| A practical guide to multi-objective reinforcement learning and planning CF Hayes, R Rădulescu, E Bargiacchi, J Källström, M Macfarlane, ... Autonomous Agents and Multi-Agent Systems 36 (1), 26, 2022 | 258 | 2022 |
| Tree‐based reinforcement learning for optimal water reservoir operation A Castelletti, S Galelli, M Restelli, R Soncini‐Sessa Water Resources Research 46 (9), 2010 | 215 | 2010 |
| Transfer of samples in batch reinforcement learning A Lazaric, M Restelli, A Bonarini Proceedings of the 25th international conference on Machine learning, 544-551, 2008 | 206 | 2008 |
| Reinforcement learning in continuous action spaces through sequential Monte Carlo methods A Lazaric, M Restelli, A Bonarini In: Adv. Neural Information Proc. Systems, 2007 | 194 | 2007 |
| Stochastic variance-reduced policy gradient M Papini, D Binaghi, G Canonaco, M Pirotta, M Restelli International Conference on Machine Learning, 4023-4032, 2018 | 190 | 2018 |
| Safe Policy Iteration M Pirotta, M Restelli, A Pecorino, D Calandriello Proceedings of the 30th international conference on Machine learning, 307-315, 2013 | 127 | 2013 |
| A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run A Castelletti, F Pianosi, M Restelli Water Resources Research 49 (6), 3476-3486, 2013 | 125 | 2013 |
| Sharing Knowledge in Multi-Task Deep Reinforcement Learning C D'Eramo, D Tateo, A Bonarini, M Restelli, J Peters International Conference on Learning Representations, 2020 | 118* | 2020 |
| Automatic error detection and reduction for an odometric sensor based on two optical mice A Bonarini, M Matteucci, M Restelli Proceedings of the 2005 IEEE international conference on robotics and …, 2005 | 111 | 2005 |
| Policy optimization via importance sampling AM Metelli, M Papini, F Faccio, M Restelli Advances in Neural Information Processing Systems 31, 2018 | 106 | 2018 |
| Policy gradient in Lipschitz Markov Decision Processes M Pirotta, M Restelli, L Bascetta Machine Learning 100 (2), 255-283, 2015 | 103 | 2015 |
| Data-driven dynamic emulation modelling for the optimal management of environmental systems A Castelletti, S Galelli, M Restelli, R Soncini-Sessa Environmental Modelling & Software 34, 30-43, 2012 | 98 | 2012 |
| Coherent transport of quantum states by deep reinforcement learning R Porotti, D Tamascelli, M Restelli, E Prati Communications Physics 2 (1), 61, 2019 | 89 | 2019 |
| Adaptive step-size for policy gradient methods M Pirotta, M Restelli, L Bascetta Advances in Neural Information Processing Systems 26, 2013 | 89 | 2013 |
| A kinematic-independent dead-reckoning sensor for indoor mobile robotics A Bonarini, M Matteucci, M Restelli 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2004 | 87 | 2004 |
| Feature selection via mutual information: New theoretical insights M Beraha, AM Metelli, M Papini, A Tirinzoni, M Restelli 2019 international joint conference on neural networks (IJCNN), 1-9, 2019 | 85 | 2019 |
| Sparse multi-task reinforcement learning D Calandriello, A Lazaric, M Restelli Advances in neural information processing systems 27, 2014 | 83 | 2014 |
| Policy gradient approaches for multi-objective sequential decision making S Parisi, M Pirotta, N Smacchia, L Bascetta, M Restelli 2014 International Joint Conference on Neural Networks (IJCNN), 2323-2330, 2014 | 78 | 2014 |
| Multi-objective reinforcement learning with continuous pareto frontier approximation M Pirotta, S Parisi, M Restelli Twenty-Ninth AAAI Conference on Artificial Intelligence, 2928-2934, 2015 | 76 | 2015 |
| Quantum compiling by deep reinforcement learning L Moro, MGA Paris, M Restelli, E Prati Communications Physics 4 (1), 178, 2021 | 75 | 2021 |
Alberto Maria MetelliAssistant Professor, Politecnico di Milano在 polimi.it 的电子邮件经过验证
