Reinforcement learning agents have demonstrated remarkable achievements in simulated environments. Data efficiency, however, significantly impedes carrying this success over to real environments. The design of data-efficient agents that address this problem calls for a deeper understanding of information acquisition and representation. This tutorial offers a framework that can guide associated agent design decisions. This framework is inspired in...