Summary of Abstract Reward Processes: Leveraging State Abstraction For Consistent Off-policy Evaluation, by Shreyas Chaudhari et al.
Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation
by Shreyas Chaudhari, Ameet Deshpande, Bruno Castro da Silva, Philip S. Thomas
First submitted to arxiv on: 3 Oct 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 Medium Difficulty Summary: This paper introduces STAR, a novel framework for off-policy evaluation (OPE) that encompasses various existing OPE methods. The proposed approach leverages state abstraction to distill complex problems into compact, discrete models called abstract reward processes (ARPs). By estimating ARPs from off-policy data, predictions are provably consistent and achieve lower mean squared prediction errors compared to previous methods. STAR is demonstrated empirically to outperform existing OPE methods in twelve cases, with the best estimator surpassing baselines in all scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This research paper helps make it easier to use machine learning for real-world problems like healthcare and self-driving cars. The authors created a new way to evaluate policies using data from before, which is important because we often can’t get fresh data. They called this new method STAR. It takes complex problems and makes them simpler by breaking them down into smaller pieces. This helps make predictions more accurate. The researchers tested STAR with twelve different scenarios and found that it outperformed other methods in most cases. |
Keywords
* Artificial intelligence * Machine learning
