Abstract: Doing Real World Reinforcement Learning implies living with steep constraints on the sample complexity of solutions. Where is this viable? Where might it be viable in the near future? In the far future? How can we design a research program around identifying and building such solutions? In short, what are the missing elements we need to really make reinforcement learning more mundane and commonly applied than Supervised Learning? The potential is certainly there given the naturalness of RL compared to supervised learning, but the present is manifestly different. https://en.wikipedia.org/wiki/JohnLangford(computer_scientist)