6 papers
Decentralized Spectrum Learning for IoT Wireless Networks Collision Mitigation
Christophe Moy, Lilian Besson
This paper describes the principles and implementation results of reinforcement learning algorithms on IoT devices for radio collision mitigation in ISM unlicensed bands. Learning…
Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions
Remi Bonnefoi, Lilian Besson, Julio Manco-Vasquez +1
In this paper, we propose and evaluate different learning strategies based on Multi-Arm Bandit (MAB) algorithms. They allow Internet of Things (IoT) devices to improve their access…
GNU Radio Implementation of MALIN: "Multi-Armed bandits Learning for Internet-of-things Networks"
Lilian Besson, Remi Bonnefoi, Christophe Moy
We implement an IoT network the following way: one gateway, one or several intelligent (i.e., learning) objects, embedding the proposed solution, and a traffic generator that emula…
Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits
Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard +1
We introduce GLR-klUCB, a novel algorithm for the piecewise iid non-stationary bandit problem with bounded rewards. This algorithm combines an efficient bandit algorithm, kl-UCB, w…
Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings
Rémi Bonnefoi, Lilian Besson, Christophe Moy +2
Setting up the future Internet of Things (IoT) networks will require to support more and more communicating devices. We prove that intelligent devices in unlicensed bands can use M…
What Doubling Tricks Can and Can't Do for Multi-Armed Bandits
Lilian Besson, Emilie Kaufmann
An online reinforcement learning algorithm is anytime if it does not need to know in advance the horizon T of the experiment. A well-known technique to obtain an anytime algorithm…