Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning. - Hector Kohler; Riad Akrour; Philippe Preux (CoRR, 2023)

AdaStop: sequential testing for efficient and reliable comparisons of Deep RL Agents. - Timothée Mathieu; Riccardo Della Vecchia; Alena Shilova; Matheus Centa de Medeiros; Hector Kohler; Odalric-Ambrym Maillard; Philippe Preux (CoRR, 2023)

Discovering the Interpretability-Performance Pareto Front of Decision Trees with Dynamic Programming. - Hector Kohler; Riad Akrour; Philippe Preux (CoRR, 2023)

Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop. - Hector Kohler; Quentin Delfosse; Paul Festor; Philippe Preux (CoRR, 2024)

PID Tuning using Cross-Entropy Deep Learning: a Lyapunov Stability Analysis. - Hector Kohler; Benoit Clement; Thomas Chaffre; Gilles Le Chenadec (CoRR, 2024)

Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning. - Hector Kohler; Quentin Delfosse; Riad Akrour; Kristian Kersting; Philippe Preux (CoRR, 2024)

AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents. - Timothée Mathieu; Matheus Centa; Riccardo Della Vecchia; Hector Kohler; Alena Shilova; Odalric-Ambrym Maillard; Philippe Preux (Trans. Mach. Learn. Res., 2024)

Evaluating Interpretable Reinforcement Learning by Distilling Policies into Programs. - Hector Kohler; Quentin Delfosse; Waris Radji; Riad Akrour; Philippe Preux (CoRR, 2025)