  Artificial intelligence (AI) and Machine Learning (ML) are considered as key
enablers for realizing the full potential of fifth-generation (5G) and beyond
mobile networks, particularly in the context of resource management and
orchestration. In this demonstration, we consider a fully-fledged 5G mobile
network and develop a multi-agent deep reinforcement learning (DRL) framework
for RAN resource allocation. By leveraging local monitoring information
generated by a shared gNodeB instance (gNB), each DRL agent aims to optimally
allocate radio resources concerning service-specific traffic demands belonging
to heterogeneous running services. We perform experiments on the deployed
testbed in real-time, showing that DRL-based agents can allocate radio
resources fairly while improving the overall efficiency of resource utilization
and minimizing the risk of over provisioning.
