# DRL-based Latency-Aware Network Slicing in O-RAN with Time-Varying SLAs

arXiv: https://arxiv.org/abs/2401.05042 · HTML: https://mlsanity.com/paper/2401.05042/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2024.

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

- [Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN](https://mlsanity.com/paper/2410.03737/) (2024)
- [Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning](https://mlsanity.com/paper/2511.15002/) (2025)
- [Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN](https://mlsanity.com/paper/2512.13715/) (2025)
- [Plan2Cleanse: Test-Time Backdoor Defense via Monte-Carlo Planning in Deep Reinforcement Learning](https://mlsanity.com/paper/2605.09638/) (2026)
- [Physically Constrained Federated Additive Models for O-RAN SLA-Risk Prediction](https://mlsanity.com/paper/2607.21665/) (2026)

## Abstract (arXiv)

The Open Radio Access Network (Open RAN) paradigm, and its reference architecture proposed by the O-RAN Alliance, is paving the way toward open, interoperable, observable and truly intelligent cellular networks. Crucial to this evolution is Machine Learning (ML), which will play a pivotal role by providing the necessary tools to realize the vision of self-organizing O-RAN systems. However, to be actionable, ML algorithms need to demonstrate high reliability, effectiveness in delivering high performance, and the ability to adapt to varying network conditions, traffic demands and performance requirements. To address these challenges, in this paper we propose a novel Deep Reinforcement Learning (DRL) agent design for O-RAN applications that can learn control policies under varying Service Level Agreement (SLAs) with heterogeneous minimum performance requirements. We focus on the case of RAN slicing and SLAs specifying maximum tolerable end-to-end latency levels. We use the OpenRAN Gym open-source environment to train a DRL agent that can adapt to varying SLAs and compare it against the state-of-the-art. We show that our agent maintains a low SLA violation rate that is 8.3x and 14.4x lower than approaches based on Deep Q- Learning (DQN) and Q-Learning while consuming respectively 0.3x and 0.6x fewer resources without the need for re-training.