# Controlled Deep Reinforcement Learning for Optimized Slice Placement

arXiv: https://arxiv.org/abs/2108.01544 · HTML: https://mlsanity.com/paper/2108.01544/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

We present a hybrid ML-heuristic approach that we name "Heuristically Assisted Deep Reinforcement Learning (HA-DRL)" to solve the problem of Network Slice Placement Optimization. The proposed approach leverages recent works on Deep Reinforcement Learning (DRL) for slice placement and Virtual Network Embedding (VNE) and uses a heuristic function to optimize the exploration of the action space by giving priority to reliable actions indicated by an efficient heuristic algorithm. The evaluation results show that the proposed HA-DRL algorithm can accelerate the learning of an efficient slice placement policy improving slice acceptance ratio when compared with state-of-the-art approaches that are based only on reinforcement learning.