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' worth of data. Furthermore, we empirically demonstrate the stability of the learned policies during the study.
@article{arxiv.2103.15908,
title = {pH-RL: A personalization architecture to bring reinforcement learning to health practice},
author = {Ali el Hassouni and Mark Hoogendoorn and Marketa Ciharova and Annet Kleiboer and Khadicha Amarti and Vesa Muhonen and Heleen Riper and A. E. Eiben},
journal= {arXiv preprint arXiv:2103.15908},
year = {2021}
}