2025
ContraDiff: Planning Towards High Return States via Contrastive Learning
ICLR 2025poster
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 and real-world scenarios, there are large ratios of low-return trajectories rather than high-return trajectories, which m…