Partial monitoring is an expressive framework for sequential decision-making with an abundance of applications, including graph-structured and dueling bandits, dynamic pricing and transductive feedback models. We survey and extend recent results on the linear formulation of partial monitoring that naturally generalizes the standard linear bandit setting. The main result is that a single algorithm, information-directed sampling (IDS), is (nearly) worst-case rate optimal in all finite-action games. We present a simple and unified analysis of stochastic partial monitoring, and further extend the model to the contextual and kernelized setting.

翻译：部分监控是一种表达力极强的序贯决策框架，广泛应用于图结构赌博机、排序赌博机、动态定价及转导反馈模型等众多场景。本文综述并拓展了部分监控线性形式的最新研究成果，该形式自然地推广了标准线性赌博机设置。主要结论为：单一算法——信息导向采样（IDS）——在所有有限动作博弈中均达到（近乎）最坏情况下的最优速率。我们提出了一种简洁统一的随机部分监控分析方法，并将该模型进一步拓展至上下文和核化设置。