Summary of Understanding the Training and Generalization Of Pretrained Transformer For Sequential Decision Making, by Hanzhao Wang et al.
Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making
by Hanzhao Wang, Yu Pan, Fupeng Sun, Shang Liu, Kalyan Talluri, Guanting Chen, Xiaocheng Li
First submitted to arxiv on: 23 May 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 The paper explores the application of pre-trained transformers to sequential decision-making problems, a subset of reinforcement learning. The authors demonstrate that optimal actions can be used in the pre-training phase, providing new insights for training and generalization. A key contribution is the introduction of a natural solution to an out-of-distribution issue in existing methods, achieved by incorporating transformer-generated action sequences into the training procedure. Numerical experiments show that pre-trained transformers outperform structured algorithms like UCB and Thompson sampling in three cases: utilizing prior knowledge, handling misspecification, and achieving better regret for short time horizons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how a special kind of artificial intelligence called pre-trained transformers can be used to make decisions one after another. This is helpful for things like setting prices or choosing what news articles to feature. The authors show that by using the best actions in the training phase, they can get better results than usual methods. They also fix a problem with these methods that makes them not work well when faced with unexpected situations. In some cases, the pre-trained transformers do a lot better than other algorithms at making decisions. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning » Transformer
