Bandwidth-Efficient Multi-Agent Communication through Information Bottleneck and Vector Quantization

2026-02-02

AI Summary

This work addresses the challenge of limited communication bandwidth in real-world multi-agent reinforcement learning systems, which often hinders collaborative efficiency. The authors propose a novel communication compression method that integrates information bottleneck theory with vector quantization to discretely encode messages under information-theoretic principles. A dynamic gating mechanism is further introduced to adaptively determine when agents should communicate, enabling selective and efficient information exchange. This approach represents the first integration of the information bottleneck principle with vector quantization for multi-agent communication. Experimental results demonstrate that the method reduces bandwidth usage by 41.4% while improving task performance by 181.8% over a no-communication baseline. Moreover, it achieves superior performance on the success rate–bandwidth Pareto frontier, with an area under the curve (AUC) of 0.198 compared to 0.142 for existing methods.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Information Theory
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Multi-agent reinforcement learning systems deployed in real-world robotics applications face severe communication constraints that significantly impact coordination effectiveness. We present a framework that combines information bottleneck theory with vector quantization to enable selective, bandwidth-efficient communication in multi-agent environments. Our approach learns to compress and discretize communication messages while preserving task-critical information through principled information-theoretic optimization. We introduce a gated communication mechanism that dynamically determines when communication is necessary based on environmental context and agent states. Experimental evaluation on challenging coordination tasks demonstrates that our method achieves 181.8% performance improvement over no-communication baselines while reducing bandwidth usage by 41.4%. Comprehensive Pareto frontier analysis shows dominance across the entire success-bandwidth spectrum with area-under-curve of 0.198 vs 0.142 for next-best methods. Our approach significantly outperforms existing communication strategies and establishes a theoretically grounded framework for deploying multi-agent systems in bandwidth-constrained environments such as robotic swarms, autonomous vehicle fleets, and distributed sensor networks.

Problem

• multi-agent communication
• bandwidth constraints
• coordination effectiveness
• real-world robotics

Innovation

• Information Bottleneck
• Vector Quantization
• Multi-Agent Communication
• Bandwidth Efficiency
• Gated Communication