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Effective agent coordination is crucial in cooperative Multi-Agent Reinforcement Learning (MARL).
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A. Pacchiano, J. Parker-Holder, Y. Tang, K. Choromanski, A. Choromanska, and M. Jordan, “Learning to score behaviors for guided policy optimization,” in the 37th International Conference on Machine Learning, (ICML 2020) , vol. 119, 13–18 Jul 2020, pp. 7445–7454
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T. Wang, J. Wang, C. Zheng, and C. Zhang, “Learning nearly decomposable value functions via communication minimization,” in the 8th International Conference on Learning Representations (ICLR 2020), Addis Ababa, Ethiopia , 2020
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Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, and C. Zhang, “Connecting the dots: Multivariate time series forecasting with graph neural networks,” in The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), Virtual Event, CA, USA , 2020, pp. 753–763
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S. Li, J. K. Gupta, P. Morales, R. E. Allen, and M. J. Kochenderfer, “Deep implicit coordination graphs for multi-agent reinforcement learning,” in the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), Virtual Event, United Kingdom , 2021, pp. 764–772
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T. Wang, L. Zeng, W. Dong, Q. Yang, Y. Yu, and C. Zhang, “Context-aware sparse deep coordination graphs,” in the 10th International Conference on Learning Representations (ICLR 2022), Virtual Event , 2022
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A. Oroojlooy and D. Hajinezhad, “A review of cooperative multi-agent deep reinforcement learning,” Appl. Intell. , vol. 53, no. 11, pp. 13 677–13 722, 2023
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W. Duan, J. Lu, and J. Xuan, “Group-aware coordination graph for multi-agent reinforcement learning,” in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI 2024, Jeju, South Korea, August 3-9, 2024 . ijcai.org, 2024, pp. 3926–3934. [Online]. Available: https://www.ijcai.org/proceedings/2024/434
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Z. Yao, F. Huang, Y. Li, W. Duan, P. Qian, N. Yang, and W. Susilo, “Mecon: A gnn-based graph classification framework for MEV activity detection,” Expert Syst. Appl. , vol. 269, p. 126486, 2025. [Online]. Available: https://doi.org/10.1016/j.eswa.2025.126486
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——, “Bayesian ego-graph inference for networked multi-agent reinforcement learning,” in The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, CA, USA , 2025. [Online]. Available: https://openreview.net/forum?id=3qeTs05bRL
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W. Duan, J. Lu, E. Yu, and J. Xuan, “Bandwidth-constrained variational message encoding for cooperative multi-agent reinforcement learning,” in Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026) . Paphos, Cyprus: IFAAMAS, May 2026. [Online]. Available: https://doi.org/10.65109/QXVZ8292
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P. Sunehag, G. Lever, A. Gruslys, W. M. Czarnecki, V. F. Zambaldi, M. Jaderberg, M. Lanctot, N. Sonnerat, J. Z. Leibo, K. Tuyls, and T. Graepel, “Value-decomposition networks for cooperative multi-agent learning based on team reward,” in the 17th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2018), Stockholm, Sweden , 2018, pp. 2085–2087
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