# pH-RL: A personalization architecture to bring reinforcement learning to health practice

arXiv: https://arxiv.org/abs/2103.15908 · HTML: https://mlsanity.com/paper/2103.15908/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [Acme: A Research Framework for Distributed Reinforcement Learning](https://mlsanity.com/paper/2006.00979/) (2020)
- [A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions](https://mlsanity.com/paper/1804.03592/) (2018)
- [Group-driven Reinforcement Learning for Personalized mHealth Intervention](https://mlsanity.com/paper/1708.04001/) (2017)

## Built on by (most cited first, 1 of 1)

- [Offline Policy Comparison under Limited Historical Agent-Environment Interactions](https://mlsanity.com/paper/2106.03934/) (2021)

## Abstract (arXiv)

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.