13 citations · 27 across the 9 of their papers we have counts for
9 papers
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own
Weirui Ye, Yunsheng Zhang, Haoyang Weng +6
Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorithms directly in the real world. For on…
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Zhecheng Yuan, Zhengrong Xue, Bo Yuan +4
Learning generalizable policies that can adapt to unseen environments remains challenging in visual Reinforcement Learning (RL). Existing approaches try to acquire a robust represe…
Resolving Copycat Problems in Visual Imitation Learning via Residual Action Prediction
Chia-Chi Chuang, Donglin Yang, Chuan Wen +1
Imitation learning is a widely used policy learning method that enables intelligent agents to acquire complex skills from expert demonstrations. The input to the imitation learning…
An Empirical Study on Disentanglement of Negative-free Contrastive Learning
Jinkun Cao, Ruiqian Nai, Qing Yang +2
Negative-free contrastive learning methods have attracted a lot of attention with simplicity and impressive performances for large-scale pretraining. However, its disentanglement p…
Keeping Minimal Experience to Achieve Efficient Interpretable Policy Distillation
Xiao Liu, Shuyang Liu, Wenbin Li +2
Although deep reinforcement learning has become a universal solution for complex control tasks, its real-world applicability is still limited because lacking security guarantees fo…
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning
Bernie Wang, Simon Xu, Kurt Keutzer +2
Meta-reinforcement learning typically requires orders of magnitude more samples than single task reinforcement learning methods. This is because meta-training needs to deal with mo…
Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation
Xiaoting Han, Lei Qi, Qian Yu +4
Unsupervised domain adaptation (UDA) methods have shown their promising performance in the cross-modality medical image segmentation tasks. These typical methods usually utilize a…
Reinforcement Learning with Latent Flow
Wenling Shang, Xiaofei Wang, Aravind Srinivas +4
Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such informati…
HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation
Kelei He, Chunfeng Lian, Bing Zhang +6
Accurate segmentation of the prostate is a key step in external beam radiation therapy treatments. In this paper, we tackle the challenging task of prostate segmentation in CT imag…