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ORCIDMirco Mutti
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2020 – today
- 2024
[c16]Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi:
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning. ICLR 2024- [c15]Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli:
Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms. ICML 2024 - [c14]Mirco Mutti, Aviv Tamar:
Test-Time Regret Minimization in Meta Reinforcement Learning. ICML 2024 - [c13]Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause:
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction. ICML 2024 - [c12]Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti:
How to Explore with Belief: State Entropy Maximization in POMDPs. ICML 2024 - [c11]Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti:
The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough. RLC 2024: 676-692 - [i18]Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari:
A Framework for Partially Observed Reward-States in RLHF. CoRR abs/2402.03282 (2024) - [i17]Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli:
Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms. CoRR abs/2402.15392 (2024) - [i16]Mirco Mutti, Aviv Tamar:
Test-Time Regret Minimization in Meta Reinforcement Learning. CoRR abs/2406.02282 (2024) - [i15]Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti:
How to Explore with Belief: State Entropy Maximization in POMDPs. CoRR abs/2406.02295 (2024) - [i14]Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli:
How to Scale Inverse RL to Large State Spaces? A Provably Efficient Approach. CoRR abs/2406.03812 (2024) - [i13]Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti:
The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough. CoRR abs/2406.12795 (2024) - [i12]Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause:
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction. CoRR abs/2407.13364 (2024) - 2023
- [b1]Mirco Mutti:
Unsupervised reinforcement learning via state entropy maximization. University of Bologna, Italy, 2023 - [j1]Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli:
Convex Reinforcement Learning in Finite Trials. J. Mach. Learn. Res. 24: 250:1-250:42 (2023) - [c10]Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael M. Bronstein, Marcello Restelli:
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. AAAI 2023: 9251-9259 - [c9]Alberto Maria Metelli, Mirco Mutti, Marcello Restelli:
A Tale of Sampling and Estimation in Discounted Reinforcement Learning. AISTATS 2023: 4575-4601 - [c8]Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Mirco Mutti:
Persuading Farsighted Receivers in MDPs: the Power of Honesty. NeurIPS 2023 - [i11]Alberto Maria Metelli, Mirco Mutti, Marcello Restelli:
A Tale of Sampling and Estimation in Discounted Reinforcement Learning. CoRR abs/2304.05073 (2023) - [i10]Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Mirco Mutti:
Persuading Farsighted Receivers in MDPs: the Power of Honesty. CoRR abs/2306.12221 (2023) - [i9]Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx
, Giorgia Ramponi:
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning. CoRR abs/2310.07518 (2023) - 2022
- [c7]Mirco Mutti, Mattia Mancassola, Marcello Restelli:
Unsupervised Reinforcement Learning in Multiple Environments. AAAI 2022: 7850-7858 - [c6]Mirco Mutti, Stefano Del Col, Marcello Restelli:
Reward-Free Policy Space Compression for Reinforcement Learning. AISTATS 2022: 3187-3203 - [c5]Mirco Mutti, Riccardo De Santi, Marcello Restelli:
The Importance of Non-Markovianity in Maximum State Entropy Exploration. ICML 2022: 16223-16239 - [c4]Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli:
Challenging Common Assumptions in Convex Reinforcement Learning. NeurIPS 2022 - [i8]Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli:
Challenging Common Assumptions in Convex Reinforcement Learning. CoRR abs/2202.01511 (2022) - [i7]Mirco Mutti, Riccardo De Santi, Marcello Restelli:
The Importance of Non-Markovianity in Maximum State Entropy Exploration. CoRR abs/2202.03060 (2022) - [i6]Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael M. Bronstein, Marcello Restelli:
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization. CoRR abs/2202.06545 (2022) - [i5]Mirco Mutti, Stefano Del Col, Marcello Restelli:
Reward-Free Policy Space Compression for Reinforcement Learning. CoRR abs/2202.11079 (2022) - 2021
- [c3]Mirco Mutti, Lorenzo Pratissoli, Marcello Restelli:
Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate. AAAI 2021: 9028-9036 - [i4]Mirco Mutti, Mattia Mancassola, Marcello Restelli:
Unsupervised Reinforcement Learning in Multiple Environments. CoRR abs/2112.08746 (2021) - 2020
- [c2]Mirco Mutti, Marcello Restelli:
An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies. AAAI 2020: 5232-5239 - [i3]Mirco Mutti, Lorenzo Pratissoli, Marcello Restelli:
A Policy Gradient Method for Task-Agnostic Exploration. CoRR abs/2007.04640 (2020)
2010 – 2019
- 2019
- [i2]Mirco Mutti, Marcello Restelli:
An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies. CoRR abs/1907.04662 (2019) - 2018
- [c1]Alberto Maria Metelli, Mirco Mutti, Marcello Restelli:
Configurable Markov Decision Processes. ICML 2018: 3488-3497 - [i1]Alberto Maria Metelli, Mirco Mutti, Marcello Restelli:
Configurable Markov Decision Processes. CoRR abs/1806.05415 (2018)
Coauthor Index
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last updated on 2024-11-13 23:52 CET by the dblp team
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