  To harness the full potential of beyond 5G (B5G) communication systems,
zero-touch network slicing (NS) is viewed as a promising fully-automated
management and orchestration (MANO) system. This paper proposes a novel
knowledge plane (KP)-based MANO framework that accommodates and exploits recent
NS technologies and is termed KB5G. Specifically, we deliberate on algorithmic
innovation and artificial intelligence (AI) in KB5G. We invoke a continuous
model-free deep reinforcement learning (DRL) method to minimize energy
consumption and virtual network function (VNF) instantiation cost. We present a
novel Actor-Critic-based NS approach to stabilize learning called, twin-delayed
double-Q soft Actor-Critic (TDSAC) method. The TDSAC enables central unit (CU)
to learn continuously to accumulate the knowledge learned in the past to
minimize future NS costs. Finally, we present numerical results to showcase the
gain of the adopted approach and verify the performance in terms of energy
consumption, CPU utilization, and time efficiency.
