Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-agent Multi-armed Bandits

• 成果类型: Article
• 署名作者: Zhu, Jingxuan; Mulle, Ethan; Smith, Christopher S.; Koppel, Alec; Liu, Ji
• 署名单位: State University of New York (SUNY) System; Stony Brook University; University of California System; University of California Santa Cruz; State University of New York (SUNY) System; Stony Brook University; State University of New York (SUNY) System; Stony Brook University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3525417
• 发表日期: 2025
• 页码: 4360-4375
• 关键词: decision making; Protocols; estimation; Program processors; privacy; Periodic structures; Knowledge engineering; faces; Directed graphs; collaboration; Cooperative control; Machine Learning; multiagent systems

摘要

This article studies a decentralized homogeneous multiarmed bandit problem in a multiagent network. The problem is simultaneously solved by N agents assuming that they face a common set of M arms and share the same arms' reward distributions. Each agent can receive information only from its neighbors, where the neighbor relationships among the agents are described by a fixed graph. Two fully decentralized upper confidence bound (UCB) algorithms are proposed for undirected graphs, respectively, based on the classic UCB1 algorithm and the state-of-the-art Kullback-Leibler upper confidence bound (KL-UCB) algorithm. The proposed decentralized UCB1 and KL-UCB algorithms permit each agent in the network to achieve a better logarithmic asymptotic regret than their single-agent counterparts, provided that the agent has at least one neighbor, and the more neighbors an agent has, the better regret it will have, meaning that the sum is more than its component parts. The same algorithm design framework is also extended to directed graphs through the design of a variant of the decentralized UCB1 algorithm, which outperforms the single-agent UCB1 algorithm.