Zongzhang Zhang @ NJU-AI
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.
- Reinforcement learning, including deep reinforcement learning and multi-agent reinforcement learning
- Probabilistic planning, particularly in partially observable Markov decision processes
- Imitation learning based on generative adversarial nets
- Di Xue, Lei Yuan, Zongzhang Zhang, and Yang Yu. Efficient Multi-Agent Communication via Shapley Message Value. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-2022), Vienna, Austria, 2022.
- Lei Yuan, Chenghe Wang, Jianhao Wang, Fuxiang Zhang, Feng Chen, Cong Guan, Zongzhang Zhang, Chongjie Zhang, and Yang Yu. Multi-Agent Concentrative Coordination with Decentralized Task Representation. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-2022), Vienna, Austria, 2022.
- Lei Yuan, Jianhao Wang, Fuxiang Zhang, Chenghe Wang, Zongzhang Zhang, Yang Yu, and Chongjie Zhang. Multi-Agent Incentive Communication via Decentralized Teammate Modeling. In: Proceedings of the 36th Conference on Artificial Intelligence (AAAI-2022), Virtual Conference, 2022.
- Fan-Ming Luo, Shengyi Jiang, Yang Yu, Zongzhang Zhang, and Yi-Feng Zhang. Adapt to Environment Sudden Changes by Learning a Context Sensitive Policy. In: Proceedings of the 36th Conference on Artificial Intelligence (AAAI-2022), Virtual Conference, 2022.
- Chenyang Wu, Guoyu Yang, Zongzhang Zhang, Yang Yu, Dong Li, Wulong Liu, and Jianye Hao. Adaptive Online Packing-guided Search for POMDPs. In: Advances in Neural Information Processing Systems 34 (NeurIPS-2021), pages 28419-28430, Virtual Conference, 2021.
- Xiong-Hui Chen, Shengyi Jiang, Feng Xu, Zongzhang Zhang, and Yang Yu. Cross-Modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning. In: Advances in Neural Information Processing Systems 34 (NeurIPS-2021), pages 12520-12532, Virtual Conference, 2021.
- Yan Zheng, Jianye Hao, Zongzhang Zhang, Zhaopeng Meng, Tianpei Yang, Yanran Li, and Changjie Fan. Efficient Policy Detecting and Reusing for Non-Stationarity in Markov Games, Autonomous Agents and Multi-Agent Systems, 2021, 35(2): 1-29.
- Cong Fei, Bing Wang, Yuzheng Zhuang, Zongzhang Zhang, et al. Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI-2020), pages 2929-2935, Yokohama, Japan, 2020.
- Tianpei Yang, Jianye Hao, Zhaopeng Meng, Zongzhang Zhang, et al. Efficient Deep Reinforcement Learning via Adaptive Policy Transfer. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI-2020), pages 3094-3100, Yokohama, Japan, 2020.
- Yan Zheng, Jianye Hao, Zongzhang Zhang, Zhaopeng Meng, and Xiaotian Hao. Efficient Multiagent Policy Optimization Based on Weighted Estimators in Stochastic Environments, Journal of Computer Science and Technology, 2020, 35(2): 268-280.
- Xiaobai Ma, Katherine R. Driggs-Campbell, Zongzhang Zhang, and Mykel J. Kochenderfer. Monte-Carlo Tree Search for Policy Optimization. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-2019), pages 3116-3122, Macao, China, 2019.
- Yan Zheng, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang, and Changjie Fan. A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents. In: Advances in Neural Information Processing Systems 31 (NeurIPS-2018), pages 960-970, Montreal, Canada, 2018.
- Zongzhang Zhang, Zhiyuan Pan, and Mykel J. Kochenderfer. Weighted Double Q-learning. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-2017), pages 3455-3461, Melbourne, Australia, 2017.
- Zongzhang Zhang, David Hsu, Wee Sun Lee, Zhan Wei Lim, and Aijun Bai. PLEASE: Palm Leaf Search for POMDPs with Large Observation Spaces. In: Proceedings of the 25th International Conference on Automated Planning and Scheduling (ICAPS-2015), pages 249-257, Jerusalem. Israel, 2015.
- Zongzhang Zhang, David Hsu, and Wee Sun Lee. Covering Number for Efficient Heuristic-Based POMDP Planning. In: Proceedings of the 31st International Conference on Machine Learning (ICML-2014), pages 28-36, Beijing, China, 2014.
- Zongzhang Zhang, Michael L. Littman, and Xiaoping Chen. Covering Number as a Complexity Measure for POMDP Planning and Learning. In: Proceedings of the 26th Conference on Artificial Intelligence (AAAI-2012), pages 1853-1859, Toronto, Ontario, Canada, 2012.
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Authorized patents >>>
- Editorial Board Member: Intelligent Computing (AAAS/Science Partner Journal, 2022 - 2024)
- Young Associate Editor: Frontiers of Computer Science (2019 - 2022)
- Senior Program Committee Member: IJCAI 2020-2021; AAAI 2019; ICAPS 2021; ECAI 2020
- Member of the Novel Program Committee Board: IJCAI 2022-2024
- Program Committee Member/Reviewer: AAAI 2018, 2020, 2022; ICML 2019-2022; IJCAI 2013, 2017-2019; NeurIPS 2018-2022; AAMAS 2021; ICLR 2021-2022; AISTATS 2022; ICAPS 2020; ECML-PKDD 2020; CoRL 2020; IJCNN 2020; CCDM 2020; ACML 2017-2019; PRICAI 2018-2019; ICA 2017-2019; ADPRL 2018; DAI 2019-2021; SSCI 2019; CCFAI 2019
- Journal Reviewer: Transactions on Pattern Analysis and Machine Intelligence, Journal of Artificial Intelligence Research, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, ACM Transactions on Intelligent Systems and Technology, Machine Learning, Pattern Recognition, IEEE Computational Intelligence Magazine, Robotics and Autonomous Systems, Information Sciences, Frontiers of Computer Science, Neurocomputing, Knowledge-Based Systems, Applied Intelligence, Expert Systems with Applications, 中国科学：信息科学, 计算机学报, 软件学报, 自动化学报, 计算机研究与发展
- Workshop Co-chair: Asian Workshop on Reinforcement Learning (AWRL) 2016-2018, PRICAI 2018's Workshop on Methods and Applications of Reinforcement Learning
- Local Organizing Committee Chair: DAI 2020, MLA 2020
- Professional Organization Membership: AAAI Member, IEEE Member, CCF Senior Member
- Reviewer Award: ICLR 2021's Outstanding Reviewer, NeurIPS 2019's Top Reviewer
- Multi-Agent Systems (for undergraduate students, Spring 2021, 2022) [textbook]
- Control Theory and Methods (for undergraduate and graduate students, Fall 2020, 2021) [textbook]
- Reinforcement Learning (for graduate students, Fall 2020, 2021, with Prof. Yang Yu) [textbook]
- Intelligent Systems: Design and Application (for undergraduate and graduate students, Spring 2020, 2021) [textbook]
- Intelligent Application Modeling (for undergraduate students, July 2019) [a summer course co-constructed with Tencent]
- 2020 - : Feng Xu 徐峰, Weijian Liao 廖沩健 (co-supervised with Prof. Ming Li)
- 2019 - : Tian Chang 常田, Yue Chen 陈越, Xianghan Kong 孔祥瀚 (co-supervised with Prof. Yang Yu)
- 2020 - : Dongyu Guo 郭东宇, Quan He 贺泉, Yafei Hu 胡亚飞, Chenyang Wu 吴晨阳, Di Xue 薛迪, Guoyu Yang 杨国钰
- 2021 - : Tianchi Li 李天赐, Yichen Li 李逸尘, Yuhang Ran 冉雨杭, Chenghe Wang 王铖鹤, Jiacheng Xu 徐嘉诚, Fuxiang Zhang 张福翔
- 2018 - : Feng Chen 陈烽, Hao Ding 丁豪, Chenxiao Gao 高辰潇, Fuguang Han 韩馥光, Rui Kong 孔锐, Wenjie Shen 沈雯杰, Aoran Wang 王傲然, Renzhe Zhou 周韧哲, Chao Chen 陈超, Xinyu Zhang 张鑫钰
- 2019 - : Ningjing Chao 巢凝静, Xuyang Chen 陈旭阳, Hai He 何海, Tianshuo Liu 刘天硕, Tianyuan Liu 刘恬远, Zhirui Zuo 左之睿, Tianyi Zhang 张天一, Anqi Li 李安琦
- 2020 - : Mingjuan Cao 曹明隽, Xianjie Shi 施贤杰, Zhichao Wu 吴智超, Ruiqi Xue 薛瑞奇, Rui Yu 俞睿
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.
National Key Laboratory for Novel Software Technology, Nanjing University, Xianlin Campus Mailbox 603, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China
(In Chinese:) 南京市栖霞区仙林大道163号，南京大学仙林校区603信箱，计算机软件新技术国家重点实验室，210023。
Created on September 11, 2019