Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information

• URL: http://arxiv.org/abs/2103.13059v1
• Date: Wed, 24 Mar 2021 10:14:16 GMT
• Title: Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information
• Authors: Wei Huang and Richard Combes and Cindy Trinh
• Abstract summary: We propose a novel algorithm for multi-player multi-armed bandits without collision sensing information. Our algorithm circumvents two problems shared by all state-of-the-art algorithms. It does not need as an input a lower bound on the minimal expected reward of an arm, and its performance does not scale inversely proportionally to the minimal expected reward.
• Score: 9.467920768170515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel algorithm for multi-player multi-armed bandits without collision sensing information. Our algorithm circumvents two problems shared by all state-of-the-art algorithms: it does not need as an input a lower bound on the minimal expected reward of an arm, and its performance does not scale inversely proportionally to the minimal expected reward. We prove a theoretical regret upper bound to justify these claims. We complement our theoretical results with numerical experiments, showing that the proposed algorithm outperforms state-of-the-art in practice as well.

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