Zihan (Hanry) Ding丁子涵
firstname.lastname@example.org / email@example.com
Google Scholar / GitHub / CV / Research Gate / Previous Website
|Deep Reinforcement Learning: Foundamentals, Research and Applications
Hao Dong, Zihan Ding, Shanghang Zhang Eds.
Springer 2020 ISBN 978-981-15-4094-3, 1st ed.
|RLzoo: A Comprehensive and Adaptive Reinforcement Learning Library |
Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Luo Mai and Hao Dong
|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.
|Sim-to-Real Transfer for Optical Tactile Sensing|
Zihan Ding, Nathan F. Lepora and Edward Johns
International Conference on Robotics and Automation (ICRA) 2020.
|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.
|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.
|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.
|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.
|Accelerated Exhaustive Eye Glints Localization Method for Infrared
Zihan Ding, Jiayi Luo, Hongping Deng
Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC ‘18.
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.