Summary of Online Adaptation For Enhancing Imitation Learning Policies, by Federico Malato et al.
Online Adaptation for Enhancing Imitation Learning Policies
by Federico Malato, Ville Hautamaki
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 A machine learning approach to recover from failure in autonomous agents’ imitation learning is proposed. The method combines a pre-trained policy’s action proposal with relevant experience recorded by an expert to adapt the agent’s behavior. This adaptation enables the agent to achieve better performance even when its initial, non-adapted policy catastrophically fails. The proposed approach demonstrates improved results compared to traditional imitation learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous agents can learn from human examples without a reward signal through imitations. But if the dataset is wrong or the task is too complex, these agents fail. To fix this, we’re going to let them adapt online. Our method combines what an expert does with what they do well and what we already know. It helps us find better actions that are closer to the expert’s. We tested it and saw that it works even when the original plan fails. |
Keywords
» Artificial intelligence » Machine learning
