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

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arxiv 2301.01320 v1 pith:IZ74GBN6 submitted 2023-01-03 cs.LG stat.ML

Shie Mannor , Aviv Tamar This is my paper · ORCID
classification cs.LGstat.ML
keywords potentialcurrentdeployabledifficultiesdirectionresearchsomebroken
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Research Agenda for Usability and Generalisation in Reinforcement Learning

    cs.AI 2024-12 conditional novelty 4.0 of 10

    RL environments should be described in user-friendly domain-specific languages or natural language, so non-engineers can define tasks and agents can generalize to new tasks.

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