Network slicing means an emerging business to operators and allows them to sell the customized slices to various tenants at different prices. In order to provide better-performing and costefficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforces the tendency actions producing more rewarding consequences, is emerging as a promising solution. In this paper, after briefly reviewing the fundamental concepts and evolution-driving factors of DRL, we investigate the application of DRL in some typical resource management scenarios of network slicing, which include radio resource slicing and priority-based core network slicing, and demonstrate the performance advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.

本研究探讨了深度强化学习在网络切片资源管理中的应用，并通过广泛的模拟比较实现了无线电资源切片和基于优先级的核心网络切片等场景中深度强化学习的优势。同时，从一个更广泛的视角讨论了在网络切片中应用深度强化学习可能面临的挑战。

网络分片资源管理的深度强化学习