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Robust and Efficient Communication in Multi-Agent Reinforcement Learning

2025-11-14 · Zejiao Liu, Yi Li, Jiali Wang, Junqi Tu, Yitian Hong, Fangfei Li, Yang Liu, Toshiharu Sugawara, Yang Tang arxiv

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.

📄 PDF Abstract BibTeX arXiv:2511.11393

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Multi-agent Reinforcement Learning Federated Learning Autonomous Driving

Communication in Multi-Agent Reinforcement Learning: Intention Sharing

2021-01-01 · ICLR 2021 1 · Woojun Kim, Jongeui Park, Youngchul Sung

Communication is one of the core components for learning coordinated behavior in multi-agent systems. In this paper, we propose a new communication scheme named Intention Sharing (IS) for multi-agent reinforcement learn…

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Communication Methods in Multi-Agent Reinforcement Learning

2026-01-19 · Christoph Wittner arxiv

Multi-agent reinforcement learning is a promising research area that extends established reinforcement learning approaches to problems formulated as multi-agent systems. Recently, a multitude of communication methods hav…

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A Visual Communication Map for Multi-Agent Deep Reinforcement Learning

2020-02-27 · Ngoc Duy Nguyen, Thanh Thi Nguyen, Doug Creighton, Saeid Nahavandi

Deep reinforcement learning has been applied successfully to solve various real-world problems and the number of its applications in the multi-agent settings has been increasing. Multi-agent learning distinctly poses sig…

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Multi-Agent Reinforcement Learning with Communication-Constrained Priors

2025-12-03 · Guang Yang, Tianpei Yang, Jingwen Qiao, Yanqing Wu 외 arxiv

Communication is one of the effective means to improve the learning of cooperative policy in multi-agent systems. However, in most real-world scenarios, lossy communication is a prevalent issue. Existing multi-agent rein…

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A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning

2019-07-06 · Yixuan Lin, Kaiqing Zhang, Zhuoran Yang, Zhaoran Wang 외

This paper considers a distributed reinforcement learning problem in which a network of multiple agents aim to cooperatively maximize the globally averaged return through communication with only local neighbors. A random…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)