Summary of Diffstitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching, by Guanghe Li et al.
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
by Guanghe Li, Yixiang Shan, Zhengbang Zhu, Ting Long, Weinan Zhang
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In offline reinforcement learning, the quality of the dataset is crucial for policy performance. However, datasets often contain limited optimal trajectories, making it challenging for algorithms to learn. To address this issue, researchers introduced Diffusion-based Trajectory Stitching (DiffStitch), a novel pipeline that generates stitching transitions between trajectories. This approach effectively connects low-reward and high-reward trajectories, forming globally optimal ones. Empirical experiments on D4RL datasets demonstrate the effectiveness of DiffStitch across various RL methodologies, including one-step methods like IQL, imitation learning methods like TD3+BC, and trajectory optimization methods like DT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning is hard because datasets have few good examples. To help, scientists created a new way to make old data better by joining small steps into big ones. This makes it easier for machines to learn from what’s already happened. It works well with different types of machine learning and shows big improvements on certain tasks. |
Keywords
* Artificial intelligence * Diffusion * Machine learning * Optimization * Reinforcement learning
