_:n8798c06f26af4e279b9770aef7c41418xb33
<http://schema.org/description>
"While reinforcement learning (RL) has proven to be the approach of choice for\ntackling many complex problems, it remains challenging to develop and deploy RL\nagents in real-life scenarios successfully. This paper presents pH-RL\n(personalization in e-Health with RL) a general RL architecture for\npersonalization to bring RL to health practice. pH-RL allows for various levels\nof personalization in health applications and allows for online and batch\nlearning. Furthermore, we provide a general-purpose implementation framework\nthat can be integrated with various healthcare applications. We describe a\nstep-by-step guideline for the successful deployment of RL policies in a mobile\napplication. We implemented our open-source RL architecture and integrated it\nwith the MoodBuster mobile application for mental health to provide messages to\nincrease daily adherence to the online therapeutic modules. We then performed a\ncomprehensive study with human participants over a sustained period. Our\nexperimental results show that the developed policies learn to select\nappropriate actions consistently using only a few days' worth of data.\nFurthermore, we empirically demonstrate the stability of the learned policies\nduring the study."
<https://deepai.org/profile/mark-hoogendoorn>