Robust and Efficient Communication for Multi-Agent Learning

2026-09-14

AI Summary

本文通过引入基于信息论的MARC框架，解决多智能体学习中通信既需信息量大又需鲁棒的问题，该方法在资源受限环境下表现优异。

Abstract

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.

Problem

• Multi-Agent Reinforcement Learning
• communication
• physical constraints
• informative messages
• robustness

Innovation

• Multi-Agent Regularized Communication (MARC)
• conditional mutual information
• attention-based architecture
• message regularization
• communication bottlenecks