  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.
