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

arXiv: https://arxiv.org/abs/2301.01320 · HTML: https://mlsanity.com/paper/2301.01320/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 2 references that are in the map.
- First posted on arXiv 3 Jan 2023.

## Built on by (most cited first, 4 of 4)

- [Deep Reinforcement Learning with Plasticity Injection](https://mlsanity.com/paper/2305.15555/) (2023)
- [Structure in Deep Reinforcement Learning: A Survey and Open Problems](https://mlsanity.com/paper/2306.16021/) (2023)
- [Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop](https://mlsanity.com/paper/2404.10906/) (2024)
- [A Research Agenda for Usability and Generalisation in Reinforcement Learning](https://mlsanity.com/paper/2412.16970/) (2024)

## Abstract (arXiv)

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.