We generalize the multiple-play multi-armed bandits (MP-MAB) problem with a
shareable arm setting, in which several plays can share the same arm.
Furthermore, each shareable arm has a finite reward capacity and a ''per-load''
reward distribution, both of which are unknown to the learner. The reward from
a shareable arm is load-dependent, which is the "per-load" reward multiplying
either the number of plays pulling the arm, or its reward capacity when the
number of plays exceeds the capacity limit. When the "per-load" reward follows
a Gaussian distribution, we prove a sample complexity lower bound of learning
the capacity from load-dependent rewards and also a regret lower bound of this
new MP-MAB problem. We devise a capacity estimator whose sample complexity
upper bound matches the lower bound in terms of reward means and capacities. We
also propose an online learning algorithm to address the problem and prove its
regret upper bound. This regret upper bound's first term is the same as regret
lower bound's, and its second and third terms also evidently correspond to
lower bound's. Extensive experiments validate our algorithm's performance and
also its gain in 5G & 4G base station selection.

研究了多臂赌博机问题中的可共享臂设置，提出了一个用于评估可共享臂容量的估计器以及一个在线学习算法，并验证了其在 5G 和 4G 基站选择中的有效性。