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Riad Akrour
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2020 – today
- 2024
- [c12]Mahdi Kallel, Debabrota Basu, Riad Akrour, Carlo D'Eramo:
Augmented Bayesian Policy Search. ICLR 2024 - [i11]Hector Kohler, Quentin Delfosse, Riad Akrour, Kristian Kersting, Philippe Preux:
Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning. CoRR abs/2405.14956 (2024) - [i10]Mahdi Kallel, Debabrota Basu, Riad Akrour, Carlo D'Eramo:
Augmented Bayesian Policy Search. CoRR abs/2407.04864 (2024) - 2023
- [i9]Hector Kohler, Riad Akrour, Philippe Preux:
Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning. CoRR abs/2304.05839 (2023) - [i8]Hector Kohler, Riad Akrour, Philippe Preux:
Discovering the Interpretability-Performance Pareto Front of Decision Trees with Dynamic Programming. CoRR abs/2309.12701 (2023) - 2022
- [j7]Riad Akrour
, Davide Tateo
Continuous Action Reinforcement Learning From a Mixture of Interpretable Experts. IEEE Trans. Pattern Anal. Mach. Intell. 44(10): 6795-6806 (2022) - [i7]Riccardo Della Vecchia, Alena Shilova, Philippe Preux, Riad Akrour:
Entropy Regularized Reinforcement Learning with Cascading Networks. CoRR abs/2210.08503 (2022) - 2021
- [j6]Riad Akrour
Convex optimization with an interpolation-based projection and its application to deep learning. Mach. Learn. 110(8): 2267-2289 (2021) - 2020
- [j5]Filipe Veiga, Riad Akrour, Jan Peters:
Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks. Frontiers Robotics AI 7: 521448 (2020) - [i6]Samuele Tosatto, Riad Akrour, Jan Peters:
An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions. CoRR abs/2001.10972 (2020) - [i5]Riad Akrour, Davide Tateo, Jan Peters:
Reinforcement Learning from a Mixture of Interpretable Experts. CoRR abs/2006.05911 (2020) - [i4]Riad Akrour, Asma Atamna, Jan Peters:
Convex Optimization with an Interpolation-based Projection and its Application to Deep Learning. CoRR abs/2011.07016 (2020)
2010 – 2019
- 2019
- [j4]Joni Pajarinen
Compatible natural gradient policy search. Mach. Learn. 108(8-9): 1443-1466 (2019) - [j3]Florian Brandherm
Learning Replanning Policies With Direct Policy Search. IEEE Robotics Autom. Lett. 4(2): 2196-2203 (2019) - [c11]Riad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumann:
Projections for Approximate Policy Iteration Algorithms. ICML 2019: 181-190 - [i3]Joni Pajarinen, Hong Linh Thai, Riad Akrour, Jan Peters, Gerhard Neumann:
Compatible Natural Gradient Policy Search. CoRR abs/1902.02823 (2019) - 2018
- [j2]Riad Akrour, Abbas Abdolmaleki, Hany Abdulsamad, Jan Peters, Gerhard Neumann:
Model-Free Trajectory-based Policy Optimization with Monotonic Improvement. J. Mach. Learn. Res. 19: 14:1-14:25 (2018) - [c10]Robert Pinsler, Riad Akrour, Takayuki Osa, Jan Peters, Gerhard Neumann:
Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences. ICRA 2018: 596-601 - [c9]Riad Akrour, Filipe Veiga, Jan Peters, Gerhard Neumann:
Regularizing Reinforcement Learning with State Abstraction. IROS 2018: 534-539 - 2017
- [j1]Christian Wirth, Riad Akrour, Gerhard Neumann, Johannes Fürnkranz:
A Survey of Preference-Based Reinforcement Learning Methods. J. Mach. Learn. Res. 18: 136:1-136:46 (2017) - [c8]Riad Akrour, Dmitry Sorokin, Jan Peters, Gerhard Neumann:
Local Bayesian Optimization of Motor Skills. ICML 2017: 41-50 - [c7]Alexander Gabriel
Empowered skills. ICRA 2017: 6435-6441 - [c6]Felix End, Riad Akrour, Jan Peters, Gerhard Neumann
Layered direct policy search for learning hierarchical skills. ICRA 2017: 6442-6448 - 2016
- [c5]Riad Akrour, Gerhard Neumann, Hany Abdulsamad, Abbas Abdolmaleki:
Model-Free Trajectory Optimization for Reinforcement Learning. ICML 2016: 2961-2970 - [c4]Michèle Sebag, Riad Akrour
Anti Imitation-Based Policy Learning. ECML/PKDD (2) 2016: 559-575 - [i2]Riad Akrour, Abbas Abdolmaleki, Hany Abdulsamad, Gerhard Neumann:
Model-free Trajectory Optimization for Reinforcement Learning. CoRR abs/1606.09197 (2016) - 2014
- [b1]Riad Akrour:
Robust Preference Learning-based Reinforcement Learning. (Apprentissage par renforcement robuste reposant sur l'apprentissage par préférences). University of Paris-Sud, Orsay, France, 2014 - [c3]Marc Schoenauer, Riad Akrour, Michèle Sebag, Jean-Christophe Souplet:
Programming by Feedback. ICML 2014: 1503-1511 - 2012
- [c2]Riad Akrour, Marc Schoenauer
APRIL: Active Preference Learning-Based Reinforcement Learning. ECML/PKDD (2) 2012: 116-131 - [i1]Riad Akrour
APRIL: Active Preference-learning based Reinforcement Learning. CoRR abs/1208.0984 (2012) - 2011
- [c1]Riad Akrour, Marc Schoenauer
Preference-Based Policy Learning. ECML/PKDD (1) 2011: 12-27
Coauthor Index
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