Configurable Markov Decision Processes. - Alberto Maria Metelli; Mirco Mutti; Marcello Restelli (ICML, 2018)

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

A Policy Gradient Method for Task-Agnostic Exploration. - Mirco Mutti; Lorenzo Pratissoli; Marcello Restelli (CoRR, 2020)

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

Unsupervised Reinforcement Learning in Multiple Environments. - Mirco Mutti; Mattia Mancassola; Marcello Restelli (AAAI, 2022)

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

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

Challenging Common Assumptions in Convex Reinforcement Learning. - Mirco Mutti; Riccardo De Santi; Piersilvio De Bartolomeis; Marcello Restelli (NeurIPS, 2022)

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 (AAAI, 2023)

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

Persuading Farsighted Receivers in MDPs: the Power of Honesty. - Martino Bernasconi; Matteo Castiglioni; Alberto Marchesi 001; Mirco Mutti (NeurIPS, 2023)

Convex Reinforcement Learning in Finite Trials. - Mirco Mutti; Riccardo De Santi; Piersilvio De Bartolomeis; Marcello Restelli (J. Mach. Learn. Res., 2023)

Unsupervised reinforcement learning via state entropy maximization. - Mirco Mutti (2023)

Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning. - Mirco Mutti; Riccardo De Santi; Marcello Restelli; Alexander Marx 001; Giorgia Ramponi (ICLR, 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 (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 001 (ICML, 2024)

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

How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach. - Filippo Lazzati; Mirco Mutti; Alberto Maria Metelli (NeurIPS, 2024)

The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough. - Riccardo Zamboni; Duilio Cirino; Marcello Restelli; Mirco Mutti (RLJ, 2024)

A Framework for Partially Observed Reward-States in RLHF. - Chinmaya Kausik; Mirco Mutti; Aldo Pacchiano; Ambuj Tewari (CoRR, 2024)

How to Scale Inverse RL to Large State Spaces? A Provably Efficient Approach. - Filippo Lazzati; Mirco Mutti; Alberto Maria Metelli (CoRR, 2024)

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

Reward Compatibility: A Framework for Inverse RL. - Filippo Lazzati; Mirco Mutti; Alberto Maria Metelli (CoRR, 2025)

Towards Principled Multi-Agent Task Agnostic Exploration. - Riccardo Zamboni; Mirco Mutti; Marcello Restelli (CoRR, 2025)

A Classification View on Meta Learning Bandits. - Mirco Mutti; Jeongyeol Kwon; Shie Mannor; Aviv Tamar (CoRR, 2025)

Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Story. - Vincenzo De Paola; Riccardo Zamboni; Mirco Mutti; Marcello Restelli (CoRR, 2025)

State Entropy Regularization for Robust Reinforcement Learning. - Yonatan Ashlag; Uri Koren; Mirco Mutti; Esther Derman; Pierre-Luc Bacon; Shie Mannor (CoRR, 2025)

From Parameters to Behavior: Unsupervised Compression of the Policy Space. - Davide Tenedini; Riccardo Zamboni; Mirco Mutti; Marcello Restelli (CoRR, 2025)