Marcello Restelli
Marcello Restelli
Associate Professor, Politecnico di Milano
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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
Tree‐based reinforcement learning for optimal water reservoir operation
A Castelletti, S Galelli, M Restelli, R Soncini‐Sessa
Water Resources Research 46 (9), 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
Reinforcement learning in continuous action spaces through sequential Monte Carlo methods
A Lazaric, M Restelli, A Bonarini
In: Adv. Neural Information Proc. Systems, 2007
Stochastic variance-reduced policy gradient
M Papini, D Binaghi, G Canonaco, M Pirotta, M Restelli
International Conference on Machine Learning, 4023-4032, 2018
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
Safe Policy Iteration
M Pirotta, M Restelli, A Pecorino, D Calandriello
Proceedings of the 30th international conference on Machine learning, 307-315, 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
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
Policy optimization via importance sampling
AM Metelli, M Papini, F Faccio, M Restelli
Advances in Neural Information Processing Systems 31, 2018
Policy gradient in Lipschitz Markov Decision Processes
M Pirotta, M Restelli, L Bascetta
Machine Learning 100 (2), 255-283, 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
Coherent transport of quantum states by deep reinforcement learning
R Porotti, D Tamascelli, M Restelli, E Prati
Communications Physics 2 (1), 61, 2019
Adaptive step-size for policy gradient methods
M Pirotta, M Restelli, L Bascetta
Advances in Neural Information Processing Systems 26, 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
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
Sparse multi-task reinforcement learning
D Calandriello, A Lazaric, M Restelli
Advances in neural information processing systems 27, 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
Quantum compiling by deep reinforcement learning
L Moro, MGA Paris, M Restelli, E Prati
Communications Physics 4 (1), 178, 2021
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
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