# Deep Reinforcement Learning for Resource Management in Network Slicing

arXiv: https://arxiv.org/abs/1805.06591 · HTML: https://mlsanity.com/paper/1805.06591/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 27 of 27)

- [Reinforcement Learning-powered Semantic Communication via Semantic Similarity](https://mlsanity.com/paper/2108.12121/) (2021)
- [Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning](https://mlsanity.com/paper/2105.04207/) (2021)
- [CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing](https://mlsanity.com/paper/2111.08397/) (2021)
- [Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing](https://mlsanity.com/paper/2311.16876/) (2023)
- [Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing](https://mlsanity.com/paper/2101.08724/) (2021)
- [Anytime-Constrained Reinforcement Learning](https://mlsanity.com/paper/2311.05511/) (2023)
- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach](https://mlsanity.com/paper/2309.07265/) (2023)
- [Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time](https://mlsanity.com/paper/2405.14183/) (2024)
- [Generalizable Resource Scaling of 5G Slices using Constrained Reinforcement Learning](https://mlsanity.com/paper/2306.09290/) (2023)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification](https://mlsanity.com/paper/2103.03745/) (2021)
- [6GAgentGym: Tool Use, Data Synthesis, and Agentic Learning for Network Management](https://mlsanity.com/paper/2603.29656/) (2026)
- [Federated learning and next generation wireless communications: A survey on bidirectional relationship](https://mlsanity.com/paper/2110.07649/) (2021)
- [On the Implementation of a Reinforcement Learning-based Capacity Sharing Algorithm in O-RAN](https://mlsanity.com/paper/2207.10390/) (2022)
- [Deep Reinforcement Learning for Combined Coverage and Resource Allocation in UAV-aided RAN-slicing](https://mlsanity.com/paper/2211.09713/) (2022)
- [Anytime-Constrained Equilibria in Polynomial Time](https://mlsanity.com/paper/2410.23637/) (2024)
- [Polynomial-Time Approximability of Constrained Reinforcement Learning](https://mlsanity.com/paper/2502.07764/) (2025)
- [To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks](https://mlsanity.com/paper/2507.17494/) (2025)
- [A Review of the Convergence of 5G/6G Architecture and Deep Learning](https://mlsanity.com/paper/2208.07643/) (2022)
- [A Deep RL Approach on Task Placement and Scaling of Edge Resources for Cellular Vehicle-to-Network Service Provisioning](https://mlsanity.com/paper/2305.09832/) (2023)
- [Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2306.11552/) (2023)
- [Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies](https://mlsanity.com/paper/2311.11206/) (2023)
- [WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks](https://mlsanity.com/paper/2409.07964/) (2024)
- [A hybrid solution for 2-UAV RAN slicing](https://mlsanity.com/paper/2409.11432/) (2024)
- [Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN](https://mlsanity.com/paper/2512.13715/) (2025)
- [When Adaptive Rewards Hurt: Causal Probing and the Switching-Stability Dilemma in LLM-Guided LEO Satellite Scheduling](https://mlsanity.com/paper/2604.03562/) (2026)

## Abstract (arXiv)

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient 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 reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the 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.