Summary of Imitation Learning From Suboptimal Demonstrations Via Meta-learning An Action Ranker, by Jiangdong Fan et al.
Imitation Learning from Suboptimal Demonstrations via Meta-Learning An Action Ranker
by Jiangdong Fan, Hongcai He, Paul Weng, Hui Xu, Jie Shao
First submitted to arxiv on: 28 Dec 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 In this paper, researchers tackle the challenge of imitation learning, where AI systems learn from demonstrations provided by experts. A major limitation is that these expert demonstrations are often scarce and expensive to obtain. To address this issue, previous methods have explored using supplementary non-expert demonstrations, but they often discard valuable information. The proposed solution, called ILMAR (Imitation Learning via Meta-Learning an Action Ranker), combines weighted behavior cloning with a new approach that selectively integrates knowledge from these supplementary demonstrations. The method also incorporates meta-goals to optimize the policy performance by minimizing the distance between the current and expert policies. Comprehensive experiments demonstrate the effectiveness of ILMAR in handling suboptimal demonstrations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imitation learning is a way for AI systems to learn new skills by watching humans do tasks. Right now, it’s hard to get enough high-quality examples from experts, which can be expensive or hard to find. Researchers have been trying to use less perfect demonstrations to help improve the process. This paper introduces a new approach called ILMAR that can make better use of these imperfect demos. The idea is to focus on the parts of the demo that are actually helpful and ignore the rest. The method also tries to get closer to what the expert would do by comparing its own actions with the expert’s. Overall, this paper shows that ILMAR can be really good at learning from imperfect demonstrations. |
Keywords
» Artificial intelligence » Meta learning
