  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.
