Controlled Deep Reinforcement Learning for Optimized Slice Placement

Jose Jurandir Alves Esteves; Amina Boubendir; Fabrice Guillemin; Pierre Sens

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arXiv · 2108.01544

Controlled Deep Reinforcement Learning for Optimized Slice Placement

Jose Jurandir Alves Esteves·Amina Boubendir·Fabrice Guillemin·Pierre Sens

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Abstract

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.

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Jose Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin, Pierre Sens. 2021-08-03. Controlled Deep Reinforcement Learning for Optimized Slice Placement. https://arxiv.org/abs/2108.01544

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