Bayesian offline RL can select hyperparameters and estimate deployed regret from posterior predictive uncertainty, with a regret bound at the parametric minimax rate.
Information-theoretic characterization of bayes performance and the choice of priors in parametric and nonparametric problems
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Fully Offline Reinforcement Learning
Bayesian offline RL can select hyperparameters and estimate deployed regret from posterior predictive uncertainty, with a regret bound at the parametric minimax rate.