Summary of Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear Mdps, by Kevin Tan et al.
Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs
by Kevin Tan, Wei Fan, Yuting Wei
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- 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 hybrid reinforcement learning framework that combines offline data with online exploration is gaining attention. This paper investigates whether such a hybrid approach can improve upon existing lower bounds in purely offline and purely online settings, without relying on a specific assumption called concentrability. The question has been partially answered for the tabular case, but remains open for other cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers wants to know if they can use a combination of old data and new experiments to make better decisions. They’re trying to figure out if this approach can be even better than using just one or the other method. So far, some experts have shown that it works in certain situations, but there’s still more to learn. |
Keywords
» Artificial intelligence » Attention » Reinforcement learning
