TieComm: Learning a Hierarchical Communication Topology Based on Tie Theory-Reference-Cited by-同舟云学术

TieComm: Learning a Hierarchical Communication Topology Based on Tie Theory

Published:2023 Issue: Volume: Page:604-613
ISSN:0302-9743
Container-title:Database Systems for Advanced Applications
language:
Short-container-title:

Author:

Yang Ming,Dong Renzhi,Wang Yiming,Liu Furui,Du Yali,Zhou Mingliang,Hou U Leong

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-30637-2_40

Reference22 articles.

1. Arney, C.: Linked: how everything is connected to everything else and what it means for business, science, and everyday life. Math. Comput. Educ. 43(3), 271 (2009)

2. Chu, T., Chinchali, S., Katti, S.: Multi-agent reinforcement learning for networked system control. In: International Conference on Learning Representations (2019)

3. Das, A., et al.: TarMAC: targeted multi-agent communication. In: International Conference on Machine Learning, pp. 1538–1546. PMLR (2019)

4. Du, Y., et al.: Learning correlated communication topology in multi-agent reinforcement learning. In: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, pp. 456–464 (2021)

5. Du, Y., et al.: Scalable model-based policy optimization for decentralized networked systems. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9019–9026. IEEE (2022)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Team-wise effective communication in multi-agent reinforcement learning;Autonomous Agents and Multi-Agent Systems;2024-07-18