mlsanityData to 14 Oct 2026

A Refined Analysis of UCBVI

arXiv 2502.17370 ↗First posted 24 Feb 2025stat.ML cs.LGcited by 1

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Abstract (arXiv)

In this work, we provide a refined analysis of the UCBVI algorithm (Azar et al., 2017), improving both the bonus terms and the regret analysis. Additionally, we compare our version of UCBVI with both its original version and the state-of-the-art MVP algorithm. Our empirical validation demonstrates that improving the multiplicative constants in the bounds has significant positive effects on the empirical performance of the algorithms.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 9 references that are in the map. First posted on arXiv 24 Feb 2025.

Builds on

3 of 9 references.

References · 3

  1. 2017Minimax Regret Bounds for Reinforcement Learningcited by 503
  2. 2020Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisitedcited by 106
  3. 2023Settling the Sample Complexity of Online Reinforcement Learningcited by 45

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Learning in Markov Decision Processes with Exogenous Dynamicscited by 2