# Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

**Authors:** Nguyen Cong Luong, Dinh Thai Hoang, Shimin Gong, Dusit Niyato, Ping Wang, Ying-Chang Liang, Dong In Kim  
**arXiv:** [1810.07862](https://arxiv.org/abs/1810.07862)  
**Date:** 2018-10-18  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1810.07862v1/

## Key points

- Deep reinforcement learning is being applied in communications and networking
- Traditional reinforcement learning struggles in complex and large-scale networks
- Deep reinforcement learning combines reinforcement learning with deep learning
- The paper provides a tutorial on deep reinforcement learning
- Various approaches of deep reinforcement learning have been proposed for communications and networking issues
- Issues discussed include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation
- Deep reinforcement learning is also applied to traffic routing, resource sharing, and data collection.

## Summary

This paper provides a comprehensive literature review on the applications of deep reinforcement learning in communications and networking. With the increasing decentralization and autonomy of modern networks, such as Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, network entities need to make local decisions to maximize network performance in uncertain environments. While traditional reinforcement learning has been effective in finding optimal policies for small state and action spaces, it may struggle in complex and large-scale networks where the state and action spaces are usually large. To overcome these limitations, deep reinforcement learning, which combines reinforcement learning with deep learning, has been developed. The paper starts by providing a tutorial on deep reinforcement learning, covering fundamental concepts to advanced models. It then reviews various approaches of deep reinforcement learning that have been proposed to address emerging issues in communications and networking. The issues discussed include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation - all crucial aspects for next-generation networks like 5G and beyond. Additionally, the paper presents applications of deep reinforcement learning for traffic routing, resource sharing, and data collection.