Zongzhang Zhang
LAMDA Group
School of Artificial Intelligence
National Key Laboratory for Novel Software Technology
Nanjing University, P. R. China
Office: Room A503, Yi Fu Building, Xianlin Campus
Email: zzzhang@nju.edu.cn, zhangzongzhang@gmail.com
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Zongzhang Zhang |
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I am now an associate professor at the School of Artificial Intelligence, Nanjing University. I am also a member of the LAMDA group, led by Prof. Zhi-Hua Zhou. From July 2014 to June 2019, I worked as an associate professor at the School of Computer Science and Technology, Soochow University. I received my Ph.D. degree from the School of Computer Science and Technology, University of Science and Technology of China, advised by Prof. Xiaoping Chen, in 2012. I worked with Prof. Mykel J. Kochenderfer as a visiting scholar at the Stanford Intelligent Systems Laboratory (SISL) from September 2018 to March 2019 and worked as a research fellow at the School of Computing, National University of Singapore, from November 2012 to June 2014, under Prof. David Hsu and Prof. Wee Sun Lee. Before that, I visited the Rutgers Laboratory for Real-Life Reinforcement Learning (RL3), directed by Prof. Michael L. Littman, as a research visiting student, from October 2010 to October 2011. I also briefly worked as a research engineer at the Noah's Ark Lab in the Huawei Company in 2012.
Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data [PDF + Supplementary] [Code]
Fuxiang Zhang, Chengxing Jia, Yi-Chen Li, Lei Yuan, Yang Yu, and Zongzhang Zhang
In: Proceedings of the 11th International Conference on Learning Representations (ICLR-2023), Kigali, Rwanda, 2023.
Policy-Independent Behavioral Metric-Based Representation for Deep Reinforcement Learning [PDF] [Supplementary]
Weijian Liao, Zongzhang Zhang, and Yang Yu
In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-2023), Washington, DC, USA, 2023.
Anti-Drifting Feature Selection via Deep Reinforcement Learning (Student Abstract)
Aoran Wang, Hongyang Yang, Feng Mao, Zongzhang Zhang, Yang Yu, and Xiaoyang Liu
In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-2023), Washington, DC, USA, 2023. (Best Student Abstract Award - Honorable Mention)
Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning [PDF] [Supplementary]
Chenyang Wu, Tianci Li, Zongzhang Zhang, and Yang Yu
In: Advances in Neural Information Processing Systems 35 (NeurIPS-2022), New Orleans, USA, 2022.
Efficient Multi-agent Communication via Self-supervised Information Aggregation [PDF] [Supplementary] [Code] [Demo]
Cong Guan, Feng Chen, Lei Yuan, Chenghe Wang, Hao Yin, Zongzhang Zhang, and Yang Yu
In: Advances in Neural Information Processing Systems 35 (NeurIPS-2022), New Orleans, USA, 2022.
Multi-agent Dynamic Algorithm Configuration [PDF] [Supplementary] [Code] [Demo]
Ke Xue, Jiacheng Xu, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, and Yang Yu
In: Advances in Neural Information Processing Systems 35 (NeurIPS-2022), New Orleans, USA, 2022.
Efficient Multi-Agent Communication via Shapley Message Value [PDF] [Code] [Demo]
Di Xue, Lei Yuan, Zongzhang Zhang, and Yang Yu
In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-2022), pages 578-584, Vienna, Austria, 2022.
Multi-Agent Concentrative Coordination with Decentralized Task Representation [PDF] [Code] [Demo]
Lei Yuan, Chenghe Wang, Jianhao Wang, Fuxiang Zhang, Feng Chen, Cong Guan, Zongzhang Zhang, Chongjie Zhang>, and Yang Yu
In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-2022), pages 599-605, Vienna, Austria, 2022.
Multi-Agent Incentive Communication via Decentralized Teammate Modeling [PDF] [Code] [Demo]
Lei Yuan, Jianhao Wang, Fuxiang Zhang, Chenghe Wang, Zongzhang Zhang, Yang Yu, and Chongjie Zhang
In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-2022), pages 9466-9474, Virtual Conference, 2022.
Adapt to Environment Sudden Changes by Learning a Context Sensitive Policy [PDF] [Code]
Fan-Ming Luo, Shengyi Jiang, Yang Yu, Zongzhang Zhang, and Yi-Feng Zhang
In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-2022), pages 7637-7646, Virtual Conference, 2022.
Adaptive Online Packing-guided Search for POMDPs [PDF] [Supplementary] [Code]
Chenyang Wu, Guoyu Yang, Zongzhang Zhang, Yang Yu, Dong Li, Wulong Liu, and Jianye Hao
In: Advances in Neural Information Processing Systems 34 (NeurIPS-2021), pages 28419-28430, Virtual Conference, 2021.
Cross-Modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning [PDF] [Supplementary] [Code]
Xiong-Hui Chen, Shengyi Jiang, Feng Xu, Zongzhang Zhang, and Yang Yu
In: Advances in Neural Information Processing Systems 34 (NeurIPS-2021), pages 12520-12532, Virtual Conference, 2021.
Efficient Policy Detecting and Reusing for Non-Stationarity in Markov Games [PDF]
Yan Zheng, Jianye Hao, Zongzhang Zhang, Zhaopeng Meng, Tianpei Yang, Yanran Li, and Changjie Fan
Autonomous Agents and Multi-Agent Systems, 2021, 35(2): 1-29.
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets [PDF]
Cong Fei, Bing Wang, Yuzheng Zhuang, Zongzhang Zhang, Jianye Hao, Hongbo Zhang, Xuewu Ji, and Wulong Liu
In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI-2020), pages 2929-2935, Yokohama, Japan, 2020.
Efficient Deep Reinforcement Learning via Adaptive Policy Transfer [PDF]
Tianpei Yang, Jianye Hao, Zhaopeng Meng, Zongzhang Zhang, Weixun Wang, Yujing Hu, Yingfeng Chen, Changjie Fan, Wulong Liu, Zhaodong Wang, and Jiajie Peng
In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI-2020), pages 3094-3100, Yokohama, Japan, 2020.
Efficient Multiagent Policy Optimization Based on Weighted Estimators in Stochastic Environments [PDF]
Yan Zheng, Jianye Hao, Zongzhang Zhang, Zhaopeng Meng, and Xiaotian Hao
Journal of Computer Science and Technology, 2020, 35(2): 268-280.
A Survey of Imitation Learning Based on Generative Adversarial Nets [PDF]
Jiahao Lin, Zongzhang Zhang, Chong Jiang, and Jianye Hao
Chinese Journal of Computers, 2020, 43(2): 326-351.
Monte-Carlo Tree Search for Policy Optimization [PDF]
Xiaobai Ma, Katherine R. Driggs-Campbell, Zongzhang Zhang, and Mykel J. Kochenderfer
In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-2019), pages 3116-3122, Macao, China, 2019.
Efficient Reinforcement Learning in Continuous State and Action Spaces with Dyna and Policy Approximation [PDF]
Shan Zhong, Quan Liu, Zongzhang Zhang, and Qiming Fu
Frontiers of Computer Science, 2019, 13(1): 106-126.
A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents [PDF] [Supplementary]
Yan Zheng, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang, and Changjie Fan
In: Advances in Neural Information Processing Systems 31 (NeurIPS-2018), pages 960-970, Montreal, Canada, 2018.
A Survey on Deep Reinforcement Learning [PDF]
Quan Liu, Jianwei Zhai, Zongzhang Zhang, Shan Zhong, Qian Zhou, Peng Zhang, and Jin Xu
Chinese Journal of Computers, 2018, 41(1): 1-27. (2017-2021's Best Paper Award)
Weighted Double Q-learning [PDF]
Zongzhang Zhang, Zhiyuan Pan, and Mykel J. Kochenderfer
In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-2017), pages 3455-3461, Melbourne, Australia, 2017.
Reasoning and Predicting POMDP Planning Complexity via Covering Numbers [PDF]
Zongzhang Zhang, Qiming Fu, Xiaofang Zhang, and Quan Liu
Frontiers of Computer Science, 2016, 10(4): 726-740.
PLEASE: Palm Leaf Search for POMDPs with Large Observation Spaces [PDF]
Zongzhang Zhang, David Hsu, Wee Sun Lee, Zhan Wei Lim, and Aijun Bai
In: Proceedings of the 25th International Conference on Automated Planning and Scheduling (ICAPS-2015), pages 249-257, Jerusalem. Israel, 2015.
Covering Number for Efficient Heuristic-Based POMDP Planning [PDF] [Supplementary]
Zongzhang Zhang, David Hsu, and Wee Sun Lee
In: Proceedings of the 31st International Conference on Machine Learning (ICML-2014), pages 28-36, Beijing, China, 2014.
Thompson Sampling based Monte-Carlo Planning in POMDPs [PDF]
Aijun Bai, Feng Wu, Zongzhang Zhang, and Xiaoping Chen
In: Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS-2014), pages 28-36, Portsmouth, USA, 2014.
Covering Number as a Complexity Measure for POMDP Planning and Learning [PDF]
Zongzhang Zhang, Michael L. Littman, and Xiaoping Chen
In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI-2012), pages 1853-1859, Toronto, Ontario, Canada, 2012.
FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs [PDF]
Zongzhang Zhang and Xiaoping Chen
In: Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI-2012), pages 934-943, Catalina Island, CA, USA, 2012.
Ph.D. Students:
Master Students:
Undergraduate Students:
To prospective students:
I am in a LAMDA's reinforcement learning team with Prof. Yang Yu.
I am looking for self-driven, diligent, adaptable and resourceful students to work on exciting research in machine learning, including topics of reinforcement learning, probabilistic planning, imitation learning, multi-agent learning, etc. If you are passionate about research, you are welcome to contact me.