A model-based bootstrap achieves distributional consistency for transition estimators in controlled Markov chains with unknown policies and yields asymptotically valid confidence intervals for offline policy evaluation and optimal policy recovery.
Thus ¯θi,j measures the rate at which the state process forgets its initial state–action pair, extending Dobrushin’s coefficients [Dobrushin, 1956] to the controlled setting
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Model-based Bootstrap of Controlled Markov Chains
A model-based bootstrap achieves distributional consistency for transition estimators in controlled Markov chains with unknown policies and yields asymptotically valid confidence intervals for offline policy evaluation and optimal policy recovery.