There is increasing excitement about reinforcement learning--a subarea of machine learning for enabling an agent to learn to make good decisions. Yet numerous questions and challenges remain for reinforcement learning to help support progress in applications that involve interacting with people, like education, consumer marketing and healthcare. I will discuss our work on some of the technical challenges that arise in this pursuit, including sample efficiency, counterfactual reasoning, robustness, and applications to health and education.