Enabling Reinforcement Learning for Network Slice Management in Multi-Agent 5G Networks

To enhance dynamic resource adaptation in fifth generation (5G) networks, network slicing management em-powered by artificial intelligence (AI) through decision-making algorithms may improve resource utilization, quality of service (QoS), as well as network scalability and flexibility. In this paper, we propose an AI-driven network slice management (AI-NSM) framework that enables enhanced adaptive resource allocation for 5G networks by ensuring additional management and orchestration for network slices. The integration of AI-NSM into 5G networks exhibits superior adaptability supporting dynamic organization of network slices based on predicted traffic patterns through reinforcement learning (RL), leading to reduced latency, optimized resource allocation, and improved QoS. Based on a virtualization platform through Oracle virtual machines, we implement an AI model including a multi-agent deep deterministic policy gradient RL algorithm that provides complementary support for other network slice management functions. Through implementation and experiments, we demon-strate that AI-NSM can enhance resource allocation and improve network responsiveness for slicing in 5G networks.