Effective Warm Start for the Online Actor-Critic Reinforcement Learning based mHealth Intervention

TL;DR

This paper introduces a warm start strategy for online reinforcement learning in mHealth interventions, leveraging previous data and decision rules to improve early and overall performance.

Contribution

It proposes a novel online RL approach that uses prior study data and decision rules for initialization, enhancing early learning and overall effectiveness.

Findings

Significant improvement in early performance compared to existing methods

Faster convergence to effective policies in online learning

Enhanced user engagement in mHealth applications

Abstract

Online reinforcement learning (RL) is increasingly popular for the personalized mobile health (mHealth) intervention. It is able to personalize the type and dose of interventions according to user's ongoing statuses and changing needs. However, at the beginning of online learning, there are usually too few samples to support the RL updating, which leads to poor performances. A delay in good performance of the online learning algorithms can be especially detrimental in the mHealth, where users tend to quickly disengage with the mHealth app. To address this problem, we propose a new online RL methodology that focuses on an effective warm start. The main idea is to make full use of the data accumulated and the decision rule achieved in a former study. As a result, we can greatly enrich the data size at the beginning of online learning in our method. Such case accelerates the online learning process for new users to achieve good performances not only at the beginning of online learning but also through the whole online learning process. Besides, we use the decision rules achieved in a previous study to initialize the parameter in our online RL model for new users. It provides a good initialization for the proposed online RL algorithm. Experiment results show that promising improvements have been achieved by our method compared with the state-of-the-art method.