Identifying the Best Arm in the Presence of Global Environment Shifts
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913476875649024 |
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| author | Srisawad, Phurinut Branke, Juergen Tran-Thanh, Long |
| author_facet | Srisawad, Phurinut Branke, Juergen Tran-Thanh, Long |
| contents | This paper formulates a new Best-Arm Identification problem in the non-stationary stochastic bandits setting, where the means of all arms are shifted in the same way due to a global influence of the environment. The aim is to identify the unique best arm across environmental change given a fixed total budget. While this setting can be regarded as a special case of Adversarial Bandits or Corrupted Bandits, we demonstrate that existing solutions tailored to those settings do not fully utilise the nature of this global influence, and thus, do not work well in practice (despite their theoretical guarantees). To overcome this issue, in this paper we develop a novel selection policy that is consistent and robust in dealing with global environmental shifts. We then propose an allocation policy, LinLUCB, which exploits information about global shifts across all arms in each environment. Empirical tests depict a significant improvement in our policies against other existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12581 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identifying the Best Arm in the Presence of Global Environment Shifts Srisawad, Phurinut Branke, Juergen Tran-Thanh, Long Machine Learning Artificial Intelligence This paper formulates a new Best-Arm Identification problem in the non-stationary stochastic bandits setting, where the means of all arms are shifted in the same way due to a global influence of the environment. The aim is to identify the unique best arm across environmental change given a fixed total budget. While this setting can be regarded as a special case of Adversarial Bandits or Corrupted Bandits, we demonstrate that existing solutions tailored to those settings do not fully utilise the nature of this global influence, and thus, do not work well in practice (despite their theoretical guarantees). To overcome this issue, in this paper we develop a novel selection policy that is consistent and robust in dealing with global environmental shifts. We then propose an allocation policy, LinLUCB, which exploits information about global shifts across all arms in each environment. Empirical tests depict a significant improvement in our policies against other existing methods. |
| title | Identifying the Best Arm in the Presence of Global Environment Shifts |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.12581 |