  Reinforcement learning agents have demonstrated remarkable achievements in
simulated environments. Data efficiency poses an impediment to carrying this
success over to real environments. The design of data-efficient agents calls
for a deeper understanding of information acquisition and representation. We
discuss concepts and regret analysis that together offer principled guidance.
This line of thinking sheds light on questions of what information to seek, how
to seek that information, and what information to retain. To illustrate
concepts, we design simple agents that build on them and present computational
results that highlight data efficiency.
