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Model-based Bootstrap for Offline Policy Evaluation in Tabular Reinforcement Learning

This story is from 2026-09-18. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.20389v1 Announce Type: new Abstract: Offline policy evaluation (OPE) is crucial in high-stakes reinforcement learning applications, where new policies must be assessed reliably before deployment. In such settings, point estimates alone are insufficient; principled uncertainty quantificat…

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  1. 2026-09-18 04:00 · arXiv stat.ML
    Model-based Bootstrap for Offline Policy Evaluation in Tabular Reinforcement Learning

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