12 citations · 16 across the 3 of their papers we have counts for
4 papers
Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space
Jernej Hribar, Andrei Marinescu, Alessandro Chiumento +1
Millions of battery-powered sensors deployed for monitoring purposes in a multitude of scenarios, e.g., agriculture, smart cities, industry, etc., require energy-efficient solution…
Using Deep Q-learning To Prolong the Lifetime of Correlated Internet of Things Devices
Jernej Hribar, Andrei Marinescu, George A. Ropokis +1
Battery-powered sensors deployed in the Internet of Things (IoT) require energy-efficient solutions to prolong their lifetime. When these sensors observe a physical phenomenon dist…
A Multi-Agent Neural Network for Dynamic Frequency Reuse in LTE Networks
Andrei Marinescu, Irene Macaluso, Luiz A. DaSilva
Fractional Frequency Reuse techniques can be employed to address interference in mobile networks, improving throughput for edge users. There is a tradeoff between the coverage and…
Decentralised Multi-Agent Reinforcement Learning for Dynamic and Uncertain Environments
Andrei Marinescu, Ivana Dusparic, Adam Taylor +2
Multi-Agent Reinforcement Learning (MARL) is a widely used technique for optimization in decentralised control problems. However, most applications of MARL are in static environmen…