Continual Model-based Reinforcement Learning for Efficient Wireless Network Parameter Optimization

A continual reinforcement learning approach that leverages forward transfer of knowledge between optimization policies with overlapping subsets of actions to learn the ultimate policy in a data-efficient task-oriented fashion, enabling a two-fold reduction in deployment lead-time compared to a reinitialize-and-retrain baseline.