Training and using a neural network for managing an environment in a communication network

A system comprising a training node for training a Student Neural Network having a plurality of parameters, wherein the Student Neural Network is for generating an action prediction matrix for an environment in a communication network, the environment comprising a plurality of nodes corresponding to entities in the communication network and a plurality of communication network resources, wherein the action prediction matrix comprises a plurality of action predictions for actions that may be executed within the environment, and wherein each action prediction of the action prediction matrix relates to at least one of a node or a resource in the environment, the training node comprising processing circuitry configured to cause the training node to:

• use a Reinforcement Learning process to train a Teacher Neural Network to generate an action prediction for a resource or node in the environment;
• use the trained Teacher Neural Network to generate a first training data set by, for each of a plurality of training situations:
• generating a representation of a state of the environment, the state representation comprising a current version of a partial action matrix that comprises actions for execution within the environment, each action relating to at least one of a node or a resource in the environment;
• and
• sequentially, for each node or each resource in the environment:
• using the trained Teacher Neural Network to generate an action prediction relating to the node or resource based on the state representation;
• adding the state representation and the generated action prediction to the first training data set;
• selecting an action for execution on the node or the resource in accordance with the action prediction;
• and
• updating the state representation to include the selected action in the partial action matrix;
• the processing circuitry being further configured to cause the training node to:
• generate a second training data set from the first training data set by, for each of the plurality of training situations:
• generating a training situation state representation comprising values that are common to all of the state representations for the training situation in the first training data set;
• and
• generating a training situation action prediction matrix comprising all of the generated action predictions for the training situation in the first training data set;
• and
• use the second training data set to update values of the parameters of the Student Neural Network;
• wherein using a Reinforcement Learning process to train the Teacher Neural Network to generate an action prediction for a resource or node in the environment comprises:
• sequentially, for each node or each resource in the environment:
• generating a representation of a state of the environment;
• using the teacher Neural Network to generate an action prediction relating to the node or resource based on the state representation for the environment;
• using a selection function to select an action for execution on the node or the resource in accordance with the action prediction, the selected action being any configuration or resource allocation related action applicable to the node or the resource;
• and
• following execution of the selected action, updating parameters of the Teacher Neural Network based on an obtained measure of success of the executed action.