Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908619749982208 |
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| author | Réveillard, William Combes, Richard |
| author_facet | Réveillard, William Combes, Richard |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25811 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms Réveillard, William Combes, Richard Machine Learning Statistics Theory 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 |
| title | Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2510.25811 |