Ai/ml-based method for producing a telecommunication protocol automatically

A method for a training host associated with a plurality of protocol agents participating to a multiagent reinforcement learning process, the method comprising:

• receiving training data from at least one protocol agent, the training data including state vectors and one or more control plane actions predicted by a machine learning model at the protocol agent;
• computing a reward on the basis of one or more control plane actions predicted respectively by the protocol agents;
• updating a critic machine learning model based on the reward and the state vectors, wherein the critic machine learning model is configured to generate an expected quality value based on the training data;
• sending a training feedback to the protocol agents, wherein the training feedback includes at least one of the expected quality value and the reward.

An apparatus, comprising:

• at least one processor;
• and
• at least one memory storing instructions that, when executed by the at least one processor, caused the apparatus to perform at least
• receiving training data from at least one protocol agent, the training data including state vectors and one or more control plane actions predicted by a machine learning model at the protocol agent;
• computing a reward on the basis of one or more control plane actions predicted respectively by the protocol agents;
• updating a critic machine learning model based on the reward and the state vectors, wherein the critic machine learning model is configured to generate an expected quality value based on the training data;
• sending a training feedback to one or more protocol agents at one or more network nodes, wherein the training feedback includes at least one of the expected quality value and the reward;
• and
• training a machine learning model to learn a communication protocol for a communication medium by assigning a function to control plane actions defined in a set of control plane actions, wherein the protocol defines control-plane messages to be transmitted via the communication medium and a user-plane policy;
• wherein the machine learning model is configured to identify, in the set of control plane actions, a control plane action to be performed during a current transmission time interval by a protocol agent at a network node.