  Machine learning (ML) is increasingly used to automate networking tasks, in a
paradigm known as zero-touch network and service management (ZSM). In
particular, Deep Reinforcement Learning (DRL) techniques have recently gathered
much attention for their ability to learn taking complex decisions in different
fields. In the ZSM context, DRL is an appealing candidate for tasks such as
dynamic resource allocation, that is generally formulated as hard optimization
problems. At the same time, successful training and deployment of DRL agents in
real-world scenarios faces a number of challenges that we outline and address
in this paper. Tackling the case of Wireless Local Area Network (WLAN) radio
resource management, we report guidelines that extend to other usecases and
more general contexts.
