Matteo Pirotta
Matteo Pirotta
Research Scientist, Facebook AI Research
Verified email at fb.com - Homepage
Title
Cited by
Cited by
Year
Safe policy iteration
M Pirotta, M Restelli, A Pecorino, D Calandriello
International Conference on Machine Learning, 307-315, 2013
562013
Adaptive step-size for policy gradient methods
M Pirotta, M Restelli, L Bascetta
Advances in Neural Information Processing Systems, 1394-1402, 2013
512013
Stochastic variance-reduced policy gradient
M Papini, D Binaghi, G Canonaco, M Pirotta, M Restelli
arXiv preprint arXiv:1806.05618, 2018
422018
Efficient bias-span-constrained exploration-exploitation in reinforcement learning
R Fruit, M Pirotta, A Lazaric, R Ortner
arXiv preprint arXiv:1802.04020, 2018
362018
Policy gradient in lipschitz markov decision processes
M Pirotta, M Restelli, L Bascetta
Machine Learning 100 (2-3), 255-283, 2015
352015
Multi-objective reinforcement learning with continuous pareto frontier approximation
M Pirotta, S Parisi, M Restelli
29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th†…, 2015
352015
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
312014
Near optimal exploration-exploitation in non-communicating markov decision processes
R Fruit, M Pirotta, A Lazaric
Advances in Neural Information Processing Systems, 2994-3004, 2018
222018
Adaptive batch size for safe policy gradients
M Papini, M Pirotta, M Restelli
Advances in Neural Information Processing Systems, 3591-3600, 2017
212017
Boosted fitted q-iteration
S Tosatto, M Pirotta, C d’Eramo, M Restelli
International Conference on Machine Learning, 3434-3443, 2017
202017
Inverse reinforcement learning through policy gradient minimization
M Pirotta, M Restelli
AAAI Conference on Artificial Intelligence, 1993-1999, 2016
182016
Policy search for the optimal control of Markov Decision Processes: A novel particle-based iterative scheme
G Manganini, M Pirotta, M Restelli, L Piroddi, M Prandini
IEEE transactions on cybernetics 46 (11), 2643-2655, 2015
172015
Compatible reward inverse reinforcement learning
AM Metelli, M Pirotta, M Restelli
Advances in neural information processing systems, 2050-2059, 2017
142017
Importance weighted transfer of samples in reinforcement learning
A Tirinzoni, A Sessa, M Pirotta, M Restelli
arXiv preprint arXiv:1805.10886, 2018
132018
Manifold-based multi-objective policy search with sample reuse
S Parisi, M Pirotta, J Peters
Neurocomputing 263, 3-14, 2017
132017
Estimating the maximum expected value in continuous reinforcement learning problems
C D'Eramo, A Nuara, M Pirotta, M Restelli
31st AAAI Conference on Artificial Intelligence, AAAI 2017, 1840-1846, 2017
132017
Multi-objective reinforcement learning through continuous pareto manifold approximation
S Parisi, M Pirotta, M Restelli
Journal of Artificial Intelligence Research 57, 187-227, 2016
132016
Frequentist regret bounds for randomized least-squares value iteration
A Zanette, D Brandfonbrener, E Brunskill, M Pirotta, A Lazaric
International Conference on Artificial Intelligence and Statistics, 1954-1964, 2020
82020
Smoothing policies and safe policy gradients
M Papini, M Pirotta, M Restelli
arXiv preprint arXiv:1905.03231, 2019
82019
Regret minimization in mdps with options without prior knowledge
R Fruit, M Pirotta, A Lazaric, E Brunskill
Advances in Neural Information Processing Systems, 3166-3176, 2017
82017
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