Summary of Provably Efficient Off-policy Adversarial Imitation Learning with Convergence Guarantees, by Yilei Chen et al.
Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees
by Yilei Chen, Vittorio Giammarino, James Queeney, Ioannis Ch. Paschalidis
First submitted to arxiv on: 26 May 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 investigates the convergence properties and sample complexity of Adversarial Imitation Learning (AIL) algorithms when using off-policy data. The authors show that reusing samples generated by the most recent policies does not compromise the convergence guarantees, even without importance sampling correction. This result highlights the benefits of having more data available, which dominates the distribution shift error induced by off-policy updates. Theoretical support is provided for the sample efficiency of off-policy AIL algorithms, a novel contribution in this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make Adversarial Imitation Learning (AIL) work better when you don’t have enough data. AIL tries to copy the behavior of an expert, but it needs a lot of examples to learn from. The researchers found that even if they reuse old samples, AIL can still get better with more data. This is important because it means we might be able to make AIL work faster and with less training data. |
