Ph.D., Associate Professor
School of Artificial Intelligence
State Key Laboratory for Novel Software Technology
Nanjing University, P. R. China
Office: International College Building A503, Xianlin Campus
Email: firstname.lastname@example.org, email@example.com
I am now an associate professor at the School of Artificial Intelligence, Nanjing University. I am also a member of the LAMDA group. 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.
林嘉豪, 章宗长, 姜冲, 郝建业. 基于生成对抗网络的模仿学习综述. 计算机学报, 2020, 43(2): 326-351.
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, 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, Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS-2018), pages 960-970, Montreal, Canada, 2018.
Zongzhang Zhang, Zhiyuan Pan, and Mykel J. Kochenderfer, Weighted Double Q-learning, Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-2017), pages 3455-3461, Melbourne, Australia, 2017.
Zongzhang Zhang, Qiming Fu, Xiaofang Zhang, and Quan Liu, Reasoning and Predicting POMDP Planning Complexity via Covering Numbers, Frontiers of Computer Science, 2016, 10(4): 726-740.
Zongzhang Zhang, David Hsu, Wee Sun Lee, Zhan Wei Lim, and Aijun Bai, PLEASE: Palm Leaf Search for POMDPs with Large Observation Spaces, 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, 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, Proceedings of the 26th Conference on Artificial Intelligence (AAAI-2012), pages 1853-1859, Toronto, Ontario, Canada, 2012.
Zongzhang Zhang and Xiaoping Chen, FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs, Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI-2012), pages 934-943, Catalina Island, CA, USA, 2012.
I am very happy to work with the following students. Unless otherwise stated, my students are co-supervised with Prof. Yang Yu.
I still have some master students at the Soochow University.
To prospective students:
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.