Summary of Leveraging Unlabeled Data Sharing Through Kernel Function Approximation in Offline Reinforcement Learning, by Yen-ru Lai et al.
Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement Learning
by Yen-Ru Lai, Fu-Chieh Chang, Pei-Yuan Wu
First submitted to arxiv on: 22 Aug 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 A novel offline reinforcement learning (RL) method is proposed to effectively utilize unlabelled data, addressing the challenge of limited or expensive labelled datasets. The algorithm, which employs kernel function approximation, demonstrates promising results while providing theoretical guarantees for its complexity. Eigenvalue decay conditions are introduced to determine the complexity of the approach. This work has significant implications for applications where labelled data is scarce or costly to obtain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning (RL) is used to learn policies from a fixed dataset. However, this process often requires large amounts of data and can be expensive when labelled datasets are needed. One solution is to use unlabelled data instead. This paper presents an algorithm that uses kernel function approximation to utilize unlabelled data in offline RL while providing theoretical guarantees for its complexity. This approach has the potential to revolutionize how we learn from data. |
Keywords
» Artificial intelligence » Reinforcement learning
