Trajectory Stitching

Trajectory stitching is a data augmentation technique used to improve the performance of reinforcement learning and diffusion models by creating longer, higher-quality trajectories from shorter, potentially suboptimal segments. Current research focuses on using diffusion models and learned dynamics models to generate these stitched trajectories, often within the context of offline reinforcement learning where data is limited. This approach shows promise in enhancing the efficiency of diffusion-based image generation and improving the performance of offline reinforcement learning algorithms by addressing data sparsity and suboptimality issues, leading to more robust and effective AI systems.