| An introduction to deep reinforcement learning V François-Lavet, P Henderson, R Islam, MG Bellemare, J Pineau Foundations and Trends® in Machine Learning, 2018 | 325 | 2018 |
| Deep reinforcement learning solutions for energy microgrids management V François-Lavet, D Taralla, D Ernst, R Fonteneau EWRL 2016, 2016 | 83 | 2016 |
| How to discount deep reinforcement learning: Towards new dynamic strategies V François-Lavet, R Fonteneau, D Ernst Deep Reinforcement Learning Workshop, NIPS 2015, 2015 | 69 | 2015 |
| 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 | 40 | 2019 |
| 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 | 31 | 2013 |
| Study of passive and active attitude control systems for the OUFTI nanosatellites V Francois-Lavet University of Liège, Faculty of Applied Sciences, Belgium, 2010 | 26 | 2010 |
| 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 | 21 | 2018 |
| 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 | 20 | 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 | 16 | 2016 |
| 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 | 10 | 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 | 10 | 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 | 10 | 2014 |
| 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 | 7 | 2018 |
| Contributions to deep reinforcement learning and its applications in smartgrids V François-Lavet Université de Liège, Liège, Belgique, 2017 | 6 | 2017 |
| 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 | 5 | 2015 |
| 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 | 5 | 2015 |
| Neural architecture search for class-incremental learning S Huang, V François-Lavet, G Rabusseau arXiv preprint arXiv:1909.06686, 2019 | 4 | 2019 |
| DeeR V François-Lavet | 3 | 2016 |
| 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 | 2 | 2017 |
| Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks M Wabartha, A Durand, V Francois-Lavet, J Pineau International Joint Conferences on Artificial Intelligence (IJCAI), 2020 | 1 | 2020 |
Joelle PineauSchool of Computer Science, McGill UniversityVerified email at cs.mcgill.ca
