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In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent agents are imperative for the successful attainment of target objectives.
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Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson · 2016
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Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
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A unified game-theoretic approach to multiagent reinforcement learning
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Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang · 2018
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Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, and Joelle Pineau · 2019
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Learning to schedule communication in multi-agent reinforcement learning
Updet: Universal multi-agent reinforcement learning via policy decoupling with transformers
Siyi Hu, Fengda Zhu, Xiaojun Chang, and Xiaodan Liang · 2021
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Pierre-Yves Lajoie, Benjamin Ramtoula, Fang Wu, and Giovanni Beltrame · 2021
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Coach-player multi-agent reinforcement learning for dynamic team composition
Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, and Anima Anandkumar · 2021
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Yaru Niu, Rohan R Paleja, and Matthew C Gombolay · 2021
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Yuanfei Wang, Fangwei Zhong, Jing Xu, and Yizhou Wang · 2021
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The starcraft multi-agent challenge
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Trust region policy optimisation in multi-agent reinforcement learning
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Temporal dynamic weighted graph convolution for multi-agent reinforcement learning
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Learning efficient diverse communication for cooperative heterogeneous teaming
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Multi-agent reinforcement learning is a sequence modeling problem
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Efficient multi-agent communication via shapley message value
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Heterogeneous multi-robot reinforcement learning
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Shengchao Hu, Li Shen, Ya Zhang, and Dacheng Tao · 2023
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Learning multi-agent coordination through connectivity-driven communication
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