Creating a global reinforcement learning (RL) model from subnetwork RL agents

A non-transitory computer-readable medium configured to store computer logic having instructions that, when executed, enable a processing device to:

• receive a global model in an Action Recommendation Engine (ARE), the global model created by
• acknowledging a plurality of subnetworks in a whole network, each subnetwork including a plurality of nodes and being represented by a tunnel group having a plurality of end-to-end tunnels through the respective subnetwork,
• selecting a first group of subnetworks from the plurality of subnetworks,
• generating a Reinforcement Learning (RL) agent for each subnetwork of the first group, wherein each RL agent is based on observations of end-to-end metrics of the end-to-end tunnels of the respective subnetwork, and wherein the observations are independent of specific topology information of the respective subnetwork, and
• training the global model based on the RL agents of the first group of subnetworks,
• utilize the global model during inference or production in a real-world environment;
• and
• recommend one or more actions to be taken as needed to improve a state of the whole network.