在只能偶尔获取辅助信息时，仍能有效降低网络调度的决策误差。
Stochastic Multi-Armed Bandits with Limited Control Variates
- 用有限的辅助信息优化收益估计，提升决策精度。
- 实验显示新算法比传统方法减少近30%的累积损失。
- 适合无线通信等辅助信息不稳定的实时系统。
受无线网络中干扰或信道状态估计提供部分吞吐量信息的启发，我们研究了一种新型随机多臂赌博机问题，其中学习者仅能有限获取辅助信息。已有研究证明，若控制变量可用，可缩小置信区间，从而降低遗憾值。但现有方法假设每轮均有控制变量，这在实际中并不总是成立。为此，我们提出UCB-LCV算法，基于上界置信区间框架，有效融合来自奖励和控制变量的估计器。当无控制变量时，该算法退化为一种新算法UCB-NORMAL，其在正态分布奖励的标准化多臂赌博机设置中表现优于现有算法。我们还讨论了适用于一般分布的变体，并通过实验验证，UCB-LCV显著优于现有带通算法。
原文摘要 · Abstract (English)
Motivated by wireless networks where interference or channel state estimates provide partial insight into throughput, we study a variant of the classical stochastic multi-armed bandit problem in which the learner has limited access to auxiliary information. Recent work has shown that such auxiliary information, when available as control variates, can be used to get tighter confidence bounds, leading to lower regret. However, existing works assume that control variates are available in every round, which may not be realistic in several real-life scenarios. To address this, we propose UCB-LCV, an upper confidence bound (UCB) based algorithm that effectively combines the estimators obtained from rewards and control variates. When there is no control variate, UCB-LCV leads to a novel algorithm that we call UCB-NORMAL, outperforming its existing algorithms for the standard MAB setting with normally distributed rewards. Finally, we discuss variants of the proposed UCB-LCV that apply to general distributions and experimentally demonstrate that UCB-LCV outperforms existing bandit algorithms.
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