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.
Then µ∗ n = n−1X i=0 (ν∗ nK ∗i n )(s, a), hence µ∗ n −nˆp∗(a) s = Pn−1 i=0 [(ν∗ nK ∗i n )(s, a)−ˆp∗(a) s ]
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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.