Summary of On Sample-efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond, by Thanh Nguyen-tang and Raman Arora
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond
by Thanh Nguyen-Tang, Raman Arora
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 investigates the problem of learning from historical datasets for sequential decision-making, also known as offline reinforcement learning. The authors propose a new notion of data diversity and use it to unify three different classes of algorithms: version spaces, regularized optimization, and posterior sampling. They show that these algorithms achieve comparable sample efficiency, recovering state-of-the-art sub-optimality bounds for finite and linear model classes. This is surprising given previous work suggesting unfavorable sample complexity for some algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning helps machines learn from past experiences to make better decisions in the future. Researchers studied three different ways to do this: version spaces, regularized optimization, and posterior sampling. They found that all these methods are equally good at using old data to make new decisions. This is a big surprise because some methods were thought to be much worse than others. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
