Summary of Ollie: Imitation Learning From Offline Pretraining to Online Finetuning, by Sheng Yue et al.
OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning
by Sheng Yue, Xingyuan Hua, Ju Ren, Sen Lin, Junshan Zhang, Yaoxue Zhang
First submitted to arxiv on: 24 May 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 This paper presents a novel approach to offline-to-online imitation learning (IL) that combines static demonstration data with minimal environmental interaction. The authors find that simply combining existing offline and online IL methods can lead to poor performance, as the initial discriminator operates randomly and disrupts the policy initialization. To address this issue, they propose OLLIE, a principled method that learns a near-expert policy initialization along with an aligned discriminator initialization. OLLIE is shown to consistently outperform baseline methods in 20 challenging tasks across various domains, including continuous control and vision-based applications. The authors demonstrate the effectiveness of their approach in terms of performance, demonstration efficiency, and convergence speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching computers how to learn from examples, but not just any examples – ones that are shown to them once. This way, they can quickly adapt when they see something new. The problem is that some earlier methods don’t work well together, causing the computer to forget what it learned beforehand. To solve this issue, the authors came up with a new approach called OLLIE. It helps the computer learn from examples and then adapt quickly to new situations. In tests on 20 different tasks, OLLIE did much better than other methods. |
