no code implementations • 7 Oct 2023 • Ziqi Zhang, Xiao Xiong, Zifeng Zhuang, Jinxin Liu, Donglin Wang
Studying how to fine-tune offline reinforcement learning (RL) pre-trained policy is profoundly significant for enhancing the sample efficiency of RL algorithms.
1 code implementation • 29 Dec 2022 • Xiao Xiong
This paper investigates the use of artificial neural networks (ANNs) to solve differential equations (DEs) and the construction of the loss function which meets both differential equation and its initial/boundary condition of a certain DE.
no code implementations • 24 Dec 2022 • Zheyi Fan, Zhaohui Li, Jingyan Wang, Dennis K. J. Lin, Xiao Xiong, Qingpei Hu
Because of the widespread existence of noise and data corruption, recovering the true regression parameters with a certain proportion of corrupted response variables is an essential task.