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içgörü - Federated reinforcement learning
Optimierung von Federated Reinforcement Learning in heterogenen MDPs durch konvergenzorientierte Stichprobennahme mit Screening
Die Autoren stellen ein neuartiges Aggregationsschema namens CAESAR vor, das konvergenzorientierte Stichprobennahme mit einem Screening-Mechanismus kombiniert, um die Lerneffizienz von Agenten in Federated Reinforcement Learning (FedRL) Szenarien mit heterogenen Markov-Entscheidungsprozessen (MDPs) zu verbessern.
Enhancing Federated Reinforcement Learning in Heterogeneous Environments through Convergence-Aware Sampling and Selective Screening
The core message of this paper is to introduce the Convergence-AwarE SAmpling with scReening (CAESAR) aggregation scheme, which enhances the learning of individual agents across varied Markov Decision Processes (MDPs) in federated reinforcement learning settings characterized by environmental heterogeneity.
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