Interval Prediction for Continuous-Time Systems with Parametric Uncertainties. - Edouard Leurent; Denis V. Efimov; Tarek Raïssi; Wilfrid Perruquetti (CDC, 2019)

Budgeted Reinforcement Learning in Continuous State Space. - Nicolas Carrara; Edouard Leurent; Romain Laroche; Tanguy Urvoy; Odalric-Ambrym Maillard; Olivier Pietquin (NeurIPS, 2019)

Practical Open-Loop Optimistic Planning. - Edouard Leurent; Odalric-Ambrym Maillard (ECML/PKDD (3), 2019)

Approximate Robust Control of Uncertain Dynamical Systems. - Edouard Leurent; Yann Blanco; Denis V. Efimov; Odalric-Ambrym Maillard (CoRR, 2019)

Scaling up budgeted reinforcement learning. - Nicolas Carrara; Edouard Leurent; Romain Laroche; Tanguy Urvoy; Odalric-Ambrym Maillard; Olivier Pietquin (CoRR, 2019)

Social Attention for Autonomous Decision-Making in Dense Traffic. - Edouard Leurent; Jean Mercat (CoRR, 2019)

Monte-Carlo Graph Search: the Value of Merging Similar States. - Edouard Leurent; Odalric-Ambrym Maillard (ACML, 2020)

Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems. - Edouard Leurent; Denis V. Efimov; Odalric-Ambrym Maillard (CDC, 2020)

Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. - Anders Jonsson 001; Emilie Kaufmann; Pierre Ménard; Omar Darwiche Domingues; Edouard Leurent; Michal Valko (NeurIPS, 2020)

Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs. - Edouard Leurent; Odalric-Ambrym Maillard; Denis V. Efimov (NeurIPS, 2020)

Robust Estimation, Prediction and Control with Linear Dynamics and Generic Costs. - Edouard Leurent; Denis V. Efimov; Odalric-Ambrym Maillard (CoRR, 2020)

Safe and Efficient Reinforcement Learning for Behavioural Planning in Autonomous Driving. (Apprentissage par renforcement sûr et efficace pour la planification comportementale en conduite autonome). - Edouard Leurent (2020)

Adaptive Reward-Free Exploration. - Emilie Kaufmann; Pierre Ménard; Omar Darwiche Domingues; Anders Jonsson 001; Edouard Leurent; Michal Valko (ALT, 2021)

Fast active learning for pure exploration in reinforcement learning. - Pierre Ménard; Omar Darwiche Domingues; Anders Jonsson 001; Emilie Kaufmann; Edouard Leurent; Michal Valko (ICML, 2021)

Automated Planning for Robotic Guidewire Navigation in the Coronary Arteries. - Pierre Schegg; Jérémie Dequidt; Eulalie Coevoet; Edouard Leurent; Rémi Sabatier; Philippe Preux; Christian Duriez (RoboSoft, 2022)

Optimizing Memory Mapping Using Deep Reinforcement Learning. - Pengming Wang 001; Mikita Sazanovich; Berkin Ilbeyi; Phitchaya Mangpo Phothilimthana; Manish Purohit; Han Yang Tay; Ngân Vu; Miaosen Wang; Cosmin Paduraru; Edouard Leurent; Anton Zhernov; Julian Schrittwieser; et al. (CoRR, 2023)

Diversifying AI: Towards Creative Chess with AlphaZero. - Tom Zahavy; Vivek Veeriah; Shaobo Hou; Kevin Waugh; Matthew Lai; Edouard Leurent; Nenad Tomasev; Lisa Schut; Demis Hassabis; Satinder Singh 001 (CoRR, 2023)

Faster sorting algorithms discovered using deep reinforcement learning. - Daniel J. Mankowitz; Andrea Michi; Anton Zhernov; Marco Gelmi; Marco Selvi; Cosmin Paduraru; Edouard Leurent; Shariq Iqbal; Jean-Baptiste Lespiau; Alex Ahern; Thomas Köppe; Kevin Millikin; et al. (Nat., 2023)

Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning. - Kaiwen Wang; Rahul Kidambi; Ryan Sullivan; Alekh Agarwal; Christoph Dann; Andrea Michi; Marco Gelmi; Yunxuan Li; Raghav Gupta; Avinava Dubey; Alexandre Ramé; Johan Ferret; et al. (EMNLP (Findings), 2024)

Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. - Kaiwen Wang; Rahul Kidambi; Ryan Sullivan; Alekh Agarwal; Christoph Dann; Andrea Michi; Marco Gelmi; Yunxuan Li; Raghav Gupta; Avinava Dubey; Alexandre Ramé; Johan Ferret; et al. (CoRR, 2024)