State Relevance for Off-Policy Evaluation
Machine Learning
2021-09-15 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.2109.06310,
title = {State Relevance for Off-Policy Evaluation},
author = {Simon P. Shen and Yecheng Jason Ma and Omer Gottesman and Finale Doshi-Velez},
journal= {arXiv preprint arXiv:2109.06310},
year = {2021}
}
Comments
ICML 2021