Vincent François-Lavet

According to our database, Vincent François-Lavet authored at least 36 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey.

Amjad Yousef Majid, Serge Saaybi, Vincent François-Lavet, R. Venkatesha Prasad, Chris J. M. Verhoeven

IEEE Trans. Neural Networks Learn. Syst., September, 2024

Guideline-informed reinforcement learning for mechanical ventilation in critical care.

Floris den Hengst, Martijn Otten, Paul W. G. Elbers, Frank van Harmelen, Vincent François-Lavet, Mark Hoogendoorn

Artif. Intell. Medicine, January, 2024

Comparative performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data.

Ameet R. Jagesar, Martijn Otten, Tariq A. Dam, Laurens Biesheuvel, Dave A. Dongelmans, Sylvia Brinkman, Patrick J. Thoral, Vincent François-Lavet, Armand R. J. Girbes, Nicolette de Keizer, Harm-Jan de Grooth, Paul W. G. Elbers

Int. J. Medical Informatics, 2024

Leveraging Knowledge Graph-Based Human-Like Memory Systems to Solve Partially Observable Markov Decision Processes.

Taewoon Kim, Vincent François-Lavet, Michael Cochez

CoRR, 2024

Latent Assistance Networks: Rediscovering Hyperbolic Tangents in RL.

Jacob E. Kooi, Mark Hoogendoorn, Vincent François-Lavet

CoRR, 2024

2023

Disentangled (Un)Controllable Features.

Jacob E. Kooi, Mark Hoogendoorn, Vincent François-Lavet

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

Improving Generalization in Reinforcement Learning Through Forked Agents.

Olivier Moulin, Vincent François-Lavet, Paul W. G. Elbers, Mark Hoogendoorn

Proceedings of the Advances and Trends in Artificial Intelligence. Theory and Applications, 2023

A Meta-Reinforcement Learning Algorithm for Causal Discovery.

Andreas W. M. Sauter, Erman Acar, Vincent François-Lavet

Proceedings of the Conference on Causal Learning and Reasoning, 2023

A Machine with Short-Term, Episodic, and Semantic Memory Systems.

Taewoon Kim, Michael Cochez, Vincent François-Lavet, Mark A. Neerincx, Piek Vossen

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Planning for potential: efficient safe reinforcement learning.

Floris den Hengst, Vincent François-Lavet, Mark Hoogendoorn, Frank van Harmelen

Mach. Learn., 2022

Improving generalization in reinforcement learning through forked agents.

Olivier Moulin, Vincent François-Lavet, Mark Hoogendoorn

CoRR, 2022

Improving adaptability to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents.

Olivier Moulin, Vincent François-Lavet, Paul W. G. Elbers, Mark Hoogendoorn

CoRR, 2022

A Machine With Human-Like Memory Systems.

Taewoon Kim, Michael Cochez, Vincent François-Lavet, Mark A. Neerincx, Piek Vossen

CoRR, 2022

Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents.

Olivier Moulin, Vincent François-Lavet, Paul W. G. Elbers, Mark Hoogendoorn

Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 2022

Reinforcement Learning with Option Machines.

Floris den Hengst, Vincent François-Lavet, Mark Hoogendoorn, Frank van Harmelen

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021

Component Transfer Learning for Deep RL Based on Abstract Representations.

Geoffrey van Driessel, Vincent François-Lavet

CoRR, 2021

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey.

Amjad Yousef Majid, Serge Saaybi, Tomas van Rietbergen, Vincent François-Lavet, R. Venkatesha Prasad, Chris J. M. Verhoeven

CoRR, 2021

Domain Adversarial Reinforcement Learning.

Bonnie Li, Vincent François-Lavet, Thang Doan, Joelle Pineau

CoRR, 2021

Understanding Capacity Saturation in Incremental Learning.

Shenyang Huang, Vincent François-Lavet, Guillaume Rabusseau

Proceedings of the 34th Canadian Conference on Artificial Intelligence, 2021

2020

RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning.

Stefano Alletto, Shenyang Huang, Vincent François-Lavet, Yohei Nakata, Guillaume Rabusseau

CoRR, 2020

Novelty Search in Representational Space for Sample Efficient Exploration.

Ruo Yu Tao, Vincent François-Lavet, Joelle Pineau

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks.

Maxime Wabartha, Audrey Durand, Vincent François-Lavet, Joelle Pineau

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract).

Vincent François-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

2019

On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability.

Vincent François-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau

J. Artif. Intell. Res., 2019

Neural Architecture Search for Class-incremental Learning.

Shenyang Huang, Vincent François-Lavet, Guillaume Rabusseau

CoRR, 2019

Combined Reinforcement Learning via Abstract Representations.

Vincent François-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

An Introduction to Deep Reinforcement Learning.

Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau

Found. Trends Mach. Learn., 2018

Reward Estimation for Variance Reduction in Deep Reinforcement Learning.

Joshua Romoff, Alexandre Piché, Peter Henderson, Vincent François-Lavet, Joelle Pineau

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Contributions to deep reinforcement learning and its applications in smartgrids.

Vincent François-Lavet

PhD thesis, 2017

On overfitting and asymptotic bias in batch reinforcement learning with partial observability.

Vincent François-Lavet, Damien Ernst, Raphael Fonteneau

CoRR, 2017

Approximate Bayes Optimal Policy Search using Neural Networks.

Michael Castronovo, Vincent François-Lavet, Raphaël Fonteneau, Damien Ernst, Adrien Couëtoux

Proceedings of the 9th International Conference on Agents and Artificial Intelligence, 2017

2015

How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies.

Vincent François-Lavet, Raphaël Fonteneau, Damien Ernst

CoRR, 2015

Imitative Learning for Online Planning in Microgrids.

Samy Aittahar, Vincent François-Lavet, Stefan Lodeweyckx, Damien Ernst, Raphaël Fonteneau

Proceedings of the Data Analytics for Renewable Energy Integration, 2015

2014

Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging.

Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst, Pierre Geurts

Proceedings of the Neural Connectomics Workshop at ECML 2014, 2014

Using approximate dynamic programming for estimating the revenues of a hydrogen-based high-capacity storage device.

Vincent François-Lavet, Raphaël Fonteneau, Damien Ernst

Proceedings of the 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, 2014

2013

An energy-based variational model of ferromagnetic hysteresis for finite element computations.

Vincent François-Lavet, François Henrotte, Laurent Stainier, Ludovic Noels, Christophe Geuzaine

J. Comput. Appl. Math., 2013