18 citations · 29 across the 16 of their papers we have counts for
17 papers
Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection
Hadrien Crassous, Mohamed Yassine Kabouri, Minahil Raza +2
This paper studies how to adapt a computer vision object detector to an unknown environment under both a robot navigation time and annotation budget constraint. Our approach select…
When (and How) to Trust the Expert: Diagnosing Query-Time Expert-Guided Reinforcement Learning
Yann Berthelot, Philippe Preux, Riad Akrour
Many continuous-control problems ship with a competent but suboptimal controller (a tuned PID, a hand-designed gait). A growing family of methods uses such controllers as queryable…
StaQ: a Finite Memory Approach to Discrete Action Policy Mirror Descent
Alex Davey, Alena Shilova, Brahim Driss +1
In Reinforcement Learning (RL), regularization with a Kullback-Leibler divergence that penalizes large deviations between successive policies has emerged as a popular tool both in…
PB: Preference Space Exploration via Population-Based Methods in Preference-Based Reinforcement Learning
Brahim Driss, Alex Davey, Riad Akrour
Preference-based reinforcement learning (PbRL) has emerged as a promising approach for learning behaviors from human feedback without predefined reward functions. However, current…
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…
Augmented Bayesian Policy Search
Mahdi Kallel, Debabrota Basu, Riad Akrour +1
Deterministic policies are often preferred over stochastic ones when implemented on physical systems. They can prevent erratic and harmful behaviors while being easier to implement…
Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning
Hector Kohler, Quentin Delfosse, Riad Akrour +2
Deep reinforcement learning agents are prone to goal misalignments. The black-box nature of their policies hinders the detection and correction of such misalignments, and the trust…
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…
Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning
Hector Kohler, Riad Akrour, Philippe Preux
Interpretability of AI models allows for user safety checks to build trust in these models. In particular, decision trees (DTs) provide a global view on the learned model and clear…
Entropy Regularized Reinforcement Learning with Cascading Networks
Riccardo Della Vecchia, Alena Shilova, Philippe Preux +1
Deep Reinforcement Learning (Deep RL) has had incredible achievements on high dimensional problems, yet its learning process remains unstable even on the simplest tasks. Deep RL us…
Convex Optimization with an Interpolation-based Projection and its Application to Deep Learning
Riad Akrour, Asma Atamna, Jan Peters
Convex optimizers have known many applications as differentiable layers within deep neural architectures. One application of these convex layers is to project points into a convex…
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts
Riad Akrour, Davide Tateo, Jan Peters
Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators. However, these successes are mostly achiev…
An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions
Samuele Tosatto, Riad Akrour, Jan Peters
The Nadaraya-Watson kernel estimator is among the most popular nonparameteric regression technique thanks to its simplicity. Its asymptotic bias has been studied by Rosenblatt in 1…
Compatible Natural Gradient Policy Search
Joni Pajarinen, Hong Linh Thai, Riad Akrour +2
Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural gradient…
Model-Free Trajectory-based Policy Optimization with Monotonic Improvement
Riad Akrour, Abbas Abdolmaleki, Hany Abdulsamad +2
Many of the recent trajectory optimization algorithms alternate between linear approximation of the system dynamics around the mean trajectory and conservative policy update. One w…
APRIL: Active Preference-learning based Reinforcement Learning
Riad Akrour, Marc Schoenauer, Michèle Sebag
This paper focuses on reinforcement learning (RL) with limited prior knowledge. In the domain of swarm robotics for instance, the expert can hardly design a reward function or demo…