Towards Deployable RL -- What's Broken with RL Research and a Potential Fix
This work critiques the RL community's direction for failing to produce economically viable and practical solutions, which is a problem for researchers and practitioners seeking real-world applications.
The paper identifies endemic difficulties in current reinforcement learning research that hinder the development of deployable RL systems, and proposes a potential fix to address these issues.
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.