  We study the prophet secretary problem, a well-studied variant of the classic
prophet inequality, where values are drawn from independent known distributions
but arrive in uniformly random order. Upon seeing a value at each step, the
decision-maker has to either select it and stop or irrevocably discard it.
Traditionally, the chosen benchmark is the expected reward of the prophet, who
knows all the values in advance and can always select the maximum one. %% In
this work, we study the prophet secretary problem against a less pessimistic
but equally well-motivated benchmark; the \emph{online} optimal. Here, the main
goal is to find polynomial-time algorithms that guarantee near-optimal expected
reward. As a warm-up, we present a quasi-polynomial time approximation scheme
(QPTAS) achieving a $(1-\e)$-approximation in $O(n^{\text{poly} \log n\cdot
f(\e)})$ time through careful discretization and non-trivial bundling
processes. Using the toolbox developed for the QPTAS, coupled with a novel
\emph{frontloading} technique that enables us to reduce the number of decisions
we need to make, we are able to remove the dependence on $n$ in the exponent
and obtain a polynomial time approximation scheme (PTAS) for this problem.
