@inproceedings{feec5654abb244db957ec0d746d398c3,
title = "pH-RL: A Personalization Architecture to Bring Reinforcement Learning to Health Practice: A Personalization Architecture to Bring Reinforcement Learning to Health Practice",
abstract = "While reinforcement learning (RL) has proven to be the approach of choice for tackling many complex problems, it remains challenging to develop and deploy RL agents in real-life scenarios successfully. This paper presents pH-RL (personalization in e-Health with RL), a general RL architecture for personalization to bring RL to health practice. pH-RL allows for various levels of personalization in health applications and allows for online and batch learning. Furthermore, we provide a general-purpose implementation framework that can be integrated with various healthcare applications. We describe a step-by-step guideline for the successful deployment of RL policies in a mobile application. We implemented our open-source RL architecture and integrated it with the MoodBuster mobile application for mental health to provide messages to increase daily adherence to the online therapeutic modules. We then performed a comprehensive study with human participants over a sustained period. Our experimental results show that the developed policies learn to select appropriate actions consistently using only a few days{\textquoteright} worth of data. Furthermore, we empirically demonstrate the stability of the learned policies during the study.",
author = "{el Hassouni}, Ali and Mark Hoogendoorn and Marketa Ciharova and Annet Kleiboer and Khadicha Amarti and Vesa Muhonen and Heleen Riper and Eiben, {A. E.}",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021 ; Conference date: 04-10-2021 Through 08-10-2021",
year = "2022",
doi = "https://doi.org/10.1007/978-3-030-95467-3_20",
language = "English",
isbn = "9783030954666",
volume = "1",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "265--280",
editor = "Giuseppe Nicosia and Varun Ojha and {La Malfa}, Emanuele and {La Malfa}, Gabriele and Giorgio Jansen and Pardalos, {Panos M.} and Giovanni Giuffrida and Renato Umeton",
booktitle = "Machine Learning, Optimization, and Data Science",
address = "Germany",
}