4 citations · 5 across the 7 of their papers we have counts for
4 papers · 1 filter
Evaluating Interpretable Reinforcement Learning by Distilling Policies into Programs
Hector Kohler, Quentin Delfosse, Waris Radji +2
There exist applications of reinforcement learning like medicine where policies need to be ''interpretable'' by humans. User studies have shown that some policy classes might be mo…
Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs
Hector Kohler, Riad Akrour, Philippe Preux
Interpretability of AI models allows for user safety checks to build trust in such AIs. In particular, Decision Trees (DTs) provide a global look at the learned model and transpare…
Breiman meets Bellman: Non-Greedy Decision Trees with MDPs
Hector Kohler, Riad Akrour, Philippe Preux
In supervised learning, decision trees are valued for their interpretability and performance. While greedy decision tree algorithms like CART remain widely used due to their comput…
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
Timothée Mathieu, Riccardo Della Vecchia, Alena Shilova +4
Recently, the scientific community has questioned the statistical reproducibility of many empirical results, especially in the field of machine learning. To contribute to the resol…