Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms
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Abstract (arXiv)
We consider a stochastic multi-armed bandit problem with i.i.d. rewards where the expected reward function is multimodal with at most m modes. We propose the first known computationally tractable algorithm for computing the solution to the Graves-Lai optimization problem, which in turn enables the implementation of asymptotically optimal algorithms for this bandit problem. The code for the proposed algorithms is publicly available at https://github.com/wilrev/MultimodalBandits
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 29 Oct 2025.
Builds on
8 of 26 references.
References · 8
- 2015Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problemcited by 52
- 2017Minimal Exploration in Structured Stochastic Banditscited by 41
- 2021Learning Multimodal Rewards from Rankingscited by 41
- 2014Unimodal Bandits: Regret Lower Bounds and Optimal Algorithmscited by 37
- 2019Solving Bernoulli Rank-One Bandits with Unimodal Thompson Samplingcited by 23
- 2020Optimal Learning for Structured Banditscited by 14
- 2021Asymptotically Optimal Strategies For Combinatorial Semi-Bandits in Polynomial Timecited by 4
- 2021Indexed Minimum Empirical Divergence for Unimodal Banditscited by 4
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Citing papers per year: 2026: 1.
1 paper.