Reproduce “Statistical Foundations of Reinforcement Learning: A Non-Asymptotic Perspective” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yuxin Chen, Yuejie Chi, Jianqing Fan, Gen Li, Yuting Wei, Yuling Yan
The monograph synthesizes finite-sample guarantees for tabular reinforcement learning across generative-model, online, and offline data settings, comparing algorithmic upper bounds with minimax lower bounds. It explains how sampling conditions shape effective methods and uncertainty strategies. Online, MVP achieves high-probability regret O(min{√(SAH³K), HK}) for every K ≥ 1, matching the minimax lower bound up to logarithmic factors in finite-horizon nonstationary MDPs.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.