# Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks

arXiv: https://arxiv.org/abs/2209.13532 · HTML: https://mlsanity.com/paper/2209.13532/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks](https://mlsanity.com/paper/2012.03158/) (2020)
- [Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication](https://mlsanity.com/paper/1911.03264/) (2019)
- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [Deep Reinforcement Learning for Scheduling in Cellular Networks](https://mlsanity.com/paper/1905.05914/) (2019)
- [Structure-aware reinforcement learning for node-overload protection in mobile edge computing](https://mlsanity.com/paper/2107.01025/) (2021)

## Built on by (most cited first, 5 of 5)

- [Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach](https://mlsanity.com/paper/2309.07265/) (2023)
- [How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN Slicing?](https://mlsanity.com/paper/2309.00489/) (2023)
- [SafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learning](https://mlsanity.com/paper/2503.12753/) (2025)
- [Adaptive Utility driven Resource Orchestration for Resilient AI (AURORA-AI)](https://mlsanity.com/paper/2606.27005/) (2026)
- [RRM-GPT: A Framework and Vision for Radio Resource Management Foundation Models](https://mlsanity.com/paper/2610.04296/) (2026)

## Abstract (arXiv)

Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the wireless networks domain. They are considered promising approaches for solving dynamic radio resource management (RRM) problems in next-generation networks. Given their capabilities to build an approximate and continuously updated model of the wireless network environments, DRL algorithms can deal with the multifaceted complexity of such environments. Nevertheless, several challenges hinder the practical adoption of DRL in commercial networks. In this article, we first discuss two key practical challenges that are faced but rarely tackled when developing DRL-based RRM solutions. We argue that it is inevitable to address these DRL-related challenges for DRL to find its way to RRM commercial solutions. In particular, we discuss the need to have safe and accelerated DRL-based RRM solutions that mitigate the slow convergence and performance instability exhibited by DRL algorithms. We then review and categorize the main approaches used in the RRM domain to develop safe and accelerated DRL-based solutions. Finally, a case study is conducted to demonstrate the importance of having safe and accelerated DRL-based RRM solutions. We employ multiple variants of transfer learning (TL) techniques to accelerate the convergence of intelligent radio access network (RAN) slicing DRL-based controllers. We also propose a hybrid TL-based approach and sigmoid function-based rewards as examples of safe exploration in DRL-based RAN slicing.