Non-stationary Bandits with Heavy Tail

Author:

Pan Weici1,Liu Zhenhua1

Affiliation:

1. Stony Brook University

Abstract

In this study, we investigate the performance of multi-armed bandit algorithms in environments characterized by heavytailed and non-stationary reward distributions, a setting that deviates from the conventional risk-neutral and sub- Gaussian assumptions.

Publisher

Association for Computing Machinery (ACM)

Reference13 articles.

1. R. Agrawal. Sample mean based index policies by o (log n) regret for the multi-armed bandit problem. Advances in applied probability, 27(4):1054--1078, 1995.

2. S. Bhatt, G. Fang, and P. Li. Piecewise stationary bandits under risk criteria. In International Conference on Artificial Intelligence and Statistics, pages 4313--4335. PMLR, 2023.

3. Bandits With Heavy Tail

4. A. Garivier and E. Moulines. On upper-confidence bound policies for non-stationary bandit problems. arXiv preprint arXiv:0805.3415, 2008.

5. Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration

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