# On the Robustness of Controlled Deep Reinforcement Learning for Slice Placement

arXiv: https://arxiv.org/abs/2108.02505 · HTML: https://mlsanity.com/paper/2108.02505/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [Heuristic for Edge-enabled Network Slicing Optimization using the Power of Two Choices](https://mlsanity.com/paper/2010.08287/) (2020)
- [A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement](https://mlsanity.com/paper/2105.06741/) (2021)

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

- [Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning](https://mlsanity.com/paper/2207.13821/) (2022)

## Abstract (arXiv)

The evaluation of the impact of using Machine Learning in the management of softwarized networks is considered in multiple research works. Beyond that, we propose to evaluate the robustness of online learning for optimal network slice placement. A major assumption to this study is to consider that slice request arrivals are non-stationary. In this context, we simulate unpredictable network load variations and compare two Deep Reinforcement Learning (DRL) algorithms: a pure DRL-based algorithm and a heuristically controlled DRL as a hybrid DRL-heuristic algorithm, to assess the impact of these unpredictable changes of traffic load on the algorithms performance. We conduct extensive simulations of a large-scale operator infrastructure. The evaluation results show that the proposed hybrid DRL-heuristic approach is more robust and reliable in case of unpredictable network load changes than pure DRL as it reduces the performance degradation. These results are follow-ups for a series of recent research we have performed showing that the proposed hybrid DRL-heuristic approach is efficient and more adapted to real network scenarios than pure DRL.