I am a PhD student at the University of Technology Sydney (UTS), supervised by Prof. Chengqi Zhang and Dr Jing Jiang. Before joining UTS, I was a visiting scholar at the Southern University of Science and Technology (SUSTech), working with Dr Jing Jiang and Prof. Xuan Song. I received my Master’s Degree and Bachelor’s Degree from the School of Data and Computer Science, Sun Yat-sen University (SYSU) in 2020 and 2017 respectively, under the supervision of Prof. Chang-Dong Wang.
My current research interests center on what, why, and how causal knowledge can be incorporated into reinforcement learning (RL). Many open problems in RL are closely related to causality, e.g., sample efficiency, generalization, and spurious correlation. Equipping RL algorithms with causal knowledge can effectively address these problems. I am also actively seeking opportunities to apply these algorithms to solve real-world problems.
- One survey paper on causal reinforcement learning is coming soon!
- One new paper on offline RL is on arxiv now.
- One paper on offline RL has been accepted by ICLR 2022 (Spotlight).
- One paper on session-based recommender systems has been accepted by TNNLS (SCI Q1).
- One paper on deep learning-based recommender systems has been accepted by AAAI 2019 (Oral).
- Our paper on serendipitous recommendation was accepted by TCYB (SCI Q1).