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Yaodong Yang

Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning



Yuanpei Chen , Yaodong Yang , Tianhao Wu , Shengjie Wang , Xidong Feng , Jiechuang Jiang , Stephen Marcus McAleer , Hao Dong , Zongqing Lu , Song-Chun Zhu

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* 36 pages, 7 figures 
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Learning Risk-Averse Equilibria in Multi-Agent Systems



Oliver Slumbers , David Henry Mguni , Stephen McAleer , Jun Wang , Yaodong Yang

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Multi-Agent Reinforcement Learning is a Sequence Modeling Problem



Muning Wen , Jakub Grudzien Kuba , Runji Lin , Weinan Zhang , Ying Wen , Jun Wang , Yaodong Yang

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A Review of Safe Reinforcement Learning: Methods, Theory and Applications



Shangding Gu , Long Yang , Yali Du , Guang Chen , Florian Walter , Jun Wang , Yaodong Yang , Alois Knoll

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On the Convergence of Fictitious Play: A Decomposition Approach



Yurong Chen , Xiaotie Deng , Chenchen Li , David Mguni , Jun Wang , Xiang Yan , Yaodong Yang

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API: Boosting Multi-Agent Reinforcement Learning via Agent-Permutation-Invariant Networks



Xiaotian Hao , Weixun Wang , Hangyu Mao , Yaodong Yang , Dong Li , Yan Zheng , Zhen Wang , Jianye Hao

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Efficient Policy Space Response Oracles



Ming Zhou , Jingxiao Chen , Ying Wen , Weinan Zhang , Yaodong Yang , Yong Yu

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* revised with single-column, 24 pages, 8 figures 
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Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning



Zehao Dou , Jakub Grudzien Kuba , Yaodong Yang

* 37 pages 
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Settling the Communication Complexity for Distributed Offline Reinforcement Learning



Juliusz Krysztof Ziomek , Jun Wang , Yaodong Yang

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