A fundamental goal of artificial intelligence is to create agents that learn to make good decisions as they interact with a stochastic environment. Some of the most exciting and valuable potential applications involve systems that interact directly with humans, such as intelligent tutoring systems or medical support software. In these cases, minimizing the amount of experience needed by an algorithm to learn to make good decisions is highly important, as each decision, good or bad, is impacting a real person. I will describe our research on tackling this challenge, including transfer learning across sequential decision making tasks, as well as its relevance to improving educational tools.