Recent advance in health and technology has made mobile apps a viable approach to delivering behavioral interventions in areas including physical activity encouragement, smoking cessation, substance abuse prevention, and mental health management. Due to the chronic nature of most of the disorders and heterogeneity among mobile users, delivery of the interventions needs to be sequential and tailored to individual needs. We operationalize the sequential decision making via a policy that takes a mobile user’s past usage pattern and health status as input and outputs an app/intervention recommendation with the goal of optimizing the cumulative rewards of interest in an indefinite horizon setting. we propose a regularized greedy gradient Q-learning (RGGQ) method to tackle this estimation problem. The optimal policy is estimated via an algorithm that synthesizes the PGM and the GGQ algorithms, and its asymptotic properties are established.