Continual Model-based Reinforcement Learning for Data Efficient Wireless Network Optimisation

arXiv:2404.19462v1 Announce Type: new
Abstract: We present a method that addresses the pain point of long lead-time required to deploy cell-level parameter optimisation policies to new wireless network sites. Given a sequence of action spaces represented by overlapping subsets of cell-level configuration parameters provided by domain experts, we formulate throughput optimisation as Continual Reinforcement Learning of control policies. Simulation results suggest that the proposed system is able to shorten the end-to-end deployment lead-time by two-fold compared to a reinitialise-and-retrain baseline …

abstract arxiv continual cs.lg data deploy domain domain experts experts network optimisation pain parameters policies reinforcement reinforcement learning spaces subsets type wireless