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.
@article{arxiv.2108.01544,
title = {Controlled Deep Reinforcement Learning for Optimized Slice Placement},
author = {Jose Jurandir Alves Esteves and Amina Boubendir and Fabrice Guillemin and Pierre Sens},
journal= {arXiv preprint arXiv:2108.01544},
year = {2021}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2105.06741