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Reinforcement learning (RL) has demonstrated great potential, but iscurrently full of overhyping and pipe dreams. We point to some difficultieswith current research which we feel are endemic to the direction taken by thecommunity. 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 iseconomically viable. We also propose a potential fix to some of thedifficulties of the field.

Towards Deployable RL -- What's Broken with RL Research and a Potential Fix