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.
@article{arxiv.2107.03015,
title = {Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research},
author = {Juan Jose Garau-Luis and Edward Crawley and Bruce Cameron},
journal= {arXiv preprint arXiv:2107.03015},
year = {2021}
}