Controlled Deep Reinforcement Learning for Optimized Slice Placement
This addresses network slice placement for telecommunications, but it is incremental as it builds on existing DRL and VNE methods.
The paper tackles the problem of Network Slice Placement Optimization by proposing a hybrid ML-heuristic approach called HA-DRL, which accelerates learning and improves slice acceptance ratio compared to state-of-the-art reinforcement learning methods.
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.