  Artificial intelligence (AI)-driven zero-touch network slicing is envisaged
as a promising cutting-edge technology to harness the full potential of
heterogeneous 5G and beyond 5G (B5G) communication systems and enable the
automation of demand-aware resource management and orchestration (MANO). In
this paper, we tackle the issue of B5G radio access network (RAN) joint slice
admission control and resource allocation according to proposed slice-enabling
cell-free massive multiple-input multiple-output (mMIMO) setup by invoking a
continuous deep reinforcement learning (DRL) method. We present a novel
Actor-Critic-based network slicing approach called, prioritized twin delayed
distributional deep deterministic policy gradient (D-TD3)}. The paper defines
and corroborates via extensive experimental results a zero-touch network
slicing scheme with a multi-objective approach where the central server learns
continuously to accumulate the knowledge learned in the past to solve future
problems and re-configure computing resources autonomously while minimizing
latency, energy consumption, and virtual network function (VNF) instantiation
cost for each slice. Moreover, we pursue a state-action return distribution
learning approach with the proposed replay policy and reward-penalty
mechanisms. Finally, we present numerical results to showcase the gain of the
adopted multi-objective strategy and verify the performance in terms of
achieved slice admission rate, latency, energy, CPU utilization, and time
efficiency.
