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This paper is primarily about offline reinforcement learning, so it passes the topic gate. Its main contribution is a Bayesian model-based method (SOReL) and an extension (TOReL) that let practitioners tune hyperparameters and estimate initial online performance without any online interaction, which current offline RL methods often quietly depend on. The regret analysis claiming minimax-optimal parametric rates is theoretical and holds only under stated regularity conditions, so readers should check those assumptions against their own settings before relying on it.
Fully Offline Reinforcement Learning
arXiv:2505.22442v4 Announce Type: replace 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2505.22442 [cs.LG]
(or arXiv:2505.22442v4 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2505.22442 Focus to learn more arXiv-issued DOI via DataCite
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