标题： UCB 算法针对指数分布 显示英文标题

标题： UCB Algorithm for Exponential Distributions

摘要： 本文介绍了一种用于多臂老虎机（MAB）问题的新算法。 该算法在认知网络相关主题（例如频谱感知和分配）中广受欢迎。 我们关注的是奖励呈指数分布的情况，这在处理瑞利衰落信道时很常见。 这种策略被称为乘性上置信界（MUCB），为每个可用臂分配一个效用指数，然后选择具有最高指数的臂。 对于每个臂，对应的指数等于一个乘法因子与该臂收集到的奖励样本均值的乘积。 我们证明了MUCB策略具有低复杂度且是渐进最优的。 显示英文摘要

摘要： We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB) problems. A machine learning paradigm popular within Cognitive Network related topics (e.g., Spectrum Sensing and Allocation). We focus on the case where the rewards are exponentially distributed, which is common when dealing with Rayleigh fading channels. This strategy, named Multiplicative Upper Confidence Bound (MUCB), associates a utility index to every available arm, and then selects the arm with the highest index. For every arm, the associated index is equal to the product of a multiplicative factor by the sample mean of the rewards collected by this arm. We show that the MUCB policy has a low complexity and is order optimal.