Towards Deployable RL -- What's Broken with RL Research and a Potential Fix
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 0 of its 2 references that are in the map. First posted on arXiv 3 Jan 2023.
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Citing papers per year: 2023: 2; 2024: 2.
4 papers.
- 2023Deep Reinforcement Learning with Plasticity Injectioncited by 71
- 2023Structure in Deep Reinforcement Learning: A Survey and Open Problemscited by 16
- 2024Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshopcited by 0
- 2024A Research Agenda for Usability and Generalisation in Reinforcement Learningcited by 0
- 2024A Research Agenda for Usability and Generalisation in Reinforcement Learning22 Dec 2024
- 2024Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop16 Apr 2024
- 2023Structure in Deep Reinforcement Learning: A Survey and Open Problems28 Jun 2023
- 2023Deep Reinforcement Learning with Plasticity Injection24 May 2023