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Publications

Brandherm, F.; Peters, J.; Neumann, G.; Akrour, R. (2019). Learning Replanning Policies with Direct Policy Search, IEEE Robotics and Automation Letters (RA-L).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour, R.; Pajarinen, J.; Neumann, G.; Peters, J. (2019). Projections for Approximate Policy Iteration Algorithms, Proceedings of the International Conference on Machine Learning (ICML).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Pinsler, R.; Akrour, R.; Osa, T.; Peters, J.; Neumann, G. (2018). Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour, R.; Abdolmaleki, A.; Abdulsamad, H.; Peters, J.; Neumann, G. (2018). Model-Free Trajectory-based Policy Optimization with Monotonic Improvement, Journal of Machine Learning Research (JMLR).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour, R.; Veiga, F.; Peters, J.; Neumann, G. (2018). Regularizing Reinforcement Learning with State Abstraction, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour, R.; Peters, J.; Neumann, G. (2018). Constraint-Space Projection Direct Policy Search, European Workshops on Reinforcement Learning (EWRL).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

End, F.; Akrour, R.; Peters, J.; Neumann, G. (2017). Layered Direct Policy Search for Learning Hierarchical Skills, Proceedings of the International Conference on Robotics and Automation (ICRA).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Gabriel, A.; Akrour, R.; Peters, J.; Neumann, G. (2017). Empowered Skills, Proceedings of the International Conference on Robotics and Automation (ICRA).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Wirth, C.; Akrour, R.; Fürnkranz, J.; Neumann G. (2017). A Survey of Preference-Based Reinforcement Learning Methods, Journal of Machine Learning Research (JMLR).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour, R.; Sorokin, D.; Peters, J.; Neumann, G. (2017). Local Bayesian Optimization of Motor Skills, Proceedings of the International Conference on Machine Learning (ICML).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour, R.; Abdolmaleki, A.; Abdulsamad, H.; Neumann, G. (2016). Model-Free Trajectory Optimization for Reinforcement Learning, Proceedings of the International Conference on Machine Learning (ICML).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour R.; Schoenauer M.; Souplet J.-C.; Sebag M. (2014). Programming by Feedback, Proceedings of the International Conference on Machine Learning (ICML).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Mayeur B.; Akrour R.; Sebag M. (2014). Direct Value Learning: a Rank-Invariant Approach to Reinforcement Learning, Neural Information Processing Systems Workshop on Autonomously Learning Robots .   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour R.; Schoenauer M.; Sebag M. (2013). On-board Robot Interactive Training, Robotics: Science and Systems Workshop on Active learning in robotics: Exploration, Curiosity, and Interaction .   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour R.; Schoenauer M.; Sebag M. (2012). APRIL: Active Preference-learning based Reinforcement Learning, Proceedings of the European Conference on Machine Learning (ECML/PKDD).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Akrour R.; Schoenauer M.; Sebag M. (2011). Preference-Based Policy Learning, Proceedings of the European Conference on Machine Learning (ECML/PKDD).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

  

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