A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication

2026-04-28

AI Summary

This work addresses the absence of a systematic taxonomy and unified framework for communication mechanisms in graph neural network (GNN)-driven multi-agent reinforcement learning. To bridge this gap, the paper proposes a general GNN-based communication pipeline and establishes the first structured survey and classification scheme, clearly delineating the core mechanisms and design principles underlying such approaches. By integrating insights from GNNs, multi-agent reinforcement learning, and communication modeling, this study enhances conceptual clarity and accessibility in the field, while also laying a theoretical foundation and offering methodological guidance for future research.

Abstract

In multi-agent reinforcement learning (MARL), the integration of a communication mechanism, allowing agents to better learn to coordinate their actions and converge on their objectives by sharing information. Based on an interaction graph, a subclass of methods employs graph neural networks (GNNs) to learn the communication, enabling agents to improve their internal representations by enriching them with information exchanged. With growing research, we note a lack of explicit structure and framework to distinguish and classify MARL approaches with communication based on GNNs. Thus, this paper surveys recent works in this field. We propose a generalized GNN-based communication process with the goal of making the underlying concepts behind the methods more obvious and accessible.

Problem

• multi-agent reinforcement learning
• graph neural networks
• communication
• survey
• classification framework

Innovation

• Graph Neural Networks
• Multi-Agent Reinforcement Learning
• Communication Mechanism
• Interaction Graph
• Survey