DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching

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 contains very limited optimal trajectories, which poses a challenge for offline RL algorithms as agents must acquire the ability to transit to high-reward regions. To address this issue, we introduce Diffusion-based Trajectory Stitching (DiffStitch), a novel diffusion-based data augmentation pipeline that systematically generates stitching transitions between trajectories. DiffStitch effectively connects low-reward trajectories with high-reward …

agents algorithms boosting cases challenge cs.ai cs.lg dataset datasets diffusion offline performance policy quality reinforcement reinforcement learning stitching trajectory transit