多智能体学习的鲁棒高效通信
Robust and Efficient Communication for Multi-Agent Learning
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- Institute for Digital Technologies（数字技术研究所）
- Loughborough University London（拉夫堡大学伦敦校区）
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中文总结 AI 辅助
本文提出多智能体正则化通信（MARC）框架，利用条件互信息和注意力机制生成鲁棒消息，在通信瓶颈下显著优于现有方法，并支持数据压缩。
中文摘要 AI 辅助
有效通信是多智能体强化学习（MARL）中分布式智能的基石，然而确保生成的消息既信息丰富又对物理约束具有鲁棒性仍然是一个重大挑战。本文提出了多智能体正则化通信（MARC），这是一种受条件互信息信息论原理启发的新型框架。MARC采用基于注意力的架构，并结合一种独特的消息正则化机制，旨在最小化未来系统状态的不确定性，从而诱导学习高度代表性的通信协议。关键的是，我们在严格的通信瓶颈和有损信道下评估MARC，模拟自主机器人网络和去中心化系统的现实世界约束。我们的结果表明，MARC在复杂协作领域中显著优于最先进的方法。此外，我们对消息特征进行了深入分析，证明MARC即使在显著数据压缩下也能保持高操作性能，为在资源受限环境中部署智能体提供了一条可扩展的路径。
英文摘要
Effective communication is a cornerstone of distributed intelligence in Multi-Agent Reinforcement Learning (MARL), yet ensuring that generated messages are both informative and robust to physical constraints remains a significant challenge. This paper introduces Multi-Agent Regularized Communication (MARC), a novel framework inspired by information-theoretic principles of conditional mutual information. MARC employs an attention-based architecture coupled with a unique message regularization mechanism designed to minimize uncertainty regarding future system states, thereby inducing the learning of highly representative communication protocols. Crucially, we evaluate MARC under stringent communication bottlenecks and lossy channels, simulating the real-world constraints of autonomous robotic networks and decentralized systems. Our results demonstrate that MARC significantly outperforms state-of-the-art methods in complex cooperative domains. Furthermore, we provide a deep analysis of message characteristics, proving that MARC maintains high operational performance even under significant data compression, offering a scalable path for deploying intelligent agents in resource-constrained environments.
