no code implementations • 26 Feb 2024 • Tianjiao Luo, Tim Pearce, Huayu Chen, Jianfei Chen, Jun Zhu
Generative Adversarial Imitation Learning (GAIL) trains a generative policy to mimic a demonstrator.
2 code implementations • 8 Feb 2024 • Huayu Chen, Guande He, Hang Su, Jun Zhu
Existing alignment methods, such as Direct Preference Optimization (DPO), are mainly tailored for pairwise preference data where rewards are implicitly defined rather than explicitly given.
1 code implementation • 11 Oct 2023 • Huayu Chen, Cheng Lu, Zhengyi Wang, Hang Su, Jun Zhu
Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling, which excels at representing heterogeneous behavior policies.
3 code implementations • 25 Apr 2023 • Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu
The main challenge for this setting is that the intermediate guidance during the diffusion sampling procedure, which is jointly defined by the sampling distribution and the energy function, is unknown and is hard to estimate.
1 code implementation • 29 Sep 2022 • Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu
To address this problem, we adopt a generative approach by decoupling the learned policy into two parts: an expressive generative behavior model and an action evaluation model.
no code implementations • 13 Sep 2022 • Huayu Chen, Huanhuan He, Jing Zhu, Shuting Sun, Jianxiu Li, Xuexiao Shao, Junxiang Li, Xiaowei Li, Bin Hu
Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results.
1 code implementation • 29 Jul 2021 • Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, Jun Zhu
In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement learning (DRL) that uses PyTorch as its backend.
no code implementations • 23 Feb 2020 • Shuting Sun, Jianxiu Li, Huayu Chen, Tao Gong, Xiaowei Li, Bin Hu
Results: Functional connectivity feature PLI is superior to the linear features and nonlinear features.