Summary of Energy-guided Diffusion Sampling For Offline-to-online Reinforcement Learning, by Xu-hui Liu et al.
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning
by Xu-Hui Liu, Tian-Shuo Liu, Shengyi Jiang, Ruifeng Chen, Zhilong Zhang, Xinwei Chen, Yang Yu
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper introduces Energy-guided Diffusion Sampling (EDIS), a novel approach that combines offline and online reinforcement learning techniques to achieve efficient and safe learning. EDIS utilizes a diffusion model to extract prior knowledge from the offline dataset and distills it using energy functions for enhanced data generation in the online phase. Theoretical analysis shows that EDIS reduces suboptimality compared to solely utilizing online or offline data. The approach is plug-in compatible with existing methods, such as Cal-QL and IQL, and demonstrates a 20% average improvement in empirical performance on MuJoCo, AntMaze, and Adroit environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us learn better by combining old and new information. It takes old data and uses it to make new data that’s more helpful for learning. This makes the learning process faster and more accurate. The method is flexible and can be used with other learning methods. By using this method, we see a 20% improvement in how well the computer learns. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Reinforcement learning
