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首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >A non-parametric solution to the multi-armed bandit problem with covariates
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A non-parametric solution to the multi-armed bandit problem with covariates

机译：具有协变量的多武装强盗问题的非参数解决方案

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In recent years, the multi-armed bandit problem regains popularity especially for the case with covariates since it has new applications in customized services such as personalized medicine. To deal with the bandit problem with covariates, a policy called binned subsample mean comparison that decomposes the original problem into some proper classic bandit problems is introduced. The growth rate in a setting that the reward of each arm depends on observable covariates is studied accordingly. When rewards follow an exponential family, it can be shown that the regret of the proposed method can achieve the nearly optimal growth rate. Simulations show that the proposed policy has the competitive performance compared with other policies. (C) 2020 Elsevier B.V. All rights reserved.
机译：近年来，多武装匪徒问题重新流行起来，尤其是对于具有协变量的情况，因为它在个性化医疗等定制服务中有了新的应用。为了解决带有协变量的bandit问题，引入了一种称为bined子样本均值比较的策略，将原始问题分解为一些适当的经典bandit问题。在每个手臂的奖励取决于可观测协变量的情况下，相应地研究了增长率。当报酬服从指数族时，可以证明所提出的方法可以获得接近最优的增长率。仿真结果表明，与其他策略相比，该策略具有更好的性能。（C） 2020爱思唯尔B.V.版权所有。

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