Imec Vzw

Universiteit Antwerpen

Universiteit Gent

Koninklijke Kpn N.v.

Ghent University

Iminds

Steven Latré

Jeroen Famaey

Ruben Mennes

Miguel Camelo

Maxim Claeys

A computer-implemented method for exploring, by a table-based parallel reinforcement learning, PRL, algorithm, an unexplored domain comprising a plurality of agents and states, the unexplored domain represented by a state-action space. The method includes the steps performed by one or more of the plurality of agents receiving an assigned partition of the state-action space represented by a table; and executing during a plurality of episodes actions for states within the partition. An action transits a state; and granting to a transited state a reward; and exchanging state-action values with other agents of the plurality of agents in the domain; and updating the table.

Exploring an unexplored domain by parallel reinforcement

A computer implemented method predicts by a neural network a radio spectrum usage in an environment comprising interferers and a plurality of wireless nodes. The nodes operate via a slotted medium access control, MAC, protocol within a radio spectrum. The method includes the following steps: obtaining per slot the radio spectrum usage by sensing the radio spectrum, labelling the slots as usable when a respective radio spectrum usage is lower than a predefined noise threshold or when a node successfully transmitted a packet through it, unusable when it is not usable and suitable to receive a packet, and calculating a loss function as a cross entropy loss with the usable and unusable slots over a total number of usable and unusable slots, and training the neural network by minimizing the loss function.

Predicting a radio spectrum usage

Growing community of inventors

Lokeren, Belgium

Steven Latré