mlsanityData to 13 Oct 2026

Learning Upper Lower Value Envelopes to Shape Online RL: A Principled Approach

arXiv 2510.19528 ↗First posted 22 Oct 2025stat.ML cs.LG math.STcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

We investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding. Our study centers on how to learn and apply value envelopes within this context. To this end, we introduce a principled two-stage framework: the first stage uses offline data to derive upper and lower bounds on value functions, while the second incorporates these learned bounds into online algorithms. Our method extends prior work by decoupling the upper and lower bounds, enabling more flexible and tighter approximations. In contrast to approaches that rely on fixed shaping functions, our envelopes are data-driven and explicitly modeled as random variables, with a filtration argument ensuring independence across phases. The analysis establishes high-probability regret bounds determined by two interpretable quantities, thereby providing a formal bridge between offline pre-training and online fine-tuning. Empirical results on tabular MDPs demonstrate substantial regret reductions compared with both UCBVI and prior methods while remaining competitive with related approaches.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 30 of its 45 references that are in the map. First posted on arXiv 22 Oct 2025.

Builds on

30 of 45 references.

References · 30

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18 more
  1. 2018Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Costcited by 79
  2. 2020Towards Playing Full MOBA Games with Deep Reinforcement Learningcited by 78
  3. 2019Corruption-robust exploration in episodic reinforcement learningcited by 72
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  6. 2021Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrapcited by 48
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  8. 2023Settling the Sample Complexity of Online Reinforcement Learningcited by 45
  9. 2021Heuristic-Guided Reinforcement Learningcited by 41
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  12. 2024Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learningcited by 22
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  15. 2021Can Q-Learning be Improved with Advice?cited by 13
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  17. 2025Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysiscited by 3
  18. 2024Online Reinforcement Learning with Passive Memorycited by 1

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