Summary of Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design, by Shuze Liu et al.
Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design
by Shuze Liu, Shangtong Zhang
First submitted to arxiv on: 31 Jan 2023
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 introduces novel methods to improve the data efficiency of online Monte Carlo estimators used in reinforcement learning, which are essential for hyperparameter tuning and testing different algorithmic design choices. The proposed approach reduces the variance of these estimators while maintaining their unbiasedness. A tailored closed-form behavior policy is developed, which can be learned from previously collected offline data using efficient algorithms. Theoretical analysis shows how the error in learning this policy affects the reduced variance. Compared to previous works, the method achieves better empirical performance in a broader set of environments with fewer requirements for offline data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve the way we evaluate policies in reinforcement learning by making it more efficient and accurate. Right now, we use a method that requires a lot of interactions with the environment, which can be a problem. The researchers came up with new ways to make this process better, so we don’t need as many interactions. They developed a special policy that can be learned from old data, making it more efficient and effective. |
Keywords
* Artificial intelligence * Hyperparameter * Reinforcement learning
