DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning. - Xiao-Yin Liu; Xiao-Hu Zhou; Xiao-Liang Xie; Shi-Qi Liu 004; Zhen-Qiu Feng; Hao Li 077; Mei-Jiang Gui; Tian-Yu Xiang; De-Xing Huang; Zeng-Guang Hou (CoRR, 2023)

CROP: Conservative Reward for Model-based Offline Policy Optimization. - Hao Li 077; Xiao-Hu Zhou; Xiao-Liang Xie; Shi-Qi Liu 004; Zhen-Qiu Feng; Xiao-Yin Liu; Mei-Jiang Gui; Tian-Yu Xiang; De-Xing Huang; Bo-Xian Yao; Zeng-Guang Hou (CoRR, 2023)

MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operator. - Xiao-Yin Liu; Xiao-Hu Zhou; Guotao Li; Hao Li 077; Mei-Jiang Gui; Tian-Yu Xiang; De-Xing Huang; Zeng-Guang Hou (IJCAI, 2024)

A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition. - Xiao-Yin Liu; Guotao Li; Xiao-Hu Zhou; Xu Liang; Zeng-Guang Hou (CoRR, 2024)

LEASE: Offline Preference-based Reinforcement Learning with High Sample Efficiency. - Xiao-Yin Liu; Guotao Li; Xiao-Hu Zhou; Zeng-Guang Hou (CoRR, 2024)

A Weight-Aware-Based Multisource Unsupervised Domain Adaptation Method for Human Motion Intention Recognition. - Xiao-Yin Liu; Guotao Li; Xiao-Hu Zhou; Xu Liang; Zeng-Guang Hou (IEEE Trans. Cybern., 2025)