Summary of Diffusing States and Matching Scores: a New Framework For Imitation Learning, by Runzhe Wu et al.
Diffusing States and Matching Scores: A New Framework for Imitation Learning
by Runzhe Wu, Yiding Chen, Gokul Swamy, Kianté Brantley, Wen Sun
First submitted to arxiv on: 17 Oct 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 This paper proposes a novel approach to adversarial imitation learning by lifting insights from diffusion modeling to the sequential setting. The method involves diffusing states and performing score-matching along diffused states to measure the discrepancy between the expert’s and learner’s states. This approach requires training score functions to predict noises via standard regression, making it more stable and easier to train than adversarial methods. Theoretically, the paper proves first- and second-order instance-dependent bounds with linear scaling in the horizon, showing that the approach avoids compounding errors that plague offline approaches to imitation learning. Empirically, the method outperforms GAN-style imitation learning baselines and discriminator-free imitation learning baselines across various continuous control problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn by copying what experts do. Instead of competing against each other like in a game, it makes things easier for the computer to learn by making states “diffuse” or spread out. This allows the computer to predict what noise or mistake it will make and correct itself. The approach is better than others because it’s more stable and doesn’t get worse over time. It even beats other methods in tasks like controlling robots to walk, sit, or crawl. |
Keywords
» Artificial intelligence » Diffusion » Gan » Regression
