Multi agent learning improves communication under tight data limits
Robust and Efficient Communication for Multi-Agent Learning
Machine LearningArtificial IntelligenceMultiagent Systems
Summary
Communicating clearly is important when multiple intelligent agents work together, but it’s hard to send messages that are both useful and reliable when there isn’t much data to share or the network is noisy. The authors created a new approach called MARC that helps agents learn to send messages that reduce confusion about what will happen next. Their method uses attention mechanisms and special rules to make each message very informative and resilient. They showed that MARC works better than other methods when communication is limited or lossy, which is useful for robots and systems that operate under strict data constraints.
What this means in practice
- •For robotic systems engineers: Design autonomous robots that maintain coordination despite limited and unreliable communication links using MARC’s robust messaging strategy.
- •For internet of things developers: Implement efficient communication protocols in decentralized IoT networks to improve coordination under bandwidth and reliability constraints.
Authors
Rafael Pina, Varuna De Silva, Corentin Artaud
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.