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Zihan (Hanry) Ding丁子涵 zhding96@gmail.com / zihand@princeton.edu Google Scholar / GitHub / Twitter / CV / Research Gate / Previous Website |
About Me
Zihan Ding is third-year Ph.D. at Electrical and Computer Engineering Department, Princeton University, supervised by Chi Jin. He obtained a MSc Machine Learning specialist degree with Distinction from Imperial College London in Fall 2019. He previously worked with Dr. Edward Johns at Imperial Robot Learning Lab for his thesis project. Before Msc, he received two Bachelor degrees from University of Science and Technology of China in 2018, major in Photoelectric Information Science and Engineering (Physics Dept., Bachelor of Science) and dual in Computer Science and Technology (CS Dept., Bachelor of Engineering). His bachelor thesis is supervised by Dr. Jinming Cui and Prof. Yunfeng Huang. He has worked as research intern at Meta FAIR lab (New York), Inspir.ai (Beijing), Tencent Robotics X (Shenzhen) and Borealis AI (Toronto).His primary research interests are: deep reinforcement learning (RL), robot learning, artificial general intelligence, simulation-to-reality transfer. He also has general interests in meta learning, transfer learning, imitation learning, quantum computation, quantum machine learning, neuroscience, etc. His previous research topics cover: robot learning, sim-to-real transfer, tactile sensory on robots, RL applications, exlainability of RL policy, generative models, multimodal policy representation, machine learning in quantum computation, robotic visuomotor control, etc.
"Research is about intellectual pilgrimage when climbing the mountain of persent knowledge, self-talking, acquiring deep and structral understanding, capturing the flash of inspiration, quick prototyping, persuing rigorism, simplicity and beauty."
Publications
| Books | |
| Deep Reinforcement Learning: Fundamentals, Research and Applications
Hao Dong, Zihan Ding, Shanghang Zhang Eds. Springer 2020 ISBN 978-981-15-4094-3, 1st ed. [Homepage][eBook][Amazon][中文版] | |
| 机器学习系统：设计与实现（Machine Learning System: Design and Implementation）
Luo Mai, Hao Dong et al 清华大学出版社 Tsinghua University Press 2022 ISBN Coming Soon. Author of Chapter: Reinforcement Learning System [Homepage] | |
| Papers | |
| Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning Zihan Ding, Chi Jin [Paper] | |
| Survey of Consciousness Theory from Computational Perspective: At the Dawn of Artificial General Intelligence Zihan Ding*, Xiaoxi Wei*, Yidan Xu* [Paper] | |
| Learning a Universal Human Prior for Dexterous Manipulation from Human Preference Zihan Ding, Yuanpei Chen, Allen Z. Ren, Shixiang Shane Gu, Hao Dong, Chi Jin [Paper][Website] | |
| Representation Learning for Low-rank General-sum Markov Games Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Zihan Ding, Chi Jin, Mengdi Wang The 11th International Conference on Learning Representations (ICLR) 2023 [Paper] | |
| A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games Zihan Ding*, Dijia Su*, Qinghua Liu, Chi Jin [Paper][Code1][Code2][Slide] | |
| Not Only Domain Randomization: Universal Policy with Embedding System Identification Zihan Ding Robotics Science and Systems (RSS) 2023 Interdisciplinary Exploration of Generalizable Manipulation Policy Learning: Paradigms and Debates Workshop [Paper][Code] | |
| Learning Distributed and Fair Policies for Network Load Balancing as Markov Potentia Game Zhiyuan Yao*, Zihan Ding* 36th Conference on Neural Information Processing Systems (NeurIPS) 2022 [Paper][Code] | |
| Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center Zhiyuan Yao, Zihan Ding and Thomas Heide Clausen 31th ACM International Conference on Information and Knowledge Management (CIKM) 2022 [Paper][Code] | |
| Reinforced Workload Distribution Fairness Zhiyuan Yao, Zihan Ding and Thomas Heide Clausen Machine Learning for Systems at 35th Conference on Neural Information Processing Systems (NeurIPS) 2021. [Paper][Code] | |
| Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning Jie Ren, Yewen Li, Zihan Ding, Wei Pan, Hao Dong [Paper][Code] | |
| Bayesian Optimization for Wavefront Sensing and Error Correction Qian Zhong-Hua, Ding Zi-Han, Ai Ming-Zhong, Zheng Yong-Xiang, Cui Jin-Ming, Huang Yun-Feng, Li Chuan-Feng, Guo Guang-Can Chinese Physics Letters 2021. [Paper] | |
| Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory Zihan Ding, Ya-Yen Tsai, Wang Wei Lee, Bidan Huang International Conference on Intelligent Robots and Systems (IROS) 2021. [Paper][Code] | |
| DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration Ya-Yen Tsai, Hui Xu, Zihan Ding, Chong Zhang, Edward Johns, Bidan Huang IEEE Robotics and Automation Letters (RA-L) 2021. [Paper] | |
| Robotic Visuomotor Control with Unsupervised Forward Model
Learned from Videos Haoqi Yuan, Ruihai Wu, Andrew Zhao, Haipeng Zhang, Zihan Ding, Hao Dong International Conference on Intelligent Robots and Systems (IROS) 2021. [Paper][Website] | |
| CDT: Cascading Decision Trees for Explainable Reinforcement Learning Zihan Ding, Pablo Hernandez-Leal, Gavin Weiguang Ding, Changjian Li, Ruitong Huang [Paper][Code] | |
| RLzoo: A Comprehensive and Adaptive Reinforcement Learning Library Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Guo Li, Quancheng Guo, Luo Mai and Hao Dong ACM Multimedia Open Source Software Competition 2021. [Paper][Repo] | |
| Crossing The Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamics Eugene Valassakis, Zihan Ding and Edward Johns International Conference on Intelligent Robots and Systems (IROS) 2020. [Paper][Website][Video] | |
| Sim-to-Real Transfer for Optical Tactile Sensing Zihan Ding, Nathan F. Lepora and Edward Johns International Conference on Robotics and Automation (ICRA) 2020. [Paper][Website][Video] | |
| Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Mai Xu, Zihan Ding, and Lianlong Wu The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020. [Paper][Code] | |
| Fast and High-Fidelity Readout of Single Trapped-Ion Qubit via Machine-Learning Methods Zihan Ding, Jinming Cui, Yunfeng Huang, Chuanfeng Li, Tao Tu, Guangcan Guo Physical Review Applied 2019. [Paper] [Code] | |
| Tensor Super-Resolution with Generative Adversarial Nets: A Large Image Generation Approach Zihan Ding, Xiao-Yang Liu, Miao Yin International Joint Conference on Artificial Intelligence, Human Brain Artificial Intelligence 2019. [Paper] [Code] | |
| Deep Reinforcement Learning for Intelligent Transportation Systems Xiao-Yang Liu, Zihan Ding, Sem Borst, Anwar Walid NeurIPS Workshop on Machine Learning for Intelligent Transportation Systems 2018. [Paper] [Code] | |
| Accelerated Exhaustive Eye Glints Localization Method for Infrared
Video Oculography Zihan Ding, Jiayi Luo, Hongping Deng Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC ‘18. [Paper] [Code] |
Open Source Projects
TensorLayer Reinforcement Learning Tutorials are hands-on examples of implementing DRL algorithms with TensorLayer, each example is self-contained with simple structure, particularly suitable for novices.
RLzoo is a collection of the most practical reinforcement learning algorithms, frameworks and applications. It is implemented with Tensorflow 2.0 and API of neural network layers in TensorLayer 2, to provide a hands-on fast-developing approach for reinforcement learning practices and benchmarks.
MARS is a library for multi-agent RL on Markov games, like PettingZoo Atari, SlimeVolleyBall, etc.
Robot Learning Library is a collorative, open-source library for robot learning, mainly using techniques like deep reinforcement learning, robotics simulation, computer vision.
AI community has accumulated an open-source code ocean over the past decade. Applying these intellectual and engineering properties to finance will initiate a paradigm shift from the conventional trading routine to an automated machine learning approach, even RLOps in finance.