DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
<|reference_start|>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 trajectories, forming globally optimal trajectories to address the challenges faced by offline RL algorithms. Empirical experiments conducted on D4RL datasets demonstrate the effectiveness of DiffStitch across RL methodologies. Notably, DiffStitch demonstrates substantial enhancements in the performance of one-step methods (IQL), imitation learning methods (TD3+BC), and trajectory optimization methods (DT).<|reference_end|>
@article{li2024diffstitch:,
    title={DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based
  Trajectory Stitching},
    author={Guanghe Li, Yixiang Shan, Zhengbang Zhu, Ting Long, Weinan Zhang},
    journal={arXiv preprint arXiv:2402.02439},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02439},
    primaryClass={cs.LG cs.AI}
}