  Artificial intelligence (AI)-driven zero-touch massive network slicing is
envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy
would be extended to the final consumer in the form of advanced digital
use-cases. In this paper, we propose a novel model-free deep reinforcement
learning (DRL) framework, called collaborative statistical Actor-Critic (CS-AC)
that enables a scalable and farsighted slice performance management in a
6G-like RAN scenario that is built upon mobile edge computing (MEC) and massive
multiple-input multiple-output (mMIMO). In this intent, the proposed CS-AC
targets the optimization of the latency cost under a long-term statistical
service-level agreement (SLA). In particular, we consider the Q-th delay
percentile SLA metric and enforce some slice-specific preset constraints on it.
Moreover, to implement distributed learners, we propose a developed variant of
soft Actor-Critic (SAC) with less hyperparameter sensitivity. Finally, we
present numerical results to showcase the gain of the adopted approach on our
built OpenAI-based network slicing environment and verify the performance in
terms of latency, SLA Q-th percentile, and time efficiency. To the best of our
knowledge, this is the first work that studies the feasibility of an AI-driven
approach for massive network slicing under statistical SLA.
