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Zheng Tian

Multi-Agent Constrained Policy Optimisation


Oct 06, 2021
Shangding Gu, Jakub Grudzien Kuba, Munning Wen, Ruiqing Chen, Ziyan Wang, Zheng Tian, Jun Wang, Alois Knoll, Yaodong Yang

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A Game-Theoretic Approach to Multi-Agent Trust Region Optimization


Jun 12, 2021
Ying Wen, Hui Chen, Yaodong Yang, Zheng Tian, Minne Li, Xu Chen, Jun Wang

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* A Multi-Agent Trust Region Learning (MATRL) algorithm that augments the single-agent trust region policy optimization with a weak stable fixed point approximated by the policy-space meta-game 
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Online Double Oracle


Mar 16, 2021
Le Cong Dinh, Yaodong Yang, Zheng Tian, Nicolas Perez Nieves, Oliver Slumbers, David Henry Mguni, Haitham Bou Ammar, Jun Wang

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* [email protected] 
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SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving


Nov 01, 2020
Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ahilan, Baokuan Zhang, Jiannan Wu, Zhengang Fu, Kasra Rezaee, Peyman Yadmellat, Mohsen Rohani, Nicolas Perez Nieves, Yihan Ni, Seyedershad Banijamali, Alexander Cowen Rivers, Zheng Tian, Daniel Palenicek, Haitham bou Ammar, Hongbo Zhang, Wulong Liu, Jianye Hao, Jun Wang

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* 20 pages, 11 figures. Paper accepted to CoRL 2020 
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Learning to Model Opponent Learning


Jun 06, 2020
Ian Davies, Zheng Tian, Jun Wang

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* Accepted to AAAI2020 as part of the Student Abstract Program 
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A Regularized Opponent Model with Maximum Entropy Objective


May 17, 2019
Zheng Tian, Ying Wen, Zhichen Gong, Faiz Punakkath, Shihao Zou, Jun Wang

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Learning Multi-agent Implicit Communication Through Actions: A Case Study in Contract Bridge, a Collaborative Imperfect-Information Game


Oct 10, 2018
Zheng Tian, Shihao Zou, Tim Warr, Lisheng Wu, Jun Wang

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Thinking Fast and Slow with Deep Learning and Tree Search


Dec 03, 2017
Thomas Anthony, Zheng Tian, David Barber

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* v1 to v2: - Add a value function in MCTS - Some MCTS hyper-parameters changed - Repetition of experiments: improved accuracy and errors shown. (note the reduction in effect size for the tpt/cat experiment) - Results from a longer training run, including changes in expert strength in training - Comparison to MoHex. v3: clarify independence of ExIt and AG0. v4: see appendix E 
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