R. Akrour

发表

A Survey of Preference-Based Reinforcement Learning Methods

Johannes Fürnkranz, Christian Wirth, Gerhard Neumann, 2017, J. Mach. Learn. Res..

An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions pdf

Jan Peters, Riad Akrour, Samuele Tosatto, 2020, Stats.

Model-Free Trajectory Optimization for Reinforcement Learning

Hany Abdulsamad, Gerhard Neumann, Abbas Abdolmaleki, 2016, ICML.

Preference Learning: Problems and Applications in Ai Preference Learning: Problems and Applications in Ai (pl-12) Contents a Preliminary Study on a Recommender System for the Million Songs Dataset Using and Learning Gai-decompositions for Representing Ordinal Rankings Alleviating Cold-user Start Pro

Marc Schoenauer, Johannes Fürnkranz, E. Hüllermeier, 2012 .

Preference-based Reinforcement Learning

Marc Schoenauer, R. Akrour, Michèle Sebag Tao, 2011 .

APRIL : Active Preference-based Reinforcement Learning

Marc Schoenauer, R. Akrour, Michèle Sebag Tao, 2012 .

Entropy Regularized Reinforcement Learning with Cascading Networks

Riccardo Della Vecchia, P. Preux, R. Akrour, 2022, ArXiv.

Reinforcement Learning Based Underwater Wireless Optical Communication Alignment for Autonomous Underwater Vehicles

J. Pajarinen, R. Akrour, T. Maki, 2022, IEEE Journal of Oceanic Engineering.

Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks

Jan Peters, Filipe Veiga, Riad Akrour, 2020, Frontiers in Robotics and AI.

Programming by Feedback

Michèle Sebag, Marc Schoenauer, Riad Akrour, 2014, ICML.

Interactive Robot Education

Michèle Sebag, Marc Schoenauer, Riad Akrour, 2013 .

Layered direct policy search for learning hierarchical skills

Jan Peters, Gerhard Neumann, Riad Akrour, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Empowered skills

Jan Peters, Gerhard Neumann, Alexander Gabriel, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

APRIL : Active Preference-based Reinforcement Learning

Marc Schoenauer, R. Akrour, Michèle Sebag Tao, 2012 .

Convex optimization with an interpolation-based projection and its application to deep learning pdf

Asma Atamna, Jan Peters, Riad Akrour, 2020, Machine Learning.

APRIL: Active Preference-learning based Reinforcement Learning pdf

Michèle Sebag, Marc Schoenauer, Riad Akrour, 2012, ECML/PKDD.

Local Bayesian Optimization of Motor Skills

Jan Peters, Gerhard Neumann, Riad Akrour, 2017, ICML.

Projections for Approximate Policy Iteration Algorithms

Jan Peters, Gerhard Neumann, Joni Pajarinen, 2019, ICML.

Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences

Jan Peters, Gerhard Neumann, Takayuki Osa, 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Regularizing Reinforcement Learning with State Abstraction

Jan Peters, Gerhard Neumann, Filipe Veiga, 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Preference-Based Policy Learning

Michèle Sebag, Marc Schoenauer, Riad Akrour, 2011, ECML/PKDD.

Continuous Action Reinforcement Learning From a Mixture of Interpretable Experts pdf

Jan Peters, Riad Akrour, Davide Tateo, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning Replanning Policies With Direct Policy Search

Jan Peters, Florian Brandherm, Gerhard Neumann, 2019, IEEE Robotics and Automation Letters.

Compatible natural gradient policy search

Jan Peters, Gerhard Neumann, Joni Pajarinen, 2019, Machine Learning.

Constraint-Space Projection Direct Policy Search

Jan Peters, Gerhard Neumann, Riad Akrour, 2018, EWRL 2018.

Model-Free Trajectory Optimization with Monotonic Improvement

Hany Abdulsamad, Gerhard Neumann, Abbas Abdolmaleki, 2016 .

Model-Free Trajectory-based Policy Optimization with Monotonic Improvement

Jan Peters, Hany Abdulsamad, Gerhard Neumann, 2016, J. Mach. Learn. Res..

Direct Value Learning: a Rank-Invariant Approach to Reinforcement Learning

Michèle Sebag, Riad Akrour, Basile Mayeur, 2014, NIPS 2014.

Direct Value Learning: a Preference-based Approach to Reinforcement Learning

Michèle Sebag, Marc Schoenauer, Yutaka Deguchi, 2012 .

Preference-Based Policy Learning

M. Sebag, Marc Schoenauer, D. Malerba, 2017 .

Direct Value Learning: Reinforcement Learning and Anti-Imitation

Michèle Sebag, Riad Akrour, Basile Mayeur, 2015 .

Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs

P. Preux, R. Akrour, H. Kohler, 2023, ArXiv.

Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning

P. Preux, R. Akrour, H. Kohler, 2023, ArXiv.

Preference-Based Policy Learning

R. Akrour, Michèle Sebag, Marc Schoenauer, 2011, ECML/PKDD.

Robust Preference Learning-based Reinforcement Learning. (Apprentissage par renforcement robuste reposant sur l'apprentissage par préférences)

Riad Akrour, R. Akrour, 2014 .

Preference-Based Reinforcement Learning Using Dyad Ranking

Marc Schoenauer, E. Hüllermeier, R. Akrour, 2018, DS.

Continuous Action Reinforcement Learning From a Mixture of Interpretable Experts

Jan Peters, Riad Akrour, Davide Tateo, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Reinforcement Learning from a Mixture of Interpretable Experts

R. Akrour, Davide Tateo, Jan Peters, 2020, ArXiv.

APRIL: Active Preference-learning based Reinforcement Learning

M. Sebag, Marc Schoenauer, R. Akrour, 2012, ECML/PKDD.

Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning

R. Akrour, Quentin Delfosse, Hector Kohler, 2024, ArXiv.