我们个性化了吗?利用重采样评估在线强化学习算法的个性化程度

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Susobhan Ghosh, Raphael Kim, Prasidh Chhabria, Raaz Dwivedi, Predrag Klasnja, Peng Liao, Kelly Zhang, Susan Murphy
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引用次数: 0

摘要

越来越多的人开始关注在数字健康领域使用强化学习(RL)来个性化治疗顺序,以支持用户采取更健康的行为。此类顺序决策问题涉及根据用户的背景(如先前的活动水平、位置等)决定何时治疗和如何治疗。在线 RL 是一种很有前景的数据驱动型方法,它可以根据每个用户的历史反应进行学习,并利用这些知识来个性化这些决策。然而,要决定是否应将 RL 算法纳入实际部署的 "优化 "干预中,我们必须评估表明 RL 算法确实在为用户提供个性化治疗的数据证据。由于 RL 算法的随机性,人们可能会产生一种错觉,以为它正在某些状态下学习,并利用这种学习提供特定的治疗。我们使用了个性化的工作定义,并介绍了一种基于重采样的方法,用于研究 RL 算法所表现出的个性化是否是 RL 算法随机性的产物。我们通过分析一项名为 HeartSteps 的体育锻炼临床试验的数据来说明我们的方法,其中包括在线 RL 算法的使用。我们展示了我们的方法如何在所有用户以及研究中的特定用户中增强算法个性化的数据驱动真实广告。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling

Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling

There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such sequential decision-making problems involve decisions about when to treat and how to treat based on the user’s context (e.g., prior activity level, location, etc.). Online RL is a promising data-driven approach for this problem as it learns based on each user’s historical responses and uses that knowledge to personalize these decisions. However, to decide whether the RL algorithm should be included in an “optimized” intervention for real-world deployment, we must assess the data evidence indicating that the RL algorithm is actually personalizing the treatments to its users. Due to the stochasticity in the RL algorithm, one may get a false impression that it is learning in certain states and using this learning to provide specific treatments. We use a working definition of personalization and introduce a resampling-based methodology for investigating whether the personalization exhibited by the RL algorithm is an artifact of the RL algorithm stochasticity. We illustrate our methodology with a case study by analyzing the data from a physical activity clinical trial called HeartSteps, which included the use of an online RL algorithm. We demonstrate how our approach enhances data-driven truth-in-advertising of algorithm personalization both across all users as well as within specific users in the study.

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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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