  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.
