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Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research

arXiv 2107.03015 ↗First posted 7 Jul 2021cs.LG cs.AIcited by 1

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Abstract (arXiv)

Deep Reinforcement Learning (DRL) is considered a potential framework to improve many real-world autonomous systems; it has attracted the attention of multiple and diverse fields. Nevertheless, the successful deployment in the real world is a test most of DRL models still need to pass. In this work we focus on this issue by reviewing and evaluating the research efforts from both domain-agnostic and domain-specific communities. On one hand, we offer a comprehensive summary of DRL challenges and summarize the different proposals to mitigate them; this helps identifying five gaps of domain-agnostic research. On the other hand, from the domain-specific perspective, we discuss different success stories and argue why other models might fail to be deployed. Finally, we take up on ways to move forward accounting for both perspectives.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 113 of its 148 references that are in the map. First posted on arXiv 7 Jul 2021.

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113 of 148 references.

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  1. 2025A Survey of Reinforcement Learning for Optimization in Automationcited by 4