  Reinforcement learning (RL) has demonstrated great potential, but is
currently full of overhyping and pipe dreams. We point to some difficulties
with current research which we feel are endemic to the direction taken by the
community. To us, the current direction is not likely to lead to "deployable"
RL: RL that works in practice and can work in practical situations yet still is
economically viable. We also propose a potential fix to some of the
difficulties of the field.
