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 without any drop in optimisation gain.
@article{arxiv.2404.19462,
title = {Continual Model-based Reinforcement Learning for Data Efficient Wireless Network Optimisation},
author = {Cengis Hasan and Alexandros Agapitos and David Lynch and Alberto Castagna and Giorgio Cruciata and Hao Wang and Aleksandar Milenovic},
journal= {arXiv preprint arXiv:2404.19462},
year = {2024}
}