Toward Optimal Second-Order Path-Length Guarantee for Adversarial Multi-Armed Bandits

Abstract:We study second-order path-length regret in adversarial $K$-armed bandits against oblivious loss sequences. Bubeck et al. [2019] designed an algorithm that achieves $\widetilde{\mathcal{O}}(K+\sqrt{KQ_{\infty,1}})$ regret, where $Q_{\infty,1}$ is the first-order path length, and left open whether $\widetilde{\mathcal{O}}(\text{poly}(K)\sqrt{1+Q_{\infty,2}})$ regret is achievable under bandit feedback, where $Q_{\infty,2}$ is the second-order path length. Somewhat surprisingly, we resolve this question positively by showing that with a more involved analysis, the exact same algorithm of Bubeck et al. [2019] achieves $\mathcal{O}\left(K\log(KT)+\sqrt{K\log(KT)\bigl(1+Q_{\infty,2}\bigr)}\right)$ expected regret when $Q_{\infty,2}$ is known, where $T$ is the horizon. This matches the $\Omega(\sqrt{KQ_{\infty,2}})$ lower bound up to logarithmic factors and additive terms. We further remove the knowledge of $Q_{\infty,2}$ using an adaptive restart scheme whose path-length estimator has uniformly bounded increments.

Machine Learning (cs.LG)

arXiv:2608.15996 [cs.LG]

(or arXiv:2608.15996v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.15996 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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