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arXiv · 2505.22442

Fully Offline Reinforcement Learning

Mattie Fellows·Clarisse Wibault·Uljad Berdica·Johannes Forkel·Maike Osborne·Jakob N. Foerster
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Abstract

Offline RL (ORL) promises safe and sample-efficient deployment but existing methods rely on undocumented online interactions for hyperparameter tuning and lack reliable fully offline estimates of initial online performance. We introduce SOReL, a fully offline Bayesian model-based RL method that learns a posterior over dynamics, estimates policy value via predictive uncertainty, and enables complete offline hyperparameter selection. We further propose TOReL, which extends this tuning framework to arbitrary model-free and model-based ORL algorithms. We provide a regret analysis showing that Bayesian offline RL achieves the minimax-optimal parametric rate under standard regularity conditions. Together, our methods establish a practical and theoretically grounded framework for fully offline RL.

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Mattie Fellows, Clarisse Wibault, Uljad Berdica, Johannes Forkel, Maike Osborne, Jakob N. Foerster. 2025-05-28. Fully Offline Reinforcement Learning. https://arxiv.org/abs/2505.22442

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