Summary of Offline Reinforcement Learning with Domain-unlabeled Data, by Soichiro Nishimori et al.
Offline Reinforcement Learning with Domain-Unlabeled Data
by Soichiro Nishimori, Xin-Qiang Cai, Johannes Ackermann, Masashi Sugiyama
First submitted to arxiv on: 11 Apr 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 The paper proposes a novel offline reinforcement learning (RL) setting called Positive-Unlabeled Offline RL (PUORL), which addresses the challenge of limited labeled target-domain data in robotics and healthcare. In PUORL, a small amount of labeled target-domain data is available, along with a large amount of domain-unlabeled data from multiple domains, including the target domain. To train a domain classifier, the authors propose a plug-and-play approach that leverages positive-unlabeled (PU) learning to extract target-domain samples from the domain-unlabeled data. The method achieves high performance and accurately identifies target-domain samples even when only 1-3% of the dataset is labeled. This algorithm seamlessly integrates with existing offline RL pipelines, enabling effective multi-domain data utilization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps machines learn new skills without needing to collect lots of data every time. It’s like teaching a robot how to do things without having to show it every single step. The problem is that most robots don’t have people around to label all the steps they need to follow, so they use something called Positive-Unlabeled Offline RL (PUORL) to figure out what steps are important and which aren’t. They did this by using a special kind of learning called PU learning, which helps them find the right path even when there’s not much data. The result is that robots can learn new skills without needing as many labeled examples. |
Keywords
» Artificial intelligence » Reinforcement learning
