English

Decentralized Multi-player Multi-armed Bandits with No Collision Information

Machine Learning 2020-03-03 v1 Information Theory math.IT Machine Learning

Abstract

The decentralized stochastic multi-player multi-armed bandit (MP-MAB) problem, where the collision information is not available to the players, is studied in this paper. Building on the seminal work of Boursier and Perchet (2019), we propose error correction synchronization involving communication (EC-SIC), whose regret is shown to approach that of the centralized stochastic MP-MAB with collision information. By recognizing that the communication phase without collision information corresponds to the Z-channel model in information theory, the proposed EC-SIC algorithm applies optimal error correction coding for the communication of reward statistics. A fixed message length, as opposed to the logarithmically growing one in Boursier and Perchet (2019), also plays a crucial role in controlling the communication loss. Experiments with practical Z-channel codes, such as repetition code, flip code and modified Hamming code, demonstrate the superiority of EC-SIC in both synthetic and real-world datasets.

Cite

@article{arxiv.2003.00162,
  title  = {Decentralized Multi-player Multi-armed Bandits with No Collision Information},
  author = {Chengshuai Shi and Wei Xiong and Cong Shen and Jing Yang},
  journal= {arXiv preprint arXiv:2003.00162},
  year   = {2020}
}

Comments

17 pages, 11 figures. Accepted to AISTATS 2020

R2 v1 2026-06-23T13:58:31.262Z