96 citations · 123 across the 5 of their papers we have counts for
5 papers
Learning Dense Reward with Temporal Variant Self-Supervision
Yuning Wu, Jieliang Luo, Hui Li
Rewards play an essential role in reinforcement learning. In contrast to rule-based game environments with well-defined reward functions, complex real-world robotic applications, s…
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Hui Li, Xing Fu, Ruofan Wu +8
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as compu…
TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning
Lu Wang, Xiaofu Chang, Shuang Li +7
Dynamic graph modeling has recently attracted much attention due to its extensive applications in many real-world scenarios, such as recommendation systems, financial transactions,…
Variational Policy Propagation for Multi-agent Reinforcement Learning
Chao Qu, Hui Li, Chang Liu +6
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions ov…
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen, Shuang Li, Hui Li +3
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither…