Publications of Simone Parisi

Journal Papers
Bib Parisi, S.; Tateo, D.; Hensel, M.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2022). Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning, Algorithms, 15, 3, pp.81. Bib Parisi, S.; Tangkaratt, V.; Peters, J.; Khan, M. E. (2019). TD-Regularized Actor-Critic Methods, Machine Learning (MLJ), 108, 8, pp.1467-1501. Bib Parisi, S.; Pirotta, M.; Peters, J. (2017). Manifold-based Multi-objective Policy Search with Sample Reuse, Neurocomputing, 263, pp.3-14. Bib Parisi, S.; Pirotta, M.; Restelli, M. (2016). Multi-objective Reinforcement Learning through Continuous Pareto Manifold Approximation, Journal of Artificial Intelligence Research (JAIR), 57, pp.187-227.

Conference Papers
Bib Tangkaratt, V.; van Hoof, H.; Parisi, S.; Neumann, G.; Peters, J.; Sugiyama, M. (2017). Policy Search with High-Dimensional Context Variables, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). Bib Parisi, S.; Ramstedt, S.; Peters, J. (2017). Goal-Driven Dimensionality Reduction for Reinforcement Learning, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS). Bib Parisi, S; Blank, A; Viernickel T; Peters, J (2016). Local-utopia Policy Selection for Multi-objective Reinforcement Learning, Proceedings of the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL). Bib Parisi, S.; Abdulsamad, H.; Paraschos, A.; Daniel, C.; Peters, J. (2015). Reinforcement Learning vs Human Programming in Tetherball Robot Games, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS). Bib Pirotta, M.; Parisi, S.; Restelli, M. (2015). Multi-Objective Reinforcement Learning with Continuous Pareto Frontier Approximation, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). Bib Parisi, S.; Pirotta, M.; Smacchia, N.; Bascetta, L.; Restelli, M. (2014). Policy gradient approaches for multi-objective sequential decision making, Proceedings of the International Joint Conference on Neural Networks (IJCNN). Bib Parisi, S.; Pirotta, M.; Smacchia, N.; Bascetta, L.; Restelli, M. (2014). Policy gradient approaches for multi-objective sequential decision making: A comparison, Proceedings of the IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).

Workshop Papers
Bib Parisi, S.; Tangkaratt, V.; Peters, J.; Khan M. E. (2018). TD-Regularized Actor-Critic Methods, European Workshop on Reinforcement Learning (EWRL). Bib Simone Parisi, Voot Tangkaratt, Jan Peters (2017). Regularized Contextual Policy Search via Mutual Information, Proceedings of the Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).