  Starting from the Cloud Radio Access Network (C-RAN), continuing with the
virtual Radio Access Network (vRAN) and most recently with Open RAN (O-RAN)
initiative, Radio Access Network (RAN) architectures have significantly evolved
in the past decade. In the last few years, the wireless industry has witnessed
a strong trend towards disaggregated, virtualized and open RANs, with numerous
tests and deployments world wide. One unique aspect that motivates this paper
is the availability of new opportunities that arise from using machine learning
to optimize the RAN in closed-loop, i.e. without human intervention, where the
complexity of disaggregation and virtualization makes well-known Self-Organized
Networking (SON) solutions inadequate. In our view, Multi-Agent Systems (MASs)
with team learning, can play an essential role in the control and coordination
of controllers of O-RAN, i.e. near-real-time and non-real-time RAN Intelligent
Controller (RIC). In this article, we first present the state-of-the-art
research in multi-agent systems and team learning, then we provide an overview
of the landscape in RAN disaggregation and virtualization, as well as O-RAN
which emphasizes the open interfaces introduced by the O-RAN Alliance. We
present a case study for agent placement and the AI feedback required in O-RAN,
and finally, we identify challenges and open issues to provide a roadmap for
researchers.
