FedGuide: Diffusion Prior Alignment and Value Baseline Guidance for Heterogeneous Federated Reinforcement Learning

תקציר מקורי באנגליתarXiv:2609.18964v2 Announce Type: replace Abstract: Federated Reinforcement Learning (FRL) enables collaborative policy learning across distributed agents with heterogeneous environments. While recent methods based on variance reduction, divergence penalization, and momentum optimization improve FRL under heterogeneous settings, they still primarily synchronize policy or value-network parameters and do not explicitly address distributional mismatch among heterogeneous clients. Therefore, we propose \textbf{FedGuide}, a FRL framework that uses diffusion priors as behavior models to provide personalized data supported distributions for heterogeneous local policy learning. Instead of directly averaging local policies, FedGuide aggregates those diffusion priors through Optimal-Transport Mixtur