# Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions

arXiv: https://arxiv.org/abs/2605.19208 · HTML: https://mlsanity.com/paper/2605.19208/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 19 May 2026.

## Placed in

- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 9 of 9)

- [Batch Policy Learning under Constraints](https://mlsanity.com/paper/1903.08738/) (2019)
- [A Review of Off-Policy Evaluation in Reinforcement Learning](https://mlsanity.com/paper/2212.06355/) (2022)
- [The Sample-Complexity of General Reinforcement Learning](https://mlsanity.com/paper/1308.4828/) (2013)
- [Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control](https://mlsanity.com/paper/1806.06931/) (2018)
- [Distributional data analysis of accelerometer data from the NHANES database using nonparametric survey regression models](https://mlsanity.com/paper/2104.01165/) (2021)
- [Flexible Functional Treatment Effect Estimation](https://mlsanity.com/paper/2309.08039/) (2023)
- [Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare](https://mlsanity.com/paper/1911.07084/) (2019)
- [Distributional outcome regression via quantile functions and its application to modelling continuously monitored heart rate and physical activity](https://mlsanity.com/paper/2301.11399/) (2023)
- [Exploring Optimal Control With Observations at a Cost](https://mlsanity.com/paper/2006.15757/) (2020)

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

- [Finite-Sample Theory for Fitted Q-Iteration When Actions Are Functions](https://mlsanity.com/paper/2609.36390/) (2026)

## Abstract (arXiv)

Physical activity (PA) plays an important role in maintaining and improving health. Daily steps have been a key PA measure that is easily accessible with common wearable devices. However, methods are lacking to recommend a personalized optimal distribution of daily steps over a period of time for the best of certain health biomarkers. In this paper, we fill this void based on the data from the All of Us Research Program which includes months of step counts as well as repeated measurements of key health biomarkers. We develop a new offline reinforcement learning (RL) algorithm to learn personalized and optimal PA distributions associated with cardiometabolic risk, where the action is a function representing the daily step distribution over a period of time. Simulation studies demonstrate the advantage of the proposed approach over existing continuous-action RL methods. The learned optimal policy from the All of Us data generally suggests people take more daily steps and also follow a more consistent pattern of PA over time while offering tailored recommendations for subgroups in blood glucose level, body mass index, blood pressure, age, and sex.