Distributed learning in congested environments with partial information
T Boyarski, A Leshem, V Krishnamurthy - arXiv preprint arXiv:2103.15901, 2021 - arxiv.org
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 …
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 …
Distributed learning in congested environments with partial information
A Leshem, V Krishnamurthy, T Boyarski - Automatica, 2024 - Elsevier
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 …
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 …
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