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ORCIDAndrei Marinescu
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2020 – today
- 2022
[j3]Jernej Hribar
, Andrei Marinescu, Alessandro Chiumento, Luiz A. DaSilva:
Energy-Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space. IEEE Internet Things J. 9(9): 6732-6744 (2022)- 2020
- [i4]Jernej Hribar, Andrei Marinescu, Alessandro Chiumento, Luiz A. DaSilva:
Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space. CoRR abs/2011.09747 (2020)
2010 – 2019
- 2019
- [j2]Sheng Chen, Zhiyuan Jiang, Sheng Zhou, Zhisheng Niu, Ziyan He, Andrei Marinescu, Luiz A. DaSilva:
Learning-Based Remote Channel Inference: Feasibility Analysis and Case Study. IEEE Trans. Wirel. Commun. 18(7): 3554-3568 (2019) - [c11]Jernej Hribar, Andrei Marinescu, George A. Ropokis, Luiz A. DaSilva:
Using Deep Q-Learning to Prolong the Lifetime of Correlated Internet of Things Devices. ICC Workshops 2019: 1-6 - [c10]Zhiyuan Jiang, Andrei Marinescu, Luiz A. DaSilva, Sheng Zhou, Zhisheng Niu:
Scalable Multi-Agent Learning for Situationally-Aware Multiple-Access and Grant-Free Transmissions. SPAWC 2019: 1-5 - [i3]Jernej Hribar, Andrei Marinescu, George A. Ropokis, Luiz A. DaSilva:
Using Deep Q-learning To Prolong the Lifetime of Correlated Internet of Things Devices. CoRR abs/1902.02850 (2019) - 2018
- [c9]Andrei Marinescu, Irene Macaluso, Luiz A. DaSilva:
A Multi-Agent Neural Network for Dynamic Frequency Reuse in LTE Networks. ICC Workshops 2018: 1-6 - [i2]Andrei Marinescu, Irene Macaluso, Luiz A. DaSilva:
A Multi-Agent Neural Network for Dynamic Frequency Reuse in LTE Networks. CoRR abs/1801.05204 (2018) - 2017
- [j1]Andrei Marinescu, Ivana Dusparic, Siobhán Clarke:
Prediction-Based Multi-Agent Reinforcement Learning in Inherently Non-Stationary Environments. ACM Trans. Auton. Adapt. Syst. 12(2): 9:1-9:23 (2017) - [c8]Andrei Marinescu, Irene Macaluso, Luiz A. DaSilva:
System Level Evaluation and Validation of the ns-3 LTE Module in 3GPP Reference Scenarios. Q2SWinet@MSWiM 2017: 59-64 - 2016
- [b1]Andrei Marinescu:
Prediction-Based Multi-Agent Reinforcement Learning for Inherently Non-Stationary Environments. Trinity College Dublin, Ireland, 2016 - 2015
- [c7]Andrei Marinescu, Ivana Dusparic, Adam Taylor, Vinny Cahill, Siobhán Clarke:
P-MARL: Prediction-Based Multi-Agent Reinforcement Learning for Non-Stationary Environments. AAMAS 2015: 1897-1898 - [c6]Ivana Dusparic, Adam Taylor, Andrei Marinescu, Vinny Cahill, Siobhán Clarke:
Maximizing renewable energy use with decentralized residential demand response. ISC2 2015: 1-6 - [c5]Jean Cavallo, Andrei Marinescu, Ivana Dusparic, Siobhán Clarke:
Evaluation of Forecasting Methods for Very Small-Scale Networks. DARE 2015: 56-75 - 2014
- [c4]Andrei Marinescu, Ivana Dusparic, Colin Harris, Vinny Cahill, Siobhán Clarke:
A dynamic forecasting method for small scale residential electrical demand. IJCNN 2014: 3767-3774 - [c3]Colin Harris, Ivana Dusparic, Edgar Galván López, Andrei Marinescu, Vinny Cahill, Siobhán Clarke:
Set point control for charging of electric vehicles on the distribution network. ISGT 2014: 1-5 - [c2]Andrei Marinescu, Colin Harris, Ivana Dusparic, Vinny Cahill, Siobhán Clarke:
A hybrid approach to very small scale electrical demand forecasting. ISGT 2014: 1-5 - [i1]Andrei Marinescu, Ivana Dusparic, Adam Taylor, Vinny Cahill, Siobhán Clarke:
Decentralised Multi-Agent Reinforcement Learning for Dynamic and Uncertain Environments. CoRR abs/1409.4561 (2014) - 2013
- [c1]Andrei Marinescu, Colin Harris, Ivana Dusparic, Siobhán Clarke, Vinny Cahill:
Residential electrical demand forecasting in very small scale: An evaluation of forecasting methods. SE4SG@ICSE 2013: 25-32
Coauthor Index
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