  In the wake of the vast population of smart device users worldwide, mobile
health (mHealth) technologies are hopeful to generate positive and wide
influence on people's health. They are able to provide flexible, affordable and
portable health guides to device users. Current online decision-making methods
for mHealth assume that the users are completely heterogeneous. They share no
information among users and learn a separate policy for each user. However,
data for each user is very limited in size to support the separate online
learning, leading to unstable policies that contain lots of variances. Besides,
we find the truth that a user may be similar with some, but not all, users, and
connected users tend to have similar behaviors. In this paper, we propose a
network cohesion constrained (actor-critic) Reinforcement Learning (RL) method
for mHealth. The goal is to explore how to share information among similar
users to better convert the limited user information into sharper learned
policies. To the best of our knowledge, this is the first online actor-critic
RL for mHealth and first network cohesion constrained (actor-critic) RL method
in all applications. The network cohesion is important to derive effective
policies. We come up with a novel method to learn the network by using the warm
start trajectory, which directly reflects the users' property. The optimization
of our model is difficult and very different from the general supervised
learning due to the indirect observation of values. As a contribution, we
propose two algorithms for the proposed online RLs. Apart from mHealth, the
proposed methods can be easily applied or adapted to other health-related
tasks. Extensive experiment results on the HeartSteps dataset demonstrates that
in a variety of parameter settings, the proposed two methods obtain obvious
improvements over the state-of-the-art methods.
