Toward Optimizing Reinforcement Learning Workload Placement at the Cloud-Edge Continuum in 6G Networks: A Scaled RL Framework

With the increasing deployment of Reinforcement Learning (RL) for network optimization at the edge of wirelessnetworks, the RL workload emerges as a significant challenge. While the placement of general Machine Learning workloadsacross the cloud–edge continuum has been widely studied, existing solutions typically exclude RL techniques due to theirdistinct structure and operational requirements. In this work, we propose a framework for RL workload placement in thecloud–edge continuum, enabling th