Folgen
Eugenio Bargiacchi
Eugenio Bargiacchi
Bestätigte E-Mail-Adresse bei ai.vub.ac.be
Titel
Zitiert von
Zitiert von
Jahr
A practical guide to multi-objective reinforcement learning and planning
CF Hayes, R Rădulescu, E Bargiacchi, J Källström, M Macfarlane, ...
Autonomous Agents and Multi-Agent Systems 36 (1), 26, 2022
652022
Learning to coordinate with coordination graphs in repeated single-stage multi-agent decision problems
E Bargiacchi, T Verstraeten, D Roijers, A Nowé, H Hasselt
International conference on machine learning, 482-490, 2018
372018
Multi-agent thompson sampling for bandit applications with sparse neighbourhood structures
T Verstraeten, E Bargiacchi, PJK Libin, J Helsen, DM Roijers, A Nowé
Scientific reports 10 (1), 6728, 2020
22*2020
AI-Toolbox: A C++ library for reinforcement learning and planning (with python bindings)
E Bargiacchi, DM Roijers, A Nowé
The Journal of Machine Learning Research 21 (1), 4118-4129, 2020
17*2020
Cooperative Prioritized Sweeping.
E Bargiacchi, T Verstraeten, DM Roijers
AAMAS, 160-168, 2021
102021
Scalable optimization for wind farm control using coordination graphs
T Verstraeten, PJ Daems, E Bargiacchi, DM Roijers, PJK Libin, J Helsen
arXiv preprint arXiv:2101.07844, 2021
72021
Reinforcement learning 101 with a virtual reality game
Y Coppens, E Bargiacchi, A Nowé
Proceedings of the 1st international workshop on education in artificial …, 2019
62019
Decentralized solutions and tactics for rts
E Bargiacchi, CR Verschoor, G Li, DM Roijers
BNAIC 2013: Proceedings of the 25th Benelux Conference on Artificial …, 2013
62013
Model-based multi-agent reinforcement learning with cooperative prioritized sweeping
E Bargiacchi, T Verstraeten, DM Roijers, A Nowé
arXiv preprint arXiv:2001.07527, 2020
52020
Dutch Nao Team Team Description for RoboCup 2014-Joao Pessoa, Brasil
P de Kok, D ten Velthuis, N Backer, J van Eck, F Voorter, A Visser, ...
University of Amsterdam, TU Delft & Maastricht University, 2014
52014
Interactive multi-objective reinforcement learning in multi-armed bandits with gaussian process utility models
DM Roijers, LM Zintgraf, P Libin, M Reymond, E Bargiacchi, A Nowé
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021
42021
P1415R1: SG19 Machine Learning Layered List
M Wong, V Reverdy, R Dubey, R Dosselmann, E Bargiacchi, J Inglada
ISO JTC1/SC22/WG21: Programming Language C++. Web. 9 Aug. 2020 http://open …, 2019
42019
Pareto conditioned networks
M Reymond, E Bargiacchi, A Nowé
arXiv preprint arXiv:2204.05036, 2022
32022
Dynamic resource allocation for multi-camera systems
E Bargiacchi
Master's thesis, University of Amsterdam, 2016
22016
Thompson sampling for loosely-coupled multi-agent systems: An application to wind farm control
T Verstraeten, E Bargiacchi, PJ Libin, J Helsen, DM Roijers, A Nowé
Adaptive and Learning Agents Workshop, 2020
12020
Heuristic Coordination in Cooperative Multi-Agent Reinforcement Learning
R Petri, E Bargiacchi, H Aldewereld, DM Roijers
Proceedings van de 33rd Benelux Conference on Artificial Intelligence en …, 2021
2021
Wind Farm Control Using Factored Bandits: A Hybrid Approach to Active Power Control
T Verstraeten, PJ Daems, E Bargiacchi, DM Roijers, PJK Libin, J Helsen
2021
Multi-Agent Thompson Sampling for Bandits with Sparse Neighbourhood Structures
T Verstraeten, E Bargiacchi, PJK Libin, J Helsen, DM Roijers, A Nowé
BNAIC/BeneLearn 2020, 394, 2020
2020
AI-Toolbox: A Framework for Fundamental Reinforcement Learning
E Bargiacchi, DM Roijers, A Nowé
BNAIC/BeneLearn 2020, 359, 2020
2020
A Virtual Maze Game to Explain Reinforcement Learning.
Y Coppens, E Bargiacchi, A Nowé
BNAIC/BENELEARN, 2019
2019
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20