Diverse Effective Relationship Exploration for Cooperative Multi-Agent Reinforcement Learning

Author:

Jiang Hao1,Liu Yuntao1,Li Shengze1,Zhang Jieyuan1,Xu Xinhai1,Liu Donghong1

Affiliation:

1. Academy of Military Science, Beijing, China

Funder

the National Natural Science Foundation of China

Publisher

ACM

Reference36 articles.

1. AlphaStar

2. Liheng Chen , Hongyi Guo , Yali Du , Fei Fang , Haifeng Zhang , Weinan Zhang , and Yong Yu . 2021 . Signal instructed coordination in cooperative multi-agent reinforcement learning . In International Conference on Distributed Artificial Intelligence. Springer, 185--205 . Liheng Chen, Hongyi Guo, Yali Du, Fei Fang, Haifeng Zhang, Weinan Zhang, and Yong Yu. 2021. Signal instructed coordination in cooperative multi-agent reinforcement learning. In International Conference on Distributed Artificial Intelligence. Springer, 185--205.

3. Li Chenghao , Tonghan Wang , Chengjie Wu , Qianchuan Zhao , Jun Yang , and Chongjie Zhang . 2021 . Celebrating diversity in shared multi-agent reinforcement learning . Advances in Neural Information Processing Systems , Vol. 34 (2021). Li Chenghao, Tonghan Wang, Chengjie Wu, Qianchuan Zhao, Jun Yang, and Chongjie Zhang. 2021. Celebrating diversity in shared multi-agent reinforcement learning. Advances in Neural Information Processing Systems, Vol. 34 (2021).

4. Jakob Foerster , Ioannis Alexandros Assael , Nando De Freitas, and Shimon Whiteson. 2016 . Learning to communicate with deep multi-agent reinforcement learning. Advances in neural information processing systems, Vol. 29 (2016). Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson. 2016. Learning to communicate with deep multi-agent reinforcement learning. Advances in neural information processing systems, Vol. 29 (2016).

5. Counterfactual Multi-Agent Policy Gradients

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