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• W3043040027 abstract "We propose a multi-agent variant of the classical multi-armed bandit problem, in which there are N agents and K arms, and pulling an arm generates a (possibly different) stochastic reward to each agent. Unlike the classical multi-armed bandit problem, the goal is not to learn the arm, as each agent may perceive a different arm as best for her. Instead, we seek to learn a fair distribution over arms. Drawing on a long line of research in economics and computer science, we use the Nash social welfare as our notion of fairness. We design multi-agent variants of three classic multi-armed bandit algorithms, and show that they achieve sublinear regret, now measured in terms of the Nash social welfare." @default.
• W3043040027 created "2020-07-23" @default.
• W3043040027 creator A5057226813 @default.
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• W3043040027 date "2020-07-13" @default.
• W3043040027 modified "2023-09-26" @default.
• W3043040027 title "Fair Algorithms for Multi-Agent Multi-Armed Bandits" @default.
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