| Policy Optimization via Importance Sampling AM Metelli, M Papini, F Faccio, M Restelli Advances in Neural Information Processing Systems 31, 5442-5454, 2018 | 41 | 2018 |
| Compatible reward inverse reinforcement learning AM Metelli, M Pirotta, M Restelli The Thirty-first Annual Conference on Neural Information Processing Systems …, 2017 | 21 | 2017 |
| Configurable Markov Decision Processes AM Metelli, M Mutti, M Restelli Proceedings of the 35th International Conference on Machine Learning 80 …, 2018 | 20 | 2018 |
| Content-based approaches for cold-start job recommendations M Bianchi, F Cesaro, F Ciceri, M Dagrada, A Gasparin, D Grattarola, ... Proceedings of the Recommender Systems Challenge 2017, 1-5, 2017 | 11 | 2017 |
| Reinforcement learning in configurable continuous environments AM Metelli, E Ghelfi, M Restelli International Conference on Machine Learning, 4546-4555, 2019 | 7 | 2019 |
| Gradient-aware model-based policy search P D'Oro, AM Metelli, A Tirinzoni, M Papini, M Restelli The Thirty-Fourth AAAI Conference on Artificial Intelligence, 3801-3808, 2020 | 6 | 2020 |
| 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 | 6 | 2019 |
| Optimistic policy optimization via multiple importance sampling M Papini, AM Metelli, L Lupo, M Restelli International Conference on Machine Learning, 4989-4999, 2019 | 5 | 2019 |
| Propagating uncertainty in reinforcement learning via wasserstein barycenters AM Metelli, A Likmeta, M Restelli 33rd Conference on Neural Information Processing Systems, NeurIPS 2019, 4335 …, 2019 | 5 | 2019 |
| Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning AM Metelli, F Mazzolini, L Bisi, L Sabbioni, M Restelli International Conference on Machine Learning 119, 6862--6873, 2020 | 4 | 2020 |
| Importance Sampling Techniques for Policy Optimization AM Metelli, M Papini, N Montali, M Restelli Journal of Machine Learning Research 21 (141), 1-75, 2020 | 4 | 2020 |
| Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving A Likmeta, AM Metelli, A Tirinzoni, R Giol, M Restelli, D Romano Robotics and Autonomous Systems 131, 103568, 2020 | 3 | 2020 |
| Policy space identification in configurable environments AM Metelli, G Manneschi, M Restelli arXiv preprint arXiv:1909.03984, 2019 | 3 | 2019 |
| Truly Batch Model-Free Inverse Reinforcement Learning about Multiple Intentions G Ramponi, A Likmeta, AM Metelli, A Tirinzoni, M Restelli International Conference on Artificial Intelligence and Statistics, 2359-2369, 2020 | 1 | 2020 |
| Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems A Likmeta, AM Metelli, G Ramponi, A Tirinzoni, M Giuliani, M Restelli Machine Learning, 1-36, 2021 | | 2021 |
| Online Learning in Non-Cooperative Configurable Markov Decision Process G Ramponi, AM Metelli, A Concetti, M Restelli | | 2021 |
| Policy Optimization as Online Learning with Mediator Feedback AM Metelli, M Papini, P D'Oro, M Restelli arXiv preprint arXiv:2012.08225, 2020 | | 2020 |
| On the use of the policy gradient and Hessian in inverse reinforcement learning AM Metelli, M Pirotta, M Restelli Intelligenza Artificiale 14 (1), 91-124, 2020 | | 2020 |
Marcello RestelliAssociate Professor, Politecnico di MilanoVerified email at polimi.it
