解决多智能体强化学习中通信不稳、延迟高、带宽有限的问题
Robust and Efficient Communication in Multi-Agent Reinforcement Learning
- 提出在真实约束下提升通信鲁棒性与效率的策略
- 覆盖自动驾驶、定位建图、联邦学习三大实际场景
- 适合关注实际部署的MARL研究者与工程人员
多智能体强化学习（MARL）在实现自主智能体协同行为方面取得显著进展。然而，现有方法大多假设通信瞬时、可靠且带宽无限，这在真实部署中极少成立。本文系统回顾了近年来在现实约束下的鲁棒高效通信策略进展，包括消息扰动、传输延迟和带宽限制。针对低延迟可靠性、高带宽数据共享及通信隐私权衡等核心挑战，重点聚焦于协作自动驾驶、分布式同时定位与地图构建（SLAM）以及联邦学习三个应用。最后，识别关键开放问题并提出未来方向，倡导将通信、学习与鲁棒性协同设计，弥合理论模型与实际应用之间的差距。
原文摘要 · Abstract (English)
Multi-agent reinforcement learning (MARL) has made significant strides in enabling coordinated behaviors among autonomous agents. However, most existing approaches assume that communication is instantaneous, reliable, and has unlimited bandwidth; these conditions are rarely met in real-world deployments. This survey systematically reviews recent advances in robust and efficient communication strategies for MARL under realistic constraints, including message perturbations, transmission delays, and limited bandwidth. Furthermore, because the challenges of low-latency reliability, bandwidth-intensive data sharing, and communication-privacy trade-offs are central to practical MARL systems, we focus on three applications involving cooperative autonomous driving, distributed simultaneous localization and mapping, and federated learning. Finally, we identify key open challenges and future research directions, advocating a unified approach that co-designs communication, learning, and robustness to bridge the gap between theoretical MARL models and practical implementations.
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