Feb. 6, 2024, 5:47 a.m. | Amir Leshem

cs.LG updates on arXiv.org arxiv.org

In this paper, we study the problem of fair multi-agent multi-arm bandit learning when agents do not communicate with each other, except collision information, provided to agents accessing the same arm simultaneously. We provide an algorithm with regret $O\left(N^3 \log \frac{B}{\Delta} f(\log T) \log T \right)$ (assuming bounded rewards, with unknown bound), where $f(t)$ is any function diverging to infinity with $t$. This significantly improves previous results which had the same upper bound on the regret of order $O(f(\log T) …

agent agents algorithm arm collision cs.cy cs.dc cs.lg delta fair function information multi-agent paper study

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