综述图神经网络在多智能体强化学习通信中的应用
A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication
- 用图神经网络构建智能体间通信机制，提升协作能力
- 提出通用通信流程框架，统一现有方法的表达方式
- 适合研究多智能体系统、图神经网络的学者参考
在多智能体强化学习（MARL）中，通信机制使智能体通过信息共享更好地协调行动并达成目标。基于交互图的若干方法利用图神经网络（GNN）学习通信过程，使智能体通过交换信息来增强自身内部表示。随着研究深入，我们发现缺乏明确结构与框架来区分和分类基于GNN的通信方法。为此，本文系统综述该领域近期工作，提出一个通用的基于GNN的通信流程，旨在使各类方法的核心思想更清晰、易理解。
原文摘要 · Abstract (English)
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.
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