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arXiv · 2012.01789

Distributed Thompson Sampling

Jing Dong·Tan Li·Shaolei Ren·Linqi Song
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Abstract

We study a cooperative multi-agent multi-armed bandits with M agents and K arms. The goal of the agents is to minimized the cumulative regret. We adapt a traditional Thompson Sampling algoirthm under the distributed setting. However, with agent's ability to communicate, we note that communication may further reduce the upper bound of the regret for a distributed Thompson Sampling approach. To further improve the performance of distributed Thompson Sampling, we propose a distributed Elimination based Thompson Sampling algorithm that allow the agents to learn collaboratively. We analyse the algorithm under Bernoulli reward and derived a problem dependent upper bound on the cumulative regret.

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Jing Dong, Tan Li, Shaolei Ren, Linqi Song. 2020-12-03. Distributed Thompson Sampling. https://arxiv.org/abs/2012.01789

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