Variance-Aware Fine-Grained Gap-Dependent Bounds for Online Reinforcement Learning

Abstract:We study model-free online reinforcement learning (RL) for episodic tabular Markov decision processes, focusing on both gap-dependent regret and policy switching cost. While fine-grained gap-dependent analysis has been established for model-free RL algorithms using Hoeffding-type exploration bonuses, such results for model-free algorithms with variance-based exploration bonuses remain unknown, despite their superior worst-case and coarse-grained gap-dependent guarantees. In this paper, we resolve this open problem by establishing the first fine-grained gap-dependent regret upper bound for UCB-Bernstein+, a refined UCB-Bernstein algorithm, in variance-aware model-free online RL. Moreover, by integrating a stage-wise policy update design into our fine-grained framework and using refined variance-based bonuses, we achieve the best-known gap-dependent local switching cost to date. In addition, our analysis yields improved worst-case guarantees for both regret and local switching cost over the original UCB-Bernstein algorithm. Numerical experiments further demonstrate that UCB-Bernstein+ achieves favorable empirical performance in both regret and local switching cost.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2610.04752 [stat.ML]

(or arXiv:2610.04752v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.04752 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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