# SafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2503.12753 · HTML: https://mlsanity.com/paper/2503.12753/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [Safe and constrained RL](https://mlsanity.com/deeprl/safe-and-constrained-rl/)

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

- [Learning to Walk in the Real World with Minimal Human Effort](https://mlsanity.com/paper/2002.08550/) (2020)
- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Virtual Reality Gaming on the Cloud: A Reality Check](https://mlsanity.com/paper/2109.10114/) (2021)
- [OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning](https://mlsanity.com/paper/2111.01616/) (2021)
- [CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing](https://mlsanity.com/paper/2111.08397/) (2021)
- [Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks](https://mlsanity.com/paper/2209.13532/) (2022)
- [Generalizable Resource Scaling of 5G Slices using Constrained Reinforcement Learning](https://mlsanity.com/paper/2306.09290/) (2023)
- [How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN Slicing?](https://mlsanity.com/paper/2309.00489/) (2023)

## Built on by (most cited first, 1 of 1)

- [Differentiable Policy Transport over Multi-Layer Network Feasibility Geometry](https://mlsanity.com/paper/2609.26068/) (2026)

## Abstract (arXiv)

Deep reinforcement learning (DRL)-based slicing policies have shown significant success in simulated environments but face challenges in physical systems such as open radio access networks (O-RANs) due to simulation-to-reality gaps. These policies often lack safety guarantees to ensure compliance with service level agreements (SLAs), such as the strict latency requirements of immersive applications. As a result, a deployed DRL slicing agent may make resource allocation (RA) decisions that degrade system performance, particularly in previously unseen scenarios. Real-world immersive applications require maintaining SLA constraints throughout deployment to prevent risky DRL exploration. In this paper, we propose SafeSlice to address both the cumulative (trajectory-wise) and instantaneous (state-wise) latency constraints of O-RAN slices. We incorporate the cumulative constraints by designing a sigmoid-based risk-sensitive reward function that reflects the slices' latency requirements. Moreover, we build a supervised learning cost model as part of a safety layer that projects the slicing agent's RA actions to the nearest safe actions, fulfilling instantaneous constraints. We conduct an exhaustive experiment that supports multiple services, including real virtual reality (VR) gaming traffic, to investigate the performance of SafeSlice under extreme and changing deployment conditions. SafeSlice achieves reductions of up to 83.23% in average cumulative latency, 93.24% in instantaneous latency violations, and 22.13% in resource consumption compared to the baselines. The results also indicate SafeSlice's robustness to changing the threshold configurations of latency constraints, a vital deployment scenario that will be realized by the O-RAN paradigm to empower mobile network operators (MNOs).