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In cooperative multi-agent reinforcement learning (MARL), where agents only have access to partial observations, efficiently leveraging local information is critical.
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Oriol Vinyals, Timo Ewalds, Sergey Bartunov, P. Georgiev, A. S. Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, J. Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, S. Petersen, K. Simonyan, T. Schaul, H. V. Hasselt, D. Silver, T. Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, D. Lawrence, Anders Ekermo, J. Repp, and Rodney Tsing · 2017
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Kemas M Lhaksmana, Yohei Murakami, and Toru Ishida · 2018
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Jianhong Wang, Yuan Zhang, Tae-Kyun Kim, and Yunjie Gu · 2020
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ROMA: multi-agent reinforcement learning with emergent roles
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Learning nearly decomposable value functions via communication minimization
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Action semantics network: Considering the effects of actions in multiagent systems
Weixun Wang, Tianpei Yang, Yong Liu, Jianye Hao, Xiaotian Hao, Yujing Hu, Yingfeng Chen, Changjie Fan, and Yang Gao · 2020
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Annie Xie, Dylan P. Losey, Ryan Tolsma, Chelsea Finn, and Dorsa Sadigh · 2020
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