Former members

• Giorgio Angelotti (PhD 2020-2023, N. Drougard, C. Chanel) — Offline Learning for Planning
• Kaitlin Maile (PhD 2020-2023, D. Wilson, H. Luga) — Neural Architecture Search
• David Bertoin (PhD 2019-2023, S. Gerchinovitz, E. Rachelson) — Generalization in RL
• Valentin Guillet (PhD 2019-2022, C. Aguilar, E. Rachelson) — Neural network distillation for generalization and transfer in Reinforcement Learning
• Sandrine Berger (Post-doc 2019-2021, M. Bauerheim, T. Jardin, E. Rachelson) — Fluid flow control with RL
• Erwan Lecarpentier (Post-doc 2021-2022, D. Wilson) — Image-based CGP for Atari
• Antoine Stevan (PhD track student 2020-2021, E. Rachelson) — Emergence of communication for RL coordination
• Mathis Clautrier (PhD track student 2019-2021, D. Wilson) — Eye-tracking and RL saliency maps
• Thibault Lahire (PhD track student 2020-2021, M. Geist, E. Rachelson) — Importance sampling in Reinforcement Learning
• Ilyass Haloui (PhD 2019-2021, C. Chanel, A. Haït) — Predictive Maintenance via Sequential Decision Making
• François Lamothe (PhD 2018-2021, A. Haït, E. Rachelson) — Unsplittable Multicommodity flows
• Sana Ikli (PhD 2017-2021, C. Mancel, M. Mongeau, X. Olive, E. Rachelson) — Coupling OR and ML methods for Aircraft Landing Scheduling
• Erwan Lecarpentier (PhD 2016-2020, C. Lesire-Cabaniols, G. Infantes, E. Rachelson) — Reinforcement learning in non-stationary environments
• Luca Mossina (PhD 2016-2020, D. Delahaye, E. Rachelson) — Applications of Machine Learning to the Resolution of Recurrent Combinatorial Optimization Problems
• Ankit Chiplunkar (PhD 2015-2017, J. Morlier, E. Rachelson) — Incorporating Prior Information from Engineering Design into Gaussian Process Regression, applications to Aeronautical Engineering
• 2020-2021 interns: C. Cuny, E. Chigot (Evolutionary RL), H. Sanchez (RL saliency maps), R. Garsuault (Robustness to model uncertainties)
• 2019-2020 interns: P. Carfantan (Prioritized Experience Replay in Soft Actor Critic algorithms), Andrea Arroyo-Ramos (Fluid control with RL), P.-L. Saint (Progressive Neural Networks), L. Hervier (co-evolution of agents and environments), T. Cormier (reward-modulated STDP), Paul Templier (Neuroevolution for RL), Pablo Miralles, Vincent Coyette (Domain Adaptation in DuckieTown)
• 2018-2019 interns: Andres Quintela-Quintanilla (robustness and transfer in Deep RL), L. Bertomier, V. Guillet (Neural Consolidation in RL), I. Bouayad (Why is Rainbow sometimes underperforming?), G. Marugan-Rubio (iBoat stall avoidance), E. Dupont (off-policy critics for DDPG)
• 2017-2018 interns: N. Megel, A. Bonet-Munoz, T. Karch (iBoat stall avoidance), F. Brulport, J.-M. Belley, P. Barde (iBoat navigation planning), Augustin Parjadis (Deep TD(lambda)), P. Planeix (Exoskeleton control with Deep RL), J.-J. Simeoni (robustness and transfer in Deep RL), V. Guillet (Deep RL agents)
• 2016-2017 interns: L. Becq, A. Bufort, H. Akhmouch, T. Le Minh, E. Herlaut, N. El Jaafari, S. Ganapathi-Raju, R. Madelaine, E. Lecarpentier (Learning to fly), L. Mossina (Multi-label Naive Bayes Classification)