We study two-sided matching markets in which one side of the market (the players) does not have a priori knowledge about its preferences for the other side (the arms) and is required to learn its preferences from experience. Also, we assume the players have no direct means of communication. This model extends the standard stochastic multi-armed bandit framework to a decentralized multiple player setting with competition. We introduce a new algorithm for this setting that, over a time horizon $T$, attains $\mathcal{O}(\log(T))$ stable regret when preferences of the arms over players are shared, and $\mathcal{O}(\log(T)^2)$ regret when there are no assumptions on the preferences on either side.

翻译：我们研究的是双向匹配市场,其中市场一方(参与者)没有先验地了解对另一方(武器)的偏好,需要从经验中学习其偏好。此外,我们假设参与者没有直接的沟通手段。这个模式将标准的随机多武装强盗框架扩大到分散的多个竞争参与者。我们为这一环境引入了一种新的算法,在一段时间内,如果武器对另一方(武器)的偏好是共享的,那么在一定的时间范围内,如果武器对后者的偏好是共享的,则获得$\mathcal{O}(log(T))$的稳定遗憾,如果对任何一方的偏好没有假设,则得到$\mathcal{O}(log(T)2)$的遗憾。