AI RESEARCH
Bayesian Ego-graph Inference for Networked Multi-Agent Reinforcement Learning
arXiv CS.LG
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ArXi:2509.16606v5 Announce Type: replace-cross In networked multi-agent reinforcement learning (Networked-MARL), decentralized agents must act under local observability and constrained communication over fixed physical graphs. Existing methods often assume static neighborhoods, limiting adaptability to dynamic or heterogeneous environments. While centralized frameworks can learn dynamic graphs, their reliance on global state access and centralized infrastructure is impractical in real-world decentralized systems.