  In mobile health (mHealth) smart devices deliver behavioral treatments
repeatedly over time to a user with the goal of helping the user adopt and
maintain healthy behaviors. Reinforcement learning appears ideal for learning
how to optimally make these sequential treatment decisions. However,
significant challenges must be overcome before reinforcement learning can be
effectively deployed in a mobile healthcare setting. In this work we are
concerned with the following challenges: 1) individuals who are in the same
context can exhibit differential response to treatments 2) only a limited
amount of data is available for learning on any one individual, and 3)
non-stationary responses to treatment. To address these challenges we
generalize Thompson-Sampling bandit algorithms to develop IntelligentPooling.
IntelligentPooling learns personalized treatment policies thus addressing
challenge one. To address the second challenge, IntelligentPooling updates each
user's degree of personalization while making use of available data on other
users to speed up learning. Lastly, IntelligentPooling allows responsivity to
vary as a function of a user's time since beginning treatment, thus addressing
challenge three. We show that IntelligentPooling achieves an average of 26%
lower regret than state-of-the-art. We demonstrate the promise of this approach
and its ability to learn from even a small group of users in a live clinical
trial.

Machine Learning;Computers and Society;Machine Learning