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

2026-02-02 · Ahmad Farooq, Kamran Iqbal arxiv

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.

📄 PDF Abstract BibTeX arXiv:2602.02035

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-agent Reinforcement Learning

Learning Efficient Multi-agent Communication: An Information Bottleneck Approach

2019-11-16 · ICML 2020 1 · Rundong Wang, Xu He, Runsheng Yu, Wei Qiu 외

We consider the problem of the limited-bandwidth communication for multi-agent reinforcement learning, where agents cooperate with the assistance of a communication protocol and a scheduler. The protocol and scheduler jo…

Multi-agent Reinforcement Learning Reinforcement Learning Scheduling

Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems

2020-11-30 · Niko A. Grupen, Daniel D. Lee, Bart Selman

In this work, we study emergent communication through the lens of cooperative multi-agent behavior in nature. Using insights from animal communication, we propose a spectrum from low-bandwidth (e.g. pheromone trails) to …

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth

2023-06-16 · Qingshuang Sun, Denis Steckelmacher, Yuan YAO, Ann Nowé 외

Communication plays a vital role in multi-agent systems, fostering collaboration and coordination. However, in real-world scenarios where communication is bandwidth-limited, existing multi-agent reinforcement learning (M…

Multi-agent Reinforcement Learning Scheduling

CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning

2021-12-20 · Qi Tian, Kun Kuang, Baoxiang Wang, Furui Liu 외

Communication is one of the core components for cooperative multi-agent reinforcement learning (MARL). The communication bandwidth, in many real applications, is always subject to certain constraints. To improve communic…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)Starcraft+1

LangCoop: Collaborative Driving with Language

2025-04-18 · Xiangbo Gao, Yuheng Wu, Rujia Wang, Chenxi Liu 외

Multi-agent collaboration holds great promise for enhancing the safety, reliability, and mobility of autonomous driving systems by enabling information sharing among multiple connected agents. However, existing multi-age…

Autonomous Driving