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

February 29, 2020 ยท Declared Dead ยท ๐Ÿ› International Conference on Artificial Intelligence and Statistics

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Authors Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang arXiv ID 2003.00162 Category cs.LG: Machine Learning Cross-listed cs.IT, stat.ML Citations 40 Venue International Conference on Artificial Intelligence and Statistics Last Checked 3 months ago
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.
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