Summary of Dida: Denoised Imitation Learning Based on Domain Adaptation, by Kaichen Huang et al.
DIDA: Denoised Imitation Learning based on Domain Adaptation
by Kaichen Huang, Hai-Hang Sun, Shenghua Wan, Minghao Shao, Shuai Feng, Le Gan, De-Chuan Zhan
First submitted to arxiv on: 4 Apr 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 addresses the problem of Learning from Noisy Demonstrations (LND), where an imitator must learn from data contaminated by noise. Previous methods improve robustness by injecting noise into expert data or using ranking information, but these approaches may fail in LND settings. To address this issue, the authors propose Denoised Imitation learning based on Domain Adaptation (DIDA), which uses two discriminators to distinguish noise and expertise levels, enabling a feature encoder to learn task-related representations. The approach is tested on MuJoCo demonstrations with varying types of noise, outperforming most baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how machines can learn from bad examples. When we’re trying to teach a machine something new, we often get “noisy” data that isn’t very good quality. This makes it harder for the machine to learn correctly. The authors of this paper came up with a new way to make learning easier in these situations. They created a method called Denoised Imitation learning based on Domain Adaptation (DIDA). It’s like having two filters that help separate the good information from the bad information. This makes it easier for the machine to learn what we want it to do. |
Keywords
* Artificial intelligence * Domain adaptation * Encoder
