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

• URL: http://arxiv.org/abs/2604.25972v1
• Date: Tue, 28 Apr 2026 07:50:24 GMT
• Title: A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication
• Abstract summary: In multi-agent reinforcement learning (MARL), the integration of a communication mechanism allows agents to better learn to coordinate their actions and converge on their objectives by sharing information.<n>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.<n>We propose a generalized GNN-based communication process with the goal of making the underlying concepts behind the methods more obvious and accessible.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Networked Agents in the Dark: Team Value Learning under Partial Observability [3.8779763612314633]
  We propose a novel cooperative multi-agent reinforcement learning (MARL) approach for networked agents.<n>In contrast to previous methods that rely on complete state information or joint observations, our agents must learn how to reach shared objectives under partial observability.<n>During training, they collect individual rewards and approximate a team value function through local communication, resulting in cooperative behavior.
  arXiv  Detail & Related papers  (2025-01-15T13:01:32Z)
• Communication Learning in Multi-Agent Systems from Graph Modeling Perspective [62.13508281188895]
  We introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We introduce a temporal gating mechanism for each agent, enabling dynamic decisions on whether to receive shared information at a given time.
  arXiv  Detail & Related papers  (2024-11-01T05:56:51Z)
• Learning Multi-Agent Communication from Graph Modeling Perspective [62.13508281188895]
  We introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. Our proposed approach, CommFormer, efficiently optimize the communication graph and concurrently refines architectural parameters through gradient descent in an end-to-end manner.
  arXiv  Detail & Related papers  (2024-05-14T12:40:25Z)
• Learning State-Augmented Policies for Information Routing in Communication Networks [84.76186111434818]
  We develop a novel State Augmentation (SA) strategy to maximize the aggregate information at source nodes using graph neural network (GNN) architectures.<n>We leverage an unsupervised learning procedure to convert the output of the GNN architecture to optimal information routing strategies.<n>In the experiments, we perform the evaluation on real-time network topologies to validate our algorithms.
  arXiv  Detail & Related papers  (2023-09-30T04:34:25Z)
• Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning [2.9904113489777826]
  This paper introduces a Multi-Agent Reinforcement Learning (MARL) approach for efficient information dissemination. We propose a Partially Observable Game (POSG) for information dissemination empowering each agent to decide on message forwarding independently. Our experimental results show that our trained policies outperform existing methods.
  arXiv  Detail & Related papers  (2023-08-25T21:30:16Z)
• Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations [51.8796674904734]
  We propose a new cooperative learning framework with pre-trained heterogeneous observation representations. We employ an encoder-decoder based graph attention to learn the intricate interactions and heterogeneous representations.
  arXiv  Detail & Related papers  (2020-12-24T04:52:29Z)
• GCN for HIN via Implicit Utilization of Attention and Meta-paths [104.24467864133942]
  Heterogeneous information network (HIN) embedding aims to map the structure and semantic information in a HIN to distributed representations. We propose a novel neural network method via implicitly utilizing attention and meta-paths. We first use the multi-layer graph convolutional network (GCN) framework, which performs a discriminative aggregation at each layer. We then give an effective relaxation and improvement via introducing a new propagation operation which can be separated from aggregation.
  arXiv  Detail & Related papers  (2020-07-06T11:09:40Z)
• Networked Multi-Agent Reinforcement Learning with Emergent Communication [18.47483427884452]
  Multi-Agent Reinforcement Learning (MARL) methods find optimal policies for agents that operate in the presence of other learning agents. One way to coordinate is by learning to communicate with each other. Can the agents develop a language while learning to perform a common task?
  arXiv  Detail & Related papers  (2020-04-06T16:13:23Z)
• Learning Multi-Agent Coordination through Connectivity-driven Communication [7.462336024223669]
  In artificial multi-agent systems, the ability to learn collaborative policies is predicated upon the agents' communication skills. We present a deep reinforcement learning approach, Connectivity Driven Communication (CDC) CDC is able to learn effective collaborative policies and can over-perform competing learning algorithms on cooperative navigation tasks.
  arXiv  Detail & Related papers  (2020-02-12T20:58:33Z)
• Learning Structured Communication for Multi-agent Reinforcement Learning [104.64584573546524]
  This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting. We propose a novel framework termed as Learning Structured Communication (LSC) by using a more flexible and efficient communication topology.
  arXiv  Detail & Related papers  (2020-02-11T07:19:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.