  Artificial intelligence (AI)-driven zero-touch network slicing (NS) is a new
paradigm enabling the automation of resource management and orchestration
(MANO) in multi-tenant beyond 5G (B5G) networks. In this paper, we tackle the
problem of cloud-RAN (C-RAN) joint slice admission control and resource
allocation by first formulating it as a Markov decision process (MDP). We then
invoke an advanced continuous deep reinforcement learning (DRL) method called
twin delayed deep deterministic policy gradient (TD3) to solve it. In this
intent, we introduce a multi-objective approach to make the central unit (CU)
learn how to re-configure computing resources autonomously while minimizing
latency, energy consumption and virtual network function (VNF) instantiation
cost for each slice. Moreover, we build a complete 5G C-RAN network slicing
environment using OpenAI Gym toolkit where, thanks to its standardized
interface, it can be easily tested with different DRL schemes. Finally, we
present extensive experimental results to showcase the gain of TD3 as well as
the adopted multi-objective strategy in terms of achieved slice admission
success rate, latency, energy saving and CPU utilization.
