Central node and a method for reinforcement learning in a radio access network

Abstract

A method performed by a central node for controlling an exploration strategy associated to Reinforcement Learning, RL, in one or more RL modules in a distributed node in a Radio Access Network, RAN, is provided. The central node evaluates a cost of actions performed for explorations in the one or more RL modules, and a performance of the one or more RL modules. Based on the evaluation, the central node determines one or more exploration parameters associated to the exploration strategy. The central node controls the exploration strategy by configuring the one or more RL modules with the determined one or more exploration parameters to update its exploration strategy, enforcing the respective one or more RL modules to act according to the updated exploration strategy to produce data samples for the one or more RL modules in the distributed node.

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