A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917444775313408 |
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| author | Cuzin-Rambaud, Valentin Matignon, Laetitia Morge, Maxime |
| author_facet | Cuzin-Rambaud, Valentin Matignon, Laetitia Morge, Maxime |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25972 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication Cuzin-Rambaud, Valentin Matignon, Laetitia Morge, Maxime Machine Learning Artificial Intelligence Multiagent Systems 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. |
| title | A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication |
| topic | Machine Learning Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2604.25972 |