
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.


coremsg

enhancing-federated-reinforcement-learning-in-heterogeneous-environments-through-convergence-aware-sampling-and-selective-screening


Enhancing Federated Reinforcement Learning in Heterogeneous Environments through Convergence-Aware Sampling and Selective Screening
