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 currently an Associate Professor at the School of Artificial Intelligence, Nanjing University, and a member of the LAMDA group led by Prof. Zhi-Hua Zhou. From July 2014 to June 2019, I served as an Associate Professor at the School of Computer Science and Technology, Soochow University. I received my bachelor's degree in mathematics from Central South University in 2007 and my Ph.D. degree in Computer Science from University of Science and Technology of China in 2012, under the supervision of Prof. Xiaoping Chen.
My research experience includes appointments as a Visiting Scholar at the Stanford Intelligent Systems Laboratory (SISL) with Prof. Mykel J. Kochenderfer (Sept. 2018 – Mar. 2019), and as a Research Fellow at the School of Computing, National University of Singapore (Nov. 2012 – Jun. 2014), working with Prof. David Hsu and Prof. Wee Sun Lee. Earlier, I was a Visiting Student at the Rutgers Laboratory for Real-Life Reinforcement Learning (RL3) directed by Prof. Michael L. Littman (Oct. 2010 – Oct. 2011). I also briefly worked as a Research Engineer at Huawei's Noah's Ark Lab in 2012.
Generalizable Multi-modal Adversarial Imitation Learning for Non-stationary Dynamics [Paper]
Yi-Chen Li, Ningjing Chao, Zongzhang Zhang*, Fuxiang Zhang, Lei Yuan, and Yang Yu
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(7): 5600-5612.
Learning to Coordinate with Different Teammates via Team Probing [Paper]
Hao Ding, Chengxing Jia, Zongzhang Zhang*, Cong Guan, Feng Chen, Lei Yuan, and Yang Yu
IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(9): 15807-15821.
Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language Models [Online]
Fuxiang Zhang, Junyou Li, Yi-Chen Li, Zongzhang Zhang*, Yang Yu, and Deheng Ye*
IEEE Transactions on Neural Networks and Learning Systems, 2025, 08: 1-12.
Efficient Multi-Agent Cooperation Learning through Teammate Lookahead [Paper]
Feng Chen, Xinwei Chen, Rong-Jun Qin, Cong Guan, Lei Yuan, Zongzhang Zhang*, and Yang Yu
Transactions on Machine Learning Research, 2025, 03: 1-27.
Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning [Paper] [Code] [Project Page]
Chen-Xiao Gao, Chenyang Wu, Mingjun Cao, Chenjun Xiao, Yang Yu, and Zongzhang Zhang*
In: Proceedings of the 42nd International Conference on Machine Learning (ICML-2025), Vancouver, Canada, 2025.
Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting Mitigation [Paper] [Code]
Yi-Chen Li, Fuxiang Zhang, Wenjie Qiu, Lei Yuan, Chengxing Jia, Zongzhang Zhang*, Yang Yu, and Bo An
In: Proceedings of the 13th International Conference on Learning Representations (ICLR-2025), Singapore, 2025.
Multi-Agent Domain Calibration with a Handful of Offline Data [Paper] [Code]
Tao Jiang, Lei Yuan, Lihe Li, Cong Guan, Zongzhang Zhang*, and Yang Yu
In: Advances in Neural Information Processing Systems 37 (NeurIPS-2024), pages 69607-69636, Vancouver, Canada, 2024.
Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics [Paper] [Code]
Xinyu Zhang, Wenjie Qiu, Yi-Chen Li, Lei Yuan, Chengxing Jia, Zongzhang Zhang*, and Yang Yu
In: Proceedings of the 41st International Conference on Machine Learning (ICML-2024), pages 59741-59758, Vienna, Austria, 2024.
Efficient and Stable Offline-to-online Reinforcement Learning via Continual Policy Revitalization [Paper] [Appendix] [Code]
Rui Kong, Chenyang Wu, Chen-Xiao Gao, Zongzhang Zhang*, and Ming Li
In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI-2024), pages 4317-4325, Jeju Island, South Korea, 2024.
Focus-Then-Decide: Segmentation-Assisted Reinforcement Learning [Paper] [Appendix] [Code] [Project Page]
Chao Chen, Jiacheng Xu, Weijian Liao, Hao Ding, Zongzhang Zhang*, Yang Yu, and Rui Zhao
In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-2024), pages 11240–11248, Vancouver, Canada, 2024.
ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning [Paper] [Appendix] [Code]
Chen-Xiao Gao, Chenyang Wu, Mingjun Cao, Rui Kong, Zongzhang Zhang*, and Yang Yu
In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-2024), pages 12127–12135, Vancouver, Canada, 2024.
Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data Limitations [Paper] [Appendix] [Code]
Renzhe Zhou, Chen-Xiao Gao, Zongzhang Zhang*, and Yang Yu
In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-2024), pages 17132-17140, Vancouver, Canada, 2024.
Deep Anomaly Detection via Active Anomaly Search [Paper] [Appendix] [Code]
Chao Chen, Dawei Wang, Feng Mao, Jiacheng Xu, Zongzhang Zhang*, and Yang Yu
In: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2024), pages 308–316, Auckland, New Zealand, 2024.
Surfing Information: The Challenge of Intelligent Decision-Making [Paper]
Chenyang Wu and Zongzhang Zhang*
Intelligent Computing, 2023, 2: Article 0041.
Policy Regularization with Dataset Constraint for Offline Reinforcement Learning [Paper] [Code]
Yuhang Ran, Yi-Chen Li, Fuxiang Zhang, Zongzhang Zhang*, and Yang Yu
In: Proceedings of the 40th International Conference on Machine Learning (ICML-2023), pages 28701-28717, Honolulu, Hawaii, USA, 2023.
Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data [Paper] [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.
Internal Logical Induction for Pixel-Symbolic Reinforcement Learning [Paper] [Code]
Jiacheng Xu, Chao Chen, Fuxiang Zhang, Lei Yuan, Zongzhang Zhang*, and Yang Yu
In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2023), pages 2825–2837, Long Beach, CA, USA, 2023.
Policy-Independent Behavioral Metric-Based Representation for Deep Reinforcement Learning [Paper] [Appendix]
Weijian Liao, Zongzhang Zhang*, and Yang Yu
In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-2023), pages 8746-8754, Washington, DC, USA, 2023.
Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning [Paper] [Appendix]
Chenyang Wu, Tianci Li, Zongzhang Zhang*, and Yang Yu
In: Advances in Neural Information Processing Systems 35 (NeurIPS-2022), pages 14210-14223, New Orleans, USA, 2022.
Efficient Multi-Agent Communication via Shapley Message Value [Paper] [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 Incentive Communication via Decentralized Teammate Modeling [Paper] [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.
Adaptive Online Packing-guided Search for POMDPs [Paper] [Appendix] [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 [Paper] [Appendix] [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.
[Full List of Publications] [DBLP] [Google Scholar] [Code Repositories]
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To prospective students:
I am in a LAMDA's reinforcement learning team (LAMDA RL Lab) 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, multi-agent systems, probabilistic planning, imitation learning, etc. If you are passionate about research, you are welcome to contact me.