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arXiv · 2101.09847

High-Confidence Off-Policy (or Counterfactual) Variance Estimation

Yash Chandak·Shiv Shankar·Philip S. Thomas
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Abstract

Many sequential decision-making systems leverage data collected using prior policies to propose a new policy. For critical applications, it is important that high-confidence guarantees on the new policy's behavior are provided before deployment, to ensure that the policy will behave as desired. Prior works have studied high-confidence off-policy estimation of the expected return, however, high-confidence off-policy estimation of the variance of returns can be equally critical for high-risk applications. In this paper, we tackle the previously open problem of estimating and bounding, with high confidence, the variance of returns from off-policy data

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Yash Chandak, Shiv Shankar, Philip S. Thomas. 2021-01-25. High-Confidence Off-Policy (or Counterfactual) Variance Estimation. https://arxiv.org/abs/2101.09847

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