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arXiv · 2111.00633

Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning

Yuanzhi Li·Ruosong Wang·Lin F. Yang
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Abstract

Recently there is a surge of interest in understanding the horizon-dependence of the sample complexity in reinforcement learning (RL). Notably, for an RL environment with horizon length $H$, previous work have shown that there is a probably approximately correct (PAC) algorithm that learns an $O(1)$-optimal policy using $\mathrm{polylog}(H)$ episodes of environment interactions when the number of states and actions is fixed. It is yet unknown whether the $\mathrm{polylog}(H)$ dependence is necessary or not. In this work, we resolve this question by developing an algorithm that achieves the same PAC guarantee while using only $O(1)$ episodes of environment interactions, completely settling the horizon-dependence of the sample complexity in RL. We achieve this bound by (i) establishing a connection between value functions in discounted and finite-horizon Markov decision processes (MDPs) and (ii) a novel perturbation analysis in MDPs. We believe our new techniques are of independent interest and could be applied in related questions in RL.

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BibTeXRIS

Yuanzhi Li, Ruosong Wang, Lin F. Yang. 2021-11-01. Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning. https://arxiv.org/abs/2111.00633

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