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Number of items: 5.

Parisi, S. and Ramstedt, S. and Peters, J. (2017):
Goal-Driven Dimensionality Reduction for Reinforcement Learning.
Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), [Conference or Workshop Item]

Parisi, S. and Pirotta, M. and Peters, J. (2017):
Manifold-based Multi-objective Policy Search with Sample Reuse.
In: Neurocomputing, 263, pp. 3-14. ISSN 0925-2312,
[Article]

Tangkaratt, V. and van Hoof, H. and Parisi, S. and Neumann, G. and Peters, J. and Sugiyama, M. (2017):
Policy Search with High-Dimensional Context Variables.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), [Conference or Workshop Item]

Parisi, S. and Blank, A. and Viernickel,, T. and Peters, J. (2016):
Local-utopia Policy Selection for Multi-objective Reinforcement Learning.
In: Proceedings of the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL),
[Conference or Workshop Item]

Parisi, S. and Abdulsamad, H. and Paraschos, A. and Daniel, C. and Peters, J. (2015):
Reinforcement Learning vs Human Programming in Tetherball Robot Games.
In: Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS),
[Conference or Workshop Item]

This list was generated on Sat May 15 00:24:55 2021 CEST.