# Group-driven Reinforcement Learning for Personalized mHealth Intervention

arXiv: https://arxiv.org/abs/1708.04001 · HTML: https://mlsanity.com/paper/1708.04001/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2017.

## Built on by (most cited first, 6 of 6)

- [Deep reinforcement learning in medical imaging: A literature review](https://mlsanity.com/paper/2103.05115/) (2021)
- [Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines](https://mlsanity.com/paper/2206.03944/) (2022)
- [RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning](https://mlsanity.com/paper/2212.01737/) (2022)
- [Explaining RL Decisions with Trajectories](https://mlsanity.com/paper/2305.04073/) (2023)
- [Oralytics Reinforcement Learning Algorithm](https://mlsanity.com/paper/2406.13127/) (2024)
- [pH-RL: A personalization architecture to bring reinforcement learning to health practice](https://mlsanity.com/paper/2103.15908/) (2021)

## Abstract (arXiv)

Due to the popularity of smartphones and wearable devices nowadays, mobile health (mHealth) technologies are promising to bring positive and wide impacts on people's health. State-of-the-art decision-making methods for mHealth rely on some ideal assumptions. Those methods either assume that the users are completely homogenous or completely heterogeneous. However, in reality, a user might be similar with some, but not all, users. In this paper, we propose a novel group-driven reinforcement learning method for the mHealth. We aim to understand how to share information among similar users to better convert the limited user information into sharper learned RL policies. Specifically, we employ the K-means clustering method to group users based on their trajectory information similarity and learn a shared RL policy for each group. Extensive experiment results have shown that our method can achieve clear gains over the state-of-the-art RL methods for mHealth.