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

Actor-Critic-Based Learning for Zero-touch Joint Resource and Energy Control in Network Slicing