Survey maps graph neural networks in multi-agent reinforcement learning

This paper surveys recent advancements in multi-agent reinforcement learning (MARL) that utilize graph neural networks (GNNs) for agent communication. It highlights how GNNs, when applied to interaction graphs, enable agents to share information and improve coordination towards common objectives. The authors aim to provide a structured classification of these GNN-based communication methods in MARL, making the underlying concepts more accessible. AI

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IMPACT Provides a structured overview of GNN-based communication in MARL, potentially guiding future research and development in agent coordination.

RANK_REASON This is a survey paper on a specific area of AI research.

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• arXiv
• Valentin Cuzin-Rambaud
• Graph Neural Network
• Multi-Agent Reinforcement Learning

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