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.

翻译：多智能体强化学习（MARL）在实现自主智能体间的协同行为方面取得了显著进展。然而，现有方法大多假设通信是瞬时、可靠且带宽无限的；这些条件在实际部署中很少得到满足。本综述系统性地回顾了在现实约束下（包括消息扰动、传输延迟和有限带宽）用于MARL的鲁棒高效通信策略的最新进展。此外，由于低延迟可靠性、带宽密集型数据共享以及通信与隐私权衡的挑战是实际MARL系统的核心问题，我们重点探讨了涉及协同自动驾驶、分布式同步定位与建图以及联邦学习的三个应用场景。最后，我们指出了关键的开放挑战和未来研究方向，提倡一种统一的方法，将通信、学习和鲁棒性协同设计，以弥合理论MARL模型与实际实现之间的差距。