| An introduction to deep reinforcement learning V François-Lavet, P Henderson, R Islam, MG Bellemare, J Pineau Foundations and Trends® in Machine Learning, 2018 | 2342* | 2018 |
| Deep reinforcement learning solutions for energy microgrids management V François-Lavet, D Taralla, D Ernst, R Fonteneau EWRL 2016, 2016 | 217 | 2016 |
| How to discount deep reinforcement learning: Towards new dynamic strategies V François-Lavet, R Fonteneau, D Ernst Deep RL WS, NIPS 2015, 2015 | 174 | 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 | 127 | 2019 |
| Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey A Majid, S Saaybi, T van Rietbergen, V Francois-Lavet, RV Prasad, ... IEEE Transactions on Neural Networks and Learning Systems, 2023 | 120 | 2023 |
| Novelty Search in representational space for sample efficient exploration RY Tao, V François-Lavet, J Pineau Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020 | 64 | 2020 |
| 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 | 61 | 2013 |
| 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 | 60 | 2018 |
| Domain Adversarial Reinforcement Learning B Li, V François-Lavet, T Doan, J Pineau Deep RL WS, NeurIPS 2020, 2021 | 58 | 2021 |
| 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 | 45 | 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 | 41 | 2018 |
| Study of passive and active attitude control systems for the OUFTI nanosatellites V Francois-Lavet University of Liège, Faculty of Applied Sciences, Belgium, 2010 | 39 | 2010 |
| Contributions to deep reinforcement learning and its applications in smartgrids V François-Lavet PQDT-Global, 2017 | 32 | 2017 |
| Reinforcement learning for radiotherapy dose fractioning automation G Moreau, V François-Lavet, P Desbordes, B Macq Biomedicines 9 (2), 214, 2021 | 31 | 2021 |
| Planning for potential: efficient safe reinforcement learning F Den Hengst, V François-Lavet, M Hoogendoorn, F van Harmelen Machine Learning 111 (6), 2255-2274, 2022 | 29 | 2022 |
| A meta-reinforcement learning algorithm for causal discovery A Sauter, E Acar, V François-Lavet CLeaR (Causal Learning and Reasoning) 2023, 2023 | 26 | 2023 |
| Vectorial incremental nonconservative consistent hysteresis model V François-Lavet, F Henrotte, L Stainier, L Noels, C Geuzaine 5th International Conference on Advanded COmputational Methods in …, 2011 | 26 | 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 9781498719704, 2016 | 24 | 2016 |
| 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 | 23 | 2015 |
| An introduction to deep reinforcement learning. Foundations and Trends® in Machine Learning 11 (3-4): 219–354 V Francois-Lavet, P Henderson, R Islam, MG Bellemare, J Pineau arXiv preprint arXiv:1811.12560, 2018 | 22 | 2018 |
Joelle PineauSchool of Computer Science, McGill University; FAIR, Meta AI; MilaVerified email at cs.mcgill.ca
