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

Abstract

Methods are provided for recommending actions to improve operability of a network. In one implementation, a method includes acknowledging a plurality of subnetworks in a whole network, each subnetwork including multiple nodes and being represented by a tunnel group having multiple end-to-end tunnels through the subnetwork. The method also includes selecting a first group of subnetworks from the plurality of subnetworks and generating a Reinforcement Learning (RL) agent for each subnetwork of the first group. Each RL agent is based on observations of end-to-end metrics of the end-to-end tunnels of the respective subnetwork. The observations are independent of specific topology information of the subnetwork. Also, the method includes training a global model based on the RL agents of the first group of subnetworks and applying the global model to an Action Recommendation Engine (ARE) configured for recommending actions that can be taken to improve a state of the whole network.