Abstract: According to embodiments herein e.g. a method performed by an orchestration agent unit for handling a service in a communications network is provided. The orchestration agent unit evaluate a number of intent types of the service based on a performance of one or more performance metrics related to intent of respective intent type when performed in the communications network including a plurality of orchestration agent units. The orchestration agent unit selects an intent type of the service based on the evaluation; and sends to at least one of the plurality of orchestration agent units, an indication indicating the selected intent type of the service.

Abstract: Methods and apparatus for implementing reinforcement learning are provided. A method in a client node that instructs actions in an environment in accordance with a policy includes identifying one or more critical states of the environment for which a current policy provides unreliable actions. The method further includes initiating transmission to a server of a retraining request, the retraining request having information relating to the one or more critical states. The method further includes receiving a new policy from the server, wherein the new policy is generated by the server using reinforcement learning based on the information relating to the one or more critical states, and instructing actions in the environment in accordance with the new policy.

Abstract: A management node for use with a cognitive layer (CL) having agents. The agents have respective agent information indicating type(s) of input data required by a model implemented by the agent, parameter(s) of the system that are to be improved by the agent, type(s) of output data provided by the model and a data distribution for the agent. A method includes (i) selecting a set of similar agents that improve a first parameter of the system: (ii) for a first agent and a second agent in the selected set of similar agents, comparing the data distribution for the first agent to the data distribution for the second agent to determine a relationship: (iii) initiating generation of the candidate agent(s) based on the relationship; and (iv) determining whether to replace one or both of the first agent and the second agent with the one or more candidate agents.

Abstract: A method for enabling a machine learning model to be aggregated from local machine learning models comprised in at least two local nodes in a wireless communications network is provided. The method comprises receiving, from each of the at least two local nodes, a parametrized function of a local machine learning model, a generator function of a local generative model, and a discriminator function of a local discriminative model, wherein the generator function and the discriminator function are trained on the same data as the parametrized function.

Abstract: A method performed by a central server node is provided. The method includes: receiving local model weights and corresponding key from a local client node; and updating a model pool having a plurality of central models and corresponding keys associated with each of the central models. Updating the model pool is based on the local model weights, and one or more of the key corresponding to the local client node and the keys collectively corresponding to each of the central models. Updating the model pool comprises updating at least two of the plurality of central models contained in the model pool.

Abstract: In one aspect, a method of determining a risk of conflict between a movable device and potential obstacles is provided. The method includes dividing a space into a plurality of positions. At each of a plurality of successive times, the method further includes: determining a conflict function for the movable device in a first position at a respective time, the first position having one or more neighbouring positions, wherein the conflict function is determined based on whether or not an obstacle is present in any of the first position and the one or more neighbouring positions; and determining a respective risk value for at least one of the first position and the one or more neighbouring positions using the conflict function and a risk value associated with a second position, wherein the movable device is planned to move from the first position to the second position at a subsequent time.