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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.11393 |
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| _version_ | 1866912709377785856 |
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| author | Liu, Zejiao Li, Yi Wang, Jiali Tu, Junqi Hong, Yitian Li, Fangfei Liu, Yang Sugawara, Toshiharu Tang, Yang |
| author_facet | Liu, Zejiao Li, Yi Wang, Jiali Tu, Junqi Hong, Yitian Li, Fangfei Liu, Yang Sugawara, Toshiharu Tang, Yang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust and Efficient Communication in Multi-Agent Reinforcement Learning Liu, Zejiao Li, Yi Wang, Jiali Tu, Junqi Hong, Yitian Li, Fangfei Liu, Yang Sugawara, Toshiharu Tang, Yang Artificial Intelligence 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. |
| title | Robust and Efficient Communication in Multi-Agent Reinforcement Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.11393 |