Zhi Wang

Associate Professor, Nanjing University Department of Control Science and Intelligent Engineering 22 Hankou Road, Gulou District, Nanjing, China E-mail: zhiwang@nju.edu.cn Reinforcement Learning, Meta / Offline / Multi-Agent RL, Robot Learning

About me

I am now an Associate Professor in Nanjing University. I received the Ph.D. degree (Advisor: Professor Han-Xiong Li) from City University of Hong Kong and the bachelor's degree from Nanjing University. I was a visiting scholar at University of New South Wales (Visiting Advisor: Professor Daoyi Dong) and Institute of Automation, Chinese Academy of Sciences (Visiting Advisors: Professor Yuanheng Zhu, Dongbin Zhao).

My research interests include reinforcement learning (RL) algorithms and their applications on robot learning.
Specifically, I work on how learning algorithms can scale RL agents to (i) dynamic environments, (ii) offline settings, and (iii) multi-agent systems, allowing them to autonomously adapt to (i) non-stationary task distributions, (ii) non-interactive scenarios, and (iii) cooperative or competitive task assignments in real-world domains.
This includes a wide range of topics such as (i) meta-RL, lifelong/continual RL, transfer RL, (ii) offline RL, large models for RL, and (iii) multi-agent RL.

Academic Services

Associate Editor

• Special Sessions, IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023, 2022, & 2021

• Special Sessions, IEEE International Conference on Networking, Sensing, and Control (ICNSC), 2020

Reviewer

• Journals: IEEE TPAMI, TNNLS, IEEE TCYB, IEEE TSYS, IEEE-ASME TMECH, IEEE-CAA JAS

• Conferences: CVPR, AAAI, ECAI

Invited Talks

• The 4th Conference on Distributed Artificial Intelligence (DAI), Lifelong reinforcement learning for dynamic environments, 2022.12

• Institute of Automation, Chinese Academy of Sciences, Lifelong reinforcement learning for dynamic environments, 2022.12

• Tongji University, Lifelong reinforcement learning for dynamic environments, 2022.12

• University of Science and Technology of China, <Deep Reinforcement Learning>, Summer Course, Instructor, 2022.07

• University of Electronic Science and Technology of China, Incremental reinforcement learning for dynamic environments, 2020.12

• University of Science and Technology of China, Incremental reinforcement learning for dynamic environments, 2020.12

• University of New South Wales, Incremental reinforcement learning for dynamic environments, 2019.04

Teaching

• <Deep Reinforcement Learning>, for postgraduates

• <Digital Circuits>, for undergraduates