Distributed learning in congested environments with partial information,Automatica

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Distributed learning in congested environments with partial information
Automatica ( IF 4.8 ) Pub Date : 2024-08-08 , DOI: 10.1016/j.automatica.2024.111817
Amir Leshem , Vikram Krishnamurthy , Tomer Boyarski

How can non-communicating agents learn to share congested resources efficiently? This is a challenging task when the agents can access the same resource simultaneously (in contrast to multi-agent multi-armed bandit problems) and the resource valuations differ among agents. We present a fully distributed algorithm for learning to share in congested environments and prove that the agents’ regret with respect to the optimal allocation is poly-logarithmic in the time horizon. Performance in the non-asymptotic regime is illustrated in numerical simulations. The distributed algorithm has applications in cloud computing and spectrum sharing.

更新日期：2024-08-08

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