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- W3185115592 abstract "This paper studies a distributed multi-armed bandit problem over a network of N agents whose neighbor relationships are described by a directed graph G. Each agent makes a sequence of decisions on selecting an arm from M candidates, yet it only receives a sample of the reward for each decision, which is an unknown random variable. A distributed upper confidence bound (UCB) algorithm is proposed for the agents to cooperatively learn the best decision. It is shown that when all the agents share a homogeneous distribution of each arm reward and G is strongly connected, the algorithm achieves a guaranteed logarithmic regret for all N agents. A sufficient condition under which the proposed distributed algorithm learns faster than the classical single-agent UCB1 is also provided." @default.
- W3185115592 created "2021-08-02" @default.
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- W3185115592 date "2021-05-25" @default.
- W3185115592 modified "2023-10-14" @default.
- W3185115592 title "A Distributed Algorithm for Multi-Armed Bandit with Homogeneous Rewards over Directed Graphs" @default.
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- W3185115592 doi "https://doi.org/10.23919/acc50511.2021.9483267" @default.
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