Summary of On the Curses Of Future and History in Future-dependent Value Functions For Off-policy Evaluation, by Yuheng Zhang et al.
On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation
by Yuheng Zhang, Nan Jiang
First submitted to arxiv on: 22 Feb 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 This research paper proposes novel methods for off-policy evaluation (OPE) in partially observable environments with complex observations. The study aims to develop estimators whose guarantees do not exponentially depend on the horizon. Building upon existing work, including future-dependent value functions, the authors identify limitations and propose novel coverage assumptions tailored to POMDPs’ structure. These assumptions enable polynomial bounds on estimation errors, leading to more efficient OPE algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better evaluate how well policies will perform in complex situations where we can’t directly observe everything. The researchers want to find ways to do this evaluation without the guarantee getting worse and worse as we look further ahead. They found some problems with previous methods, but also discovered new ideas that make it possible to evaluate policies more efficiently. |
