# Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN

arXiv: https://arxiv.org/abs/2208.14394 · HTML: https://mlsanity.com/paper/2208.14394/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Evolution-Guided Policy Gradient in Reinforcement Learning](https://mlsanity.com/paper/1805.07917/) (2018)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning](https://mlsanity.com/paper/1706.00387/) (2017)
- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [Intelligent O-RAN for Beyond 5G and 6G Wireless Networks](https://mlsanity.com/paper/2005.08374/) (2020)
- [Predictive Closed-Loop Service Automation in O-RAN based Network Slicing](https://mlsanity.com/paper/2202.01966/) (2022)
- [Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks](https://mlsanity.com/paper/2111.12064/) (2021)
- [Performance Analysis and Optimization of Uplink Cellular Networks with Flexible Frame Structure](https://mlsanity.com/paper/2103.03342/) (2021)

## Built on by (most cited first, 11 of 11)

- [Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN](https://mlsanity.com/paper/2410.03737/) (2024)
- [Attention-based Open RAN Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09490/) (2023)
- [Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach](https://mlsanity.com/paper/2309.07265/) (2023)
- [Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2401.06922/) (2024)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios](https://mlsanity.com/paper/2302.04108/) (2023)
- [ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing](https://mlsanity.com/paper/2506.00576/) (2025)
- [Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2306.10071/) (2023)
- [Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning](https://mlsanity.com/paper/2511.15002/) (2025)
- [Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps](https://mlsanity.com/paper/2506.12812/) (2025)
- [Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN](https://mlsanity.com/paper/2512.13715/) (2025)

## Abstract (arXiv)

The next-generation wireless networks are required to satisfy a variety of services and criteria concurrently. To address upcoming strict criteria, a new open radio access network (O-RAN) with distinguishing features such as flexible design, disaggregated virtual and programmable components, and intelligent closed-loop control was developed. O-RAN slicing is being investigated as a critical strategy for ensuring network quality of service (QoS) in the face of changing circumstances. However, distinct network slices must be dynamically controlled to avoid service level agreement (SLA) variation caused by rapid changes in the environment. Therefore, this paper introduces a novel framework able to manage the network slices through provisioned resources intelligently. Due to diverse heterogeneous environments, intelligent machine learning approaches require sufficient exploration to handle the harshest situations in a wireless network and accelerate convergence. To solve this problem, a new solution is proposed based on evolutionary-based deep reinforcement learning (EDRL) to accelerate and optimize the slice management learning process in the radio access network's (RAN) intelligent controller (RIC) modules. To this end, the O-RAN slicing is represented as a Markov decision process (MDP) which is then solved optimally for resource allocation to meet service demand using the EDRL approach. In terms of reaching service demands, simulation results show that the proposed approach outperforms the DRL baseline by 62.2%.