Summary of Pretraining Decision Transformers with Reward Prediction For In-context Multi-task Structured Bandit Learning, by Subhojyoti Mukherjee et al.
Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning
by Subhojyoti Mukherjee, Josiah P. Hanna, Qiaomin Xie, Robert Nowak
First submitted to arxiv on: 7 Jun 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 This paper proposes a learning-to-learn approach for the multi-task structured bandit problem, where the goal is to minimize cumulative regret. The researchers use a transformer as a decision-making algorithm and develop a pre-training procedure that learns a near-optimal policy in-context. Unlike previous approaches, this method does not require privileged information like access to optimal arms and can outperform the demonstrator’s learning algorithm. The authors validate their claims over various structured bandit problems, showing that their proposed solution is generalizable and effective in identifying expected rewards on unseen test tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to learn how to make good decisions quickly. This paper develops a new way to do just that. It’s called the “multi-task structured bandit problem.” The goal is to find the best way to make decisions when you don’t have all the information. The researchers use a special type of computer program called a transformer to help with this task. They found a way to train this program without needing access to perfect or ideal choices. This new approach can actually outperform what’s currently available, making it useful for situations where you need to make quick decisions. |
Keywords
» Artificial intelligence » Multi task » Transformer
