用信息瓶颈与向量量化实现高效多智能体通信
Bandwidth-Efficient Multi-Agent Communication through Information Bottleneck and Vector Quantization
- 结合信息瓶颈与向量量化，动态压缩并离散通信内容
- 通信带宽减少71.4%，性能比无通信提升181.8%
- 适合机器人集群、自动驾驶车队等带宽受限场景
在真实机器人应用中，多智能体强化学习系统面临严峻的通信约束，严重影响协同效果。本文提出一种融合信息瓶颈理论与向量量化的方法，实现多智能体环境中的选择性、低带宽通信。该方法通过信息论优化，学习压缩并离散通信消息，同时保留任务关键信息。引入门控通信机制，根据环境状态和智能体状态动态判断通信必要性。在复杂协作任务上的实验表明，该方法相较无通信基线性能提升181.8%，带宽降低71.4%。帕累托前沿分析显示，在整个成功率-带宽谱上均占优，曲线下面积达0.198，优于次优方法的0.142。本方法显著超越现有通信策略，为机器人蜂群、自动驾驶车队及分布式传感器网络等带宽受限场景提供理论扎实的部署框架。
原文摘要 · Abstract (English)
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 71.4%. Pareto frontier analysis shows dominance across the entire success-bandwidth spectrum, with an area under the 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.
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