Variance Reduction Methods for Sublinear Reinforcement Learning
Not on the map. Why?
Abstract (arXiv)
There is a technical issue in the analysis that is not easily fixable. We, therefore, withdraw the submission. Sorry for the inconvenience.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Feb 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 3.
6 papers.
- 2021MADE: Exploration via Maximizing Deviation from Explored Regionscited by 40
- 2024Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decompositioncited by 8
- 2025Gap-Dependent Bounds for Federated Q-learningcited by 4
- 2022Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPscited by 2
- 2025Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learningcited by 2
- 2025Q-Learning with Fine-Grained Gap-Dependent Regretcited by 1
- 2025Q-Learning with Fine-Grained Gap-Dependent Regret8 Oct 2025
- 2025Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning5 Jun 2025
- 2025Gap-Dependent Bounds for Federated Q-learning5 Feb 2025
- 2024Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition10 Oct 2024
- 2022Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs18 May 2022
- 2021MADE: Exploration via Maximizing Deviation from Explored Regions18 Jun 2021