally depend on the remaining horizon, yet certainty-equivalence Bellman planning can still match the theoretical lower bound at $\widetilde{O}(\sqrt{d/T})$ regret.
Achieving a rate-optimal queue length regret of $\widetilde{\mathcal{O}}(T^{-1/2})$ reveals that strategic exploration can significantly enhance learning efficiency in contextual queueing environments.
