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arXiv · 1904.06309

Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication

Yuanhao Wang·Jiachen Hu·Xiaoyu Chen·Liwei Wang
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Abstract

We study the problem of regret minimization for distributed bandits learning, in which $M$ agents work collaboratively to minimize their total regret under the coordination of a central server. Our goal is to design communication protocols with near-optimal regret and little communication cost, which is measured by the total amount of transmitted data. For distributed multi-armed bandits, we propose a protocol with near-optimal regret and only $O(M\log(MK))$ communication cost, where $K$ is the number of arms. The communication cost is independent of the time horizon $T$, has only logarithmic dependence on the number of arms, and matches the lower bound except for a logarithmic factor. For distributed $d$-dimensional linear bandits, we propose a protocol that achieves near-optimal regret and has communication cost of order $\tilde{O}(Md)$, which has only logarithmic dependence on $T$.

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Yuanhao Wang, Jiachen Hu, Xiaoyu Chen, Liwei Wang. 2019-04-12. Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication. https://arxiv.org/abs/1904.06309

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