Greedy Algorithm for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure

We study the greedy (exploitation-only) algorithm in bandit problems with a known reward structure. We allow arbitrary finite reward structures, while prior work focused on a few specific ones. We fully characterize when the greedy algorithm asymptotically succeeds or fails, in the sense of sublinear vs. linear regret as a function of time. Our characterization identifies a partial identifiability property of the problem instance as the necessary and sufficient condition for the asymptotic success. Notably, once this property holds, the problem becomes easy -- any algorithm will succeed (in the same sense as above), provided it satisfies a mild non-degeneracy condition. We further extend our characterization to contextual bandits and interactive decision-making with arbitrary feedback, and demonstrate its broad applicability across various examples.

@article{slivkins2025_2503.04010,
  title={ Greedy Algorithm for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure },
  author={ Aleksandrs Slivkins and Yunzong Xu and Shiliang Zuo },
  journal={arXiv preprint arXiv:2503.04010},
  year={ 2025 }
}