| A survey of preference-based reinforcement learning methods C Wirth, R Akrour, G Neumann, J Fürnkranz Journal of Machine Learning Research 18 (136), 1-46, 2017 | 449 | 2017 |
| April: Active preference learning-based reinforcement learning R Akrour, M Schoenauer, M Sebag Machine Learning and Knowledge Discovery in Databases: European Conference …, 2012 | 218 | 2012 |
| Preference-based policy learning R Akrour, M Schoenauer, M Sebag Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 209 | 2011 |
| Programming by feedback R Akrour, M Schoenauer, M Sebag, JC Souplet International Conference on Machine Learning 32, 1503-1511, 2014 | 89* | 2014 |
| Model-free trajectory optimization for reinforcement learning R Akrour, G Neumann, H Abdulsamad, A Abdolmaleki International Conference on Machine Learning, 2961-2970, 2016 | 54 | 2016 |
| Model-free trajectory-based policy optimization with monotonic improvement R Akrour, A Abdolmaleki, H Abdulsamad, J Peters, G Neumann Journal of machine learning research 19 (14), 1-25, 2018 | 32 | 2018 |
| Reinforcement learning based underwater wireless optical communication alignment for autonomous underwater vehicles Y Weng, J Pajarinen, R Akrour, T Matsuda, J Peters, T Maki IEEE Journal of Oceanic Engineering 47 (4), 1231-1245, 2022 | 30 | 2022 |
| Compatible natural gradient policy search J Pajarinen, HL Thai, R Akrour, J Peters, G Neumann Machine Learning 108, 1443-1466, 2019 | 30 | 2019 |
| Sample and feedback efficient hierarchical reinforcement learning from human preferences R Pinsler, R Akrour, T Osa, J Peters, G Neumann 2018 IEEE international conference on robotics and automation (ICRA), 596-601, 2018 | 28 | 2018 |
| Continuous action reinforcement learning from a mixture of interpretable experts R Akrour, D Tateo, J Peters | 27* | 2020 |
| Regularizing reinforcement learning with state abstraction R Akrour, F Veiga, J Peters, G Neumann 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018 | 25 | 2018 |
| Local Bayesian optimization of motor skills R Akrour, D Sorokin, J Peters, G Neumann International Conference on Machine Learning, 41-50, 2017 | 24 | 2017 |
| Layered direct policy search for learning hierarchical skills F End, R Akrour, J Peters, G Neumann 2017 IEEE International Conference on Robotics and Automation (ICRA), 6442-6448, 2017 | 22 | 2017 |
| Hierarchical tactile-based control decomposition of dexterous in-hand manipulation tasks F Veiga, R Akrour, J Peters Frontiers in Robotics and AI 7, 521448, 2020 | 19 | 2020 |
| Projections for approximate policy iteration algorithms R Akrour, J Pajarinen, J Peters, G Neumann International Conference on Machine Learning, 181-190, 2019 | 16 | 2019 |
| Interactive robot education R Akrour, M Schoenauer, M Sebag ECML/PKDD Workshop on Reinforcement Learning with Generalized Feedback …, 2013 | 15 | 2013 |
| Interpretable and editable programmatic tree policies for reinforcement learning H Kohler, Q Delfosse, R Akrour, K Kersting, P Preux arXiv preprint arXiv:2405.14956, 2024 | 11 | 2024 |
| An upper bound of the bias of Nadaraya-Watson kernel regression under Lipschitz assumptions S Tosatto, R Akrour, J Peters Stats 4 (1), 1-17, 2020 | 6 | 2020 |
| Learning replanning policies with direct policy search F Brandherm, J Peters, G Neumann, R Akrour IEEE Robotics and Automation Letters 4 (2), 2196-2203, 2019 | 6 | 2019 |
| Towards reinforcement learning of human readable policies R Akrour, D Tateo, J Peters The European Conference on Machine Learning and Principles and Practice of …, 2019 | 6 | 2019 |
