Mutti, Mirco

34 publications

ICML 2025 A Classification View on Meta Learning Bandits Mirco Mutti, Jeongyeol Kwon, Shie Mannor, Aviv Tamar

ICLR 2025 A Theoretical Framework for Partially-Observed Reward States in RLHF Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari

NeurIPS 2025 Blindfolded Experts Generalize Better: Insights from Robotic Manipulation and Videogames Ev Zisselman, Mirco Mutti, Shelly Francis-Meretzki, Elisei Shafer, Aviv Tamar

ICML 2025 Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Story Vincenzo De Paola, Riccardo Zamboni, Mirco Mutti, Marcello Restelli

NeurIPS 2025 State Entropy Regularization for Robust Reinforcement Learning Yonatan Ashlag, Uri Koren, Mirco Mutti, Esther Derman, Pierre-Luc Bacon, Shie Mannor

NeurIPS 2025 Towards Principled Unsupervised Multi-Agent Reinforcement Learning Riccardo Zamboni, Mirco Mutti, Marcello Restelli

ICMLW 2024 A Theoretical Framework for Partially Observed Reward-States in RLHF Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari

ICMLW 2024 A Theoretical Framework for Partially-Observed Reward States in RLHF Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari

ICLR 2024 Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi

ICML 2024 Geometric Active Exploration in Markov Decision Processes: The Benefit of Abstraction Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause

NeurIPS 2024 How Does Inverse RL Scale to Large State Spaces? a Provably Efficient Approach Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli

ICML 2024 How to Explore with Belief: State Entropy Maximization in POMDPs Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti

ICML 2024 Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli

ICML 2024 Test-Time Regret Minimization in Meta Reinforcement Learning Mirco Mutti, Aviv Tamar

AISTATS 2023 A Tale of Sampling and Estimation in Discounted Reinforcement Learning Alberto Maria Metelli, Mirco Mutti, Marcello Restelli

JMLR 2023 Convex Reinforcement Learning in Finite Trials Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli

NeurIPSW 2023 Exploiting Causal Representations in Reinforcement Learning: A Posterior Sampling Approach Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi

NeurIPS 2023 Persuading Farsighted Receivers in MDPs: The Power of Honesty Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Mirco Mutti

AAAI 2023 Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderón, Michael M. Bronstein, Marcello Restelli

AISTATS 2022 Reward-Free Policy Space Compression for Reinforcement Learning Mirco Mutti, Stefano Del Col, Marcello Restelli

NeurIPS 2022 Challenging Common Assumptions in Convex Reinforcement Learning Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli

ICMLW 2022 Invariance Discovery for Systematic Generalization in Reinforcement Learning Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael M. Bronstein, Marcello Restelli

ICMLW 2022 Non-Markovian Policies for Unsupervised Reinforcement Learning in Multiple Environments Pietro Maldini, Mirco Mutti, Riccardo De Santi, Marcello Restelli

ICMLW 2022 Non-Markovian Policies for Unsupervised Reinforcement Learning in Multiple Environments Pietro Maldini, Mirco Mutti, Riccardo De Santi, Marcello Restelli

NeurIPSW 2022 Provably Efficient Causal Model-Based Reinforcement Learning for Environment-Agnostic Generalization Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael M. Bronstein, Marcello Restelli

ICML 2022 The Importance of Non-Markovianity in Maximum State Entropy Exploration Mirco Mutti, Riccardo De Santi, Marcello Restelli

AAAI 2022 Unsupervised Reinforcement Learning in Multiple Environments Mirco Mutti, Mattia Mancassola, Marcello Restelli

ICMLW 2021 Learning to Explore Multiple Environments Without Rewards Mirco Mutti, Mattia Mancassola, Marcello Restelli

ICMLW 2021 Reward-Free Policy Space Compression for Reinforcement Learning Mirco Mutti, Stefano Del Col, Marcello Restelli

AAAI 2021 Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate Mirco Mutti, Lorenzo Pratissoli, Marcello Restelli

ICMLW 2021 The Importance of Non-Markovianity in Maximum State Entropy Exploration Mirco Mutti, Riccardo De Santi, Marcello Restelli

ICMLW 2020 A Policy Gradient Method for Task-Agnostic Exploration Mirco Mutti, Lorenzo Pratissoli, Marcello Restelli

AAAI 2020 An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies Mirco Mutti, Marcello Restelli

ICML 2018 Configurable Markov Decision Processes Alberto Maria Metelli, Mirco Mutti, Marcello Restelli