  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.
