| 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 | 65 | 2022 |
| 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 | 37 | 2018 |
| 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 | 10 | 2021 |
| 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 | 7 | 2021 |
| 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 | 6 | 2019 |
| 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 | 6 | 2013 |
| Model-based multi-agent reinforcement learning with cooperative prioritized sweeping E Bargiacchi, T Verstraeten, DM Roijers, A Nowé arXiv preprint arXiv:2001.07527, 2020 | 5 | 2020 |
| 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 | 5 | 2014 |
| 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 | 4 | 2021 |
| 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 | 4 | 2019 |
| Pareto conditioned networks M Reymond, E Bargiacchi, A Nowé arXiv preprint arXiv:2204.05036, 2022 | 3 | 2022 |
| Dynamic resource allocation for multi-camera systems E Bargiacchi Master's thesis, University of Amsterdam, 2016 | 2 | 2016 |
| 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 | 1 | 2020 |
| 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 |
Diederik M. RoijersVrije Universiteit Brussel (VUB) & City of AmsterdamBestätigte E-Mail-Adresse bei ai.vub.ac.be
