Vincent François
Vincent François
McGill University and Mila
Verified email at
TitleCited byYear
An introduction to deep reinforcement learning
V François-Lavet, P Henderson, R Islam, MG Bellemare, J Pineau
Foundations and Trends® in Machine Learning 11 (3-4), 219-354, 2018
How to discount deep reinforcement learning: Towards new dynamic strategies
V François-Lavet, R Fonteneau, D Ernst
Deep Reinforcement Learning Workshop, NIPS 2015, 2015
Deep reinforcement learning solutions for energy microgrids management
V François-Lavet, D Taralla, D Ernst, R Fonteneau
European Workshop on Reinforcement Learning (EWRL 2016), 2016
Study of passive and active attitude control systems for the OUFTI nanosatellites
V Francois-Lavet
University of Liège, Faculty of Applied Sciences, Belgium, 2010
An energy-based variational model of ferromagnetic hysteresis for finite element computations
V François-Lavet, F Henrotte, L Stainier, L Noels, C Geuzaine
Journal of Computational and Applied Mathematics 246, 243-250, 2013
Vectorial incremental nonconservative consistent hysteresis model
V François-Lavet, F Henrotte, L Stainier, L Noels, C Geuzaine
Proceedings of the 5th International Conference on Advanded COmputational …, 2011
Towards the minimization of the levelized energy costs of microgrids using both long-term and short-term storage devices
V François-Lavet, Q Gemine, D Ernst, R Fonteneau
Smart Grid: Networking, Data Management, and Business Models, 295-319, 2016
Combined Reinforcement Learning via Abstract Representations
V François-Lavet, Y Bengio, D Precup, J Pineau
Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 2019
Simple connectome inference from partial correlation statistics in calcium imaging
A Sutera, A Joly, V François-Lavet, A Qiu, G Louppe, D Ernst, P Geurts
Neural Connectomics Workshop, 23-35, 2015
Using approximate dynamic programming for estimating the revenues of a hydrogen-based high-capacity storage device
V François-Lavet, R Fonteneau, D Ernst
2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement …, 2014
Reward Estimation for Variance Reduction in Deep Reinforcement Learning
J Romoff, A Piché, P Henderson, V Francois-Lavet, J Pineau
Conference on Robot Learning (CoRL 2018), 2018
Imitative learning for online planning in microgrids
S Aittahar, V François-Lavet, S Lodeweyckx, D Ernst, R Fonteneau
International Workshop on Data Analytics for Renewable Energy Integration, 1-15, 2015
On overfitting and asymptotic bias in batch reinforcement learning with partial observability
V François-Lavet, G Rabusseau, J Pineau, D Ernst, R Fonteneau
Journal of Artificial Intelligence Research 65, 1-30, 2019
Optimizing Home Energy Management and Electric Vehicle Charging with Reinforcement Learning
D Wu, G Rabusseau, V François-lavet, D Precup, B Boulet
ALA 2018, 2018
Approximate Bayes Optimal Policy Search using Neural Networks
M Castronovo, V François-Lavet, R Fonteneau, D Ernst, A Couëtoux
Proceedings of the 9th International Conference on Agents and Artificial …, 2017
Electricity storage with liquid fuels in a zone powered by 100% variable renewables
L Grégoire, FL Vincent, E Damien, JM Christoph, SL Klaus
2015 12th International Conference on the European Energy Market (EEM), 1-5, 2015
Contributions to deep reinforcement learning and its applications in smartgrids
V François-Lavet
Université de Liège, Liège, Belgique, 2017
Neural Architecture Search for Class-incremental Learning
S Huang, V François-Lavet, G Rabusseau
arXiv preprint arXiv:1909.06686, 2019
Sampling diverse neural networks for exploration in reinforcement learning
M Wabartha, A Durand, V François-Lavet, J Pineau
Exploring Continual Learning Using Incremental Architecture Search
S Huang, V François-Lavet, G Rabusseau, J Pineau
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