1 citations · 1 across the 4 of their papers we have counts for
5 papers
DRAM: Delta-rule Recurrent Associative Memory for Robot Manipulation Policies
Xinyu Zhao, Yixiang Shan, Tao Yang +5
Robotic manipulation is inherently history-dependent, yet most pretrained robotic policies condition on only the current observation or a short temporal window. Equipping such poli…
Decoupling Policy Extraction for Offline Reinforcement Learning
Xuyao Lin, Yixiang Shan, Jinru Duan +7
Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled learning process is well motiv…
RAD: Retrieval High-quality Demonstrations to Enhance Decision-making
Lu Guo, Yixiang Shan, Zhengbang Zhu +5
Offline reinforcement learning (RL) learns policies from fixed datasets, thereby avoiding costly or unsafe environment interactions. However, its reliance on finite static datasets…
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
Guanghe Li, Yixiang Shan, Zhengbang Zhu +2
In offline reinforcement learning (RL), the performance of the learned policy highly depends on the quality of offline datasets. However, in many cases, the offline dataset contain…
Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning
Yixiang Shan, Zhengbang Zhu, Ting Long +4
The performance of offline reinforcement learning (RL) is sensitive to the proportion of high-return trajectories in the offline dataset. However, in many simulation environments a…