[32] Tianmin Shu and Yuandong Tian. M3rl: Mind-aware multi-agent management reinforcement learning. In International Conference on Learning Representations, 2019.
[33] Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi. Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning. In International Conference on Machine Learning, pages 5887-5896. PMLR, 2019.
[34] Jianyu Su, Stephen Adams, and Peter Beling. Value-decomposition multi-agent actor-critics. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 11352-11360, 2021.
[35] Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, and Thore Graepel. Value-decomposition networks for cooperative multi-agent learning based on team reward. In International Conference on Autonomous Agents and Multi-Agent Systems, pages 2085-2087, 2018.
[36] Ming Tan. Multi-agent reinforcement learning: Independent vs. cooperative agents. In International Conference on Machine Learning, pages 330-337, 1993.
[37] Lipeng Wan, Zeyang Liu, Xingyu Chen, Xuguang Lan, and Nanning Zheng. Greedy based value representation for optimal coordination in multi-agent reinforcement learning. In International Conference on Machine Learning, pages 22512-22535. PMLR, 2022.
[38] Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, and Chongjie Zhang. Qplex: Duplex dueling multi-agent q-learning. In International Conference on Learning Representations, 2021.
[39] Tonghan Wang, Heng Dong, Victor Lesser, and Chongjie Zhang. Roma: Multi-agent reinforcement learning with emergent roles. In International Conference on Machine Learning, pages 9876-9886. PMLR, 2020.
[40] Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, and Chongjie Zhang. Rode: Learning roles to decompose multi-agent tasks. In International Conference on Learning Representations, 2021.
[41] Tonghan Wang, Jianhao Wang, Yi Wu, and Chongjie Zhang. Influence-based multi-agent exploration. In International Conference on Learning Representations, 2019.
[42] Chao Wen, Xinghu Yao, Yuhui Wang, and Xiaoyang Tan. Smix  $(\lambda)$ : Enhancing centralized value functions for cooperative multi-agent reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 7301-7308, 2020.
[43] G. Wittemyer and Wayne M. Getz. Hierarchical dominance structure and social organization in african elephants, loxodonta africana. Animal Behaviour, 73(4):671-681, 2007.
[44] Zhao Xu, Yang Lyu, Quan Pan, Jinwen Hu, Chunhui Zhao, and Shuai Liu. Multi-vehicle flocking control with deep deterministic policy gradient method. In International Conference on Control and Automation, pages 306–311. IEEE, 2018.
[45] Yaodong Yang, Jianye Hao, Guangyong Chen, Hongyao Tang, Yingfeng Chen, Yujing Hu, Changjie Fan, and Zhongyu Wei. Q-value path decomposition for deep multiagent reinforcement learning. In International Conference on Machine Learning, pages 10706-10715. PMLR, 2020.
[46] Yaodong Yang, Jianye Hao, Ben Liao, Kun Shao, Guangyong Chen, Wulong Liu, and Hongyao Tang. Qatten: A general framework for cooperative multiagent reinforcement learning. arXiv preprint arXiv:2002.03939, 2020.
[47] Dayong Ye, Minjie Zhang, and Yun Yang. A multi-agent framework for packet routing in wireless sensor networks. Sensors, 15(5):10026-10047, 2015.
[48] Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu. The surprising effectiveness of ppo in cooperative multi-agent games. In Advances in Neural Information Processing Systems, volume 35, pages 24611-24624, 2022.