Web: http://arxiv.org/abs/2005.12900

Sept. 19, 2022, 1:12 a.m. | Gen Li, Yuting Wei, Yuejie Chi, Yuxin Chen

stat.ML updates on arXiv.org arxiv.org

This paper is concerned with the sample efficiency of reinforcement learning,
assuming access to a generative model (or simulator). We first consider
$\gamma$-discounted infinite-horizon Markov decision processes (MDPs) with
state space $\mathcal{S}$ and action space $\mathcal{A}$. Despite a number of
prior works tackling this problem, a complete picture of the trade-offs between
sample complexity and statistical accuracy is yet to be determined. In
particular, all prior results suffer from a severe sample size barrier, in the
sense that their claimed …

arxiv breaking reinforcement reinforcement learning

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