  This paper targets a variant of the stochastic multi-armed bandit problem
called good arm identification (GAI). GAI is a pure-exploration bandit problem
with the goal to output as many good arms using as few samples as possible,
where a good arm is defined as an arm whose expected reward is greater than a
given threshold. In this work, we propose DGAI - a differentiable good arm
identification algorithm to improve the sample complexity of the
state-of-the-art HDoC algorithm in a data-driven fashion. We also showed that
the DGAI can further boost the performance of a general multi-arm bandit (MAB)
problem given a threshold as a prior knowledge to the arm set. Extensive
experiments confirm that our algorithm outperform the baseline algorithms
significantly in both synthetic and real world datasets for both GAI and MAB
tasks.
