A Simulation-based Approach for Solving Generalized Semi-Markov Decision Processes. - Emmanuel Rachelson; Gauthier Quesnel; Frédérick Garçia; Patrick Fabiani (ECAI, 2008)

Extending the Bellman equation for MDPs to continuous actions and cont. time in the discounted case. - Emmanuel Rachelson; Frédérick Garçia; Patrick Fabiani (ISAIM, 2008)

TiMDPpoly: An Improved Method for Solving Time-Dependent MDPs. - Emmanuel Rachelson; Patrick Fabiani; Frédérick Garçia (ICTAI, 2009)

Combining Mixed Integer Programming and Supervised Learning for Fast Re-planning. - Emmanuel Rachelson; Ala Ben Abbes; Sebastien Diemer (ICTAI (2), 2010)

On the locality of action domination in sequential decision making. - Emmanuel Rachelson; Michail G. Lagoudakis (ISAIM, 2010)

Optimal Sample Selection for Batch-mode Reinforcement Learning. - Emmanuel Rachelson; François Schnitzler; Louis Wehenkel; Damien Ernst (ICAART (1), 2011)

Formal Detection of Attentional Tunneling in Human Operator-Automation Interactions. - Nicolas Regis; Frédéric Dehais; Emmanuel Rachelson; Charles Thooris; Sergio Pizziol; Mickaël Causse; Catherine Tessier (IEEE Trans. Hum. Mach. Syst., 2014)

Sparse Physics-based Gaussian Process for Multi-output Regression using Variational Inference. - Ankit Chiplunkar; Emmanuel Rachelson; Michele Colombo; Joseph Morlier (ICPRAM, 2016)

Approximate Inference in Related Multi-output Gaussian Process Regression. - Ankit Chiplunkar; Emmanuel Rachelson; Michele Colombo; Joseph Morlier (ICPRAM (Revised Selected Papers), 2016)

Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring. - Erwan Lecarpentier; Sebastian Rapp; Marc Melo; Emmanuel Rachelson (CoRR, 2017)

Naive Bayes Classification for Subset Selection. - Luca Mossina; Emmanuel Rachelson (CoRR, 2017)

Open Loop Execution of Tree-Search Algorithms. - Erwan Lecarpentier; Guillaume Infantes; Charles Lesire; Emmanuel Rachelson (IJCAI, 2018)

Multi-label Classification for the Generation of Sub-problems in Time-constrained Combinatorial Optimization. - Luca Mossina; Emmanuel Rachelson; Daniel Delahaye (ICORES, 2019)

Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning. - Erwan Lecarpentier; Emmanuel Rachelson (NeurIPS, 2019)

A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem. - Luca Mossina; Emmanuel Rachelson; Daniel Delahaye (CoRR, 2019)

Learning to handle parameter perturbations in Combinatorial Optimization: An application to facility location. - Andrea Lodi 001; Luca Mossina; Emmanuel Rachelson (EURO J. Transp. Logist., 2020)

Lipschitz Lifelong Reinforcement Learning. - Erwan Lecarpentier; David Abel; Kavosh Asadi; Yuu Jinnai; Emmanuel Rachelson; Michael L. Littman (AAAI, 2021)

A geometric encoding for neural network evolution. - Paul Templier; Emmanuel Rachelson; Dennis G. Wilson (GECCO, 2021)

The aircraft runway scheduling problem: A survey. - Sana Ikli; Catherine Mancel; Marcel Mongeau; Xavier Olive; Emmanuel Rachelson (Comput. Oper. Res., 2021)

Randomized rounding algorithms for large scale unsplittable flow problems. - François Lamothe; Emmanuel Rachelson; Alain Haït; Cédric Baudoin; Jean-Baptiste Dupe (J. Heuristics, 2021)

When, where, and how to add new neurons to ANNs. - Kaitlin Maile; Emmanuel Rachelson; Hervé Luga; Dennis George Wilson (AutoML, 2022)

Neural Distillation as a State Representation Bottleneck in Reinforcement Learning. - Valentin Guillet; Dennis George Wilson; Emmanuel Rachelson (CoLLAs, 2022)

LUCIE: an evaluation and selection method for stochastic problems. - Erwan Lecarpentier; Paul Templier; Emmanuel Rachelson; Dennis G. Wilson (GECCO, 2022)

Local Feature Swapping for Generalization in Reinforcement Learning. - David Bertoin; Emmanuel Rachelson (ICLR, 2022)

Large Batch Experience Replay. - Thibault Lahire; Matthieu Geist; Emmanuel Rachelson (ICML, 2022)

Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning. - David Bertoin; Adil Zouitine; Mehdi Zouitine; Emmanuel Rachelson (NeurIPS, 2022)

On Neural Consolidation for Transfer in Reinforcement Learning. - Valentin Guillet; Dennis Wilson; Carlos Aguilar Melchor; Emmanuel Rachelson (SSCI, 2022)

Look where you look! Saliency-guided Q-networks for visual RL tasks. - David Bertoin; Adil Zouitine; Mehdi Zouitine; Emmanuel Rachelson (CoRR, 2022)

Neural Distillation as a State Representation Bottleneck in Reinforcement Learning. - Valentin Guillet; Dennis G. Wilson; Carlos Aguilar Melchor; Emmanuel Rachelson (CoRR, 2022)

Dynamic unsplittable flows with path-change penalties: New formulations and solution schemes for large instances. - François Lamothe; Emmanuel Rachelson; Alain Haït; Cédric Baudoin; Jean-Baptiste Dupe (Comput. Oper. Res., 2023)

Curiosity Creates Diversity in Policy Search. - Paul-Antoine Le Tolguenec; Emmanuel Rachelson; Yann Besse; Dennis G. Wilson (ACM Trans. Evol. Learn. Optim., 2023)

Genetic Drift Regularization: On Preventing Actor Injection from Breaking Evolution Strategies. - Paul Templier; Emmanuel Rachelson; Antoine Cully; Dennis G. Wilson (CEC, 2024)

Learning Heuristics for Combinatorial Optimization Problems on K-Partite Hypergraphs. - Mehdi Zouitine; Ahmad Berjaoui; Agnès Lagnoux; Clément Pellegrini; Emmanuel Rachelson (CPAIOR (2), 2024)

Quality with Just Enough Diversity in Evolutionary Policy Search. - Paul Templier; Luca Grillotti; Emmanuel Rachelson; Dennis Wilson; Antoine Cully (GECCO, 2024)

Summary of "Curiosity creates Diversity in Policy Search". - Paul-Antoine Le Tolguenec; Emmanuel Rachelson; Yann Besse; Dennis Wilson (GECCO Companion, 2024)

Exploration-Driven Reinforcement Learning for Avionic System Fault Detection (Experience Paper). - Paul-Antoine Le Tolguenec; Emmanuel Rachelson; Yann Besse; Florent Teichteil-Königsbuch; Nicolas Schneider; Hélène Waeselynck; Dennis Wilson (ISSTA, 2024)

Exploration by Learning Diverse Skills through Successor State Representations. - Paul-Antoine Le Tolguenec; Yann Besse; Florent Teichteil-Königsbuch; Dennis Wilson; Emmanuel Rachelson (NeurIPS, 2024)

Time-Constrained Robust MDPs. - Adil Zouitine; David Bertoin; Pierre Clavier; Matthieu Geist; Emmanuel Rachelson (NeurIPS, 2024)

Bootstrapping Expectiles in Reinforcement Learning. - Pierre Clavier; Emmanuel Rachelson; Erwan Le Pennec; Matthieu Geist (CoRR, 2024)

RRLS : Robust Reinforcement Learning Suite. - Adil Zouitine; David Bertoin; Pierre Clavier; Matthieu Geist; Emmanuel Rachelson (CoRR, 2024)

Exploration by Learning Diverse Skills through Successor State Measures. - Paul-Antoine Le Tolguenec; Yann Besse; Florent Teichteil-Königsbuch; Dennis G. Wilson; Emmanuel Rachelson (CoRR, 2024)

The Overfocusing Bias of Convolutional Neural Networks: A Saliency-Guided Regularization Approach. - David Bertoin; Eduardo Hugo Sanchez; Mehdi Zouitine; Emmanuel Rachelson (CoRR, 2024)

Solving robust MDPs as a sequence of static RL problems. - Adil Zouitine; Matthieu Geist; Emmanuel Rachelson (CoRR, 2024)

Exploration by Running Away from the Past. - Paul-Antoine Le Tolguenec; Yann Besse; Florent Teichteil-Königsbuch; Dennis G. Wilson; Emmanuel Rachelson (CoRR, 2024)

Learning State Reachability as a Graph in Translation Invariant Goal-based Reinforcement Learning Tasks. - Hedwin Bonnavaud; Alexandre Albore; Emmanuel Rachelson (Trans. Mach. Learn. Res., 2024)

Disentanglement by Cyclic Reconstruction. - David Bertoin; Emmanuel Rachelson (IEEE Trans. Neural Networks Learn. Syst., 2024)

Learning Abstract World Models with a Group-Structured Latent Space. - Thomas Delliaux; Nguyen-Khanh Vu; Vincent François-Lavet; Elise van der Pol; Emmanuel Rachelson (CoRR, 2025)

Influence branching for learning to solve mixed-integer programs online. - Paul Strang; Zacharie Alès; Côme Bissuel; Olivier Juan; Safia Kedad-Sidhoum; Emmanuel Rachelson (CoRR, 2025)

A Markov Decision Process for Variable Selection in Branch & Bound. - Paul Strang; Zacharie Alès; Côme Bissuel; Olivier Juan; Safia Kedad-Sidhoum; Emmanuel Rachelson (CoRR, 2025)