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Model-based Bootstrap for Offline Policy Evaluation in Tabular Reinforcement Learning
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 quantification, such as confidence intervals and variance es
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