Summary of A Note on Sample Complexity Of Interactive Imitation Learning with Log Loss, by Yichen Li et al.
A Note on Sample Complexity of Interactive Imitation Learning with Log Loss
by Yichen Li, Chicheng Zhang
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 research note builds upon recent advancements in imitation learning (IL), specifically offline behavior cloning (BC) with log loss. The study revisits interactive IL, focusing on the DAgger algorithm with log loss. The authors demonstrate two key findings: first, a one-sample-per-round variant of DAgger outperforms BC in state-wise annotation; second, without recoverability assumption, DAgger with first-step mixture policies matches the performance of BC. Additionally, the study introduces a new notion of decoupled Hellinger distance that separates state and action sequences, which can be of independent interest. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imitation learning is a way to learn from experts in decision-making problems. Recently, researchers found that offline imitation learning is very good at making decisions. However, they also showed that the interactive version, called DAgger, isn’t as good because it doesn’t use all the information available. In this study, scientists looked at how to make DAgger better and found two ways to improve it. They also came up with a new way to measure how close their results are to what experts would do. |
