Data efficiency poses an impediment to carrying the success of reinforcement learning agents over from simulated to real environments. The design of data-efficient agents calls for a deeper understanding of information acquisition and representation. I will discuss concepts and a regret bound that together offer principled guidance. The bound sheds light on questions of what information to seek, how to seek that information, and what information to retain. To illustrate concepts, I will also share results generated by simple agents that build on them.