Vincent François-Lavet
Zitiert von
Zitiert von
An introduction to deep reinforcement learning
V François-Lavet, P Henderson, R Islam, MG Bellemare, J Pineau
Foundations and Trends® in Machine Learning, 2018
Deep reinforcement learning solutions for energy microgrids management
V François-Lavet, D Taralla, D Ernst, R Fonteneau
EWRL 2016, 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
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
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
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
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
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
Study of passive and active attitude control systems for the OUFTI nanosatellites
V Francois-Lavet
University of Liège, Faculty of Applied Sciences, Belgium, 2010
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
Domain Adversarial Reinforcement Learning
B Li, V François-Lavet, T Doan, J Pineau
Deep RL workshop, NeurIPS 2020, 2021
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
Contributions to deep reinforcement learning and its applications in smartgrids
V François-Lavet
ULiège-Université de Liège, 2017
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
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
An introduction to deep reinforcement learning. arXiv 2018
V Francois-Lavet, P Henderson, R Islam, MG Bellemare, J Pineau
arXiv preprint arXiv:1811.12560, 0
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
A meta-reinforcement learning algorithm for causal discovery
A Sauter, E Acar, V François-Lavet
CLeaR (Causal Learning and Reasoning) 2023, 2023
Reinforcement learning for radiotherapy dose fractioning automation
G Moreau, V François-Lavet, P Desbordes, B Macq
Biomedicines 9 (2), 214, 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
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