Devising robust Multi-Armed Bandit algorithms in the presence of non-stationarities and long-range dependencies

在存在非平稳性和远程依赖性的情况下设计稳健的 Multi-Armed Bandit 算法

• 批准号: 2437073
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2437073
• 关键词: Devising; robust; Multi; Armed; Bandit

The Multi-Armed Bandit (MAB) problem is one of the most central instances of sequential decision making under uncertainty, which plays a key role in online learning and optimization. MABs arise in a variety of modern real-world applications, such as online advertisement, Internet routing, and sequential portfolio selection, only to name a few. In this problem, a forecaster aims to maximize the expected sum of the rewards actively collected from unknown processes. MABs are typically studied under the assumption that the rewards are i.i.d.. However, this assumption does not necessarily hold in many practical situations. The objective of this project is to analyze the possibilities and limitations of more challenging, yet more realistic (restless) MAB settings, where the reward distributions may exhibit long-range dependencies and may possess potential non-stationarities. As part of the project, novel MAB strategies with good performance guarantees will be sought, and applications to real-world problems will be explored.

多臂强盗（MAB）问题是不确定性下连续决策的最中心实例之一，在在线学习和优化中起着关键作用。 mabs出现在各种现代现实世界中，例如在线广告，互联网路由和连续组合选择，仅举几例。在此问题中，预报掌握的目的是最大程度地提高从未知过程中积极收集的奖励的预期总和。通常根据奖励是I.I.D的假设来研究mAb。但是，在许多实际情况下，此假设不一定存在。该项目的目的是分析更具挑战性，更现实（不安的）单元格环境的可能性和局限性，在这种情况下，奖励分布可能会表现出远距离的依赖性并可能具有潜在的非平稳性。作为该项目的一部分，将寻求具有良好性能保证的新型MAB策略，并将探讨对现实世界中问题的应用。