no code implementations • 27 Jun 2024 • ZiHao Wang, Shaofei Cai, Zhancun Mu, Haowei Lin, Ceyao Zhang, Xuejie Liu, Qing Li, Anji Liu, Xiaojian Ma, Yitao Liang
First, we introduce a self-supervised approach to learn a behavior encoder that produces discretized tokens for behavior trajectories $\tau = \{o_0, a_0, \dots\}$ and an imitation learning policy decoder conditioned on these tokens.
no code implementations • 31 Oct 2023 • Xuejie Liu, Anji Liu, Guy Van Den Broeck, Yitao Liang
A popular paradigm for offline Reinforcement Learning (RL) tasks is to first fit the offline trajectories to a sequence model, and then prompt the model for actions that lead to high expected return.
no code implementations • 16 Feb 2023 • Xuejie Liu, Anji Liu, Guy Van Den Broeck, Yitao Liang
In this paper, we theoretically and empirically discover that the performance of a PC can exceed that of its teacher model.
no code implementations • 18 Aug 2015 • Xuejie Liu, Jingbin Wang, Ming Yin, Benjamin Edwards, Peijuan Xu
Context of data points, which is usually defined as the other data points in a data set, has been found to play important roles in data representation and classification.
no code implementations • 9 Feb 2015 • Mohua Zhang, Jianhua Peng, Xuejie Liu, Jim Jing-Yan Wang
It attempts to represent the feature vector of a data sample by reconstructing it as the sparse linear combination of some basic elements, and a $L_2$ norm distance function is usually used as the loss function for the reconstruction error.