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）问题是不确定条件下序贯决策问题中最核心的问题之一，在在线学习和优化中起着关键作用。MAB出现在各种现代现实世界的应用中，例如在线广告，互联网路由和顺序投资组合选择，仅举几例。在这个问题中，预测者的目标是最大化从未知过程中积极收集的奖励的预期总和。MAB通常在假设奖励是独立同分布的情况下进行研究。然而，这一假设在许多实际情况下并不一定成立。该项目的目标是分析更具挑战性，但更现实（不安）的MAB设置的可能性和局限性，其中奖励分布可能表现出长期依赖性，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，并将探索实际问题的应用。