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Spotlight
State Relevance for Off-Policy Evaluation
Simon Shen · Jason Yecheng Ma · Omer Gottesman · Finale Doshi-Velez
Importance sampling-based estimators for off-policy evaluation (OPE) are valued for their simplicity, unbiasedness, and reliance on relatively few assumptions. However, the variance of these estimators is often high, especially when trajectories are of different lengths. In this work, we introduce Omitting-States-Irrelevant-to-Return Importance Sampling (OSIRIS), an estimator which reduces variance by strategically omitting likelihood ratios associated with certain states. We formalize the conditions under which OSIRIS is unbiased and has lower variance than ordinary importance sampling, and we demonstrate these properties empirically.
Author Information
Simon Shen (Harvard University)
Jason Yecheng Ma (University of Pennsylvania)
Omer Gottesman (Harvard University)
Finale Doshi-Velez (Harvard University)
Related Events (a corresponding poster, oral, or spotlight)
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2021 Poster: State Relevance for Off-Policy Evaluation »
Wed. Jul 21st 04:00 -- 06:00 PM Room
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