# Deep Reinforcement Learning for End-to-End Network Slicing: Challenges and Solutions

**Authors:** Qiang Liu, Nakjung Choi, Tao Han  
**arXiv:** [2207.14766](https://arxiv.org/abs/2207.14766)  
**Date:** 2022-07-29  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2207.14766v1/

## Key points

- Network slicing enables diverse use cases with varying performance requirements in 5G and beyond networks
- Traditional model-based solutions are inadequate for network slicing due to the numerous configurable parameters across multiple technical domains
- Deep reinforcement learning (DRL) is proposed as a model-free approach to address this problem
- A standard-compliant system architecture is presented to facilitate DRL-based solutions in 5G and beyond networks
- Challenges associated with designing and deploying DRL in network slicing systems are analyzed
- Promising techniques for automating end-to-end network slicing using DRL are explored, including safety and distributed DRL, as well as imitation learning
- These approaches can help orchestrate resources for network slices more efficiently
- The article provides insights into how DRL can overcome the complexities of network slicing in emerging networks
- The proposed solutions have implications for cost-effective service delivery across various industries with diverse performance requirements.

## Summary

In this article titled "Deep Reinforcement Learning for End-to-End Network Slicing: Challenges and Solutions," authors Qiang Liu, Nakjung Choi, and Tao Han discuss the role of network slicing in enabling diverse use cases with varying performance requirements in 5G and beyond networks. Network slicing involves dynamically creating virtual end-to-end networks to cater to specific resource demands. However, traditional model-based solutions are inadequate due to the hundreds of configurable parameters across multiple technical domains that define the performance of a network slice. To address this problem, the authors propose the use of deep reinforcement learning (DRL), a model-free approach. They first analyze the network slicing problem and present a standard-compliant system architecture that facilitates DRL-based solutions in 5G and beyond networks. The article then delves into an in-depth analysis of the challenges associated with designing and deploying DRL in network slicing systems. Furthermore, the authors explore several promising techniques for automating end-to-end network slicing using DRL. These techniques include safety and distributed DRL, as well as imitation learning. By leveraging these approaches, it becomes possible to orchestrate resources for network slices more efficiently. Overall, this article provides valuable insights into how DRL can be utilized to overcome the complexities of network slicing in emerging 5G and beyond networks. The proposed solutions have significant implications for cost-effective service delivery across various vertical industries with diverse performance requirements.