demonstration retrieval 1generative models 1offline reinforcement learning 1policy generalization 1retrieval-based planning 1
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cs.LG2026
Target-Aligned Bellman Backup for Cross-domain Offline Reinforcement Learning
Wei Liu, Ting Long
Cross-domain offline reinforcement learning (CDRL) aims to improve policy learning in a target domain by leveraging data collected from a source domain. Existing works typically as…
cs.LG2024
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…
cs.LG2024
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…