Summary of Off-policy Evaluation in Markov Decision Processes Under Weak Distributional Overlap, by Mohammad Mehrabi and Stefan Wager
Off-Policy Evaluation in Markov Decision Processes under Weak Distributional Overlap
by Mohammad Mehrabi, Stefan Wager
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed doubly robust methods for off-policy evaluation in Markov decision processes (MDPs) have shown promising results under sequential ignorability. The existing approaches, however, rely heavily on the strong distributional overlap assumption, which is typically only credible when the state space of the MDP is bounded. To address this limitation, researchers introduce a class of truncated doubly robust (TDR) estimators that can perform well even when the distribution ratio of the target and data-collection policies is not square-integrable. The proposed approach recovers the large-sample behavior previously established under strong distributional overlap and achieves a minimax rate of convergence over a class of MDPs defined only using mixing conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores new ways to evaluate how well a policy performs in a situation, without actually seeing what happens when you use that policy. This is useful because it lets you test different policies without having to try them all out. The current methods for doing this rely on a strong assumption about the situation, which might not always be true. The researchers introduce a new way to do this evaluation that works even when the assumption doesn’t hold. They show that their method can work well in many situations and can give accurate results. |
