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

Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1519-1528
Number of pages10
JournalProceedings of Machine Learning Research
Volume108
StatePublished - 2020
Event23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020 - Virtual, Online
Duration: Aug 26 2020Aug 28 2020

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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