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Mirco Mutti
Title
Cited by
Cited by
Year
Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate
M Mutti, L Pratissoli, M Restelli
AAAI 2021, 2021
58*2021
Configurable Markov Decision Processes
AM Metelli, M Mutti, M Restelli
ICML 2018, 2018
472018
An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies
M Mutti, M Restelli
AAAI 2020, 2019
222019
Unsupervised Reinforcement Learning in Multiple Environments
M Mutti, M Mancassola, M Restelli
AAAI 2022, 2022
212022
The Importance of Non-Markovianity in Maximum State Entropy Exploration
M Mutti, R De Santi, M Restelli
ICML 2022, 2022
182022
Challenging Common Assumptions in Convex Reinforcement Learning
M Mutti, R De Santi, P De Bartolomeis, M Restelli
NeurIPS 2022, 2022
122022
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization
M Mutti, R De Santi, E Rossi, JF Calderon, M Bronstein, M Restelli
AAAI 2023, 2022
9*2022
Reward-Free Policy Space Compression for Reinforcement Learning
M Mutti, S Del Col, M Restelli
AISTATS 2022, 2022
42022
Convex Reinforcement Learning in Finite Trials
M Mutti, R De Santi, P De Bartolomeis, M Restelli
JMLR 24 (250), 1-42, 2023
32023
A Tale of Sampling and Estimation in Discounted Reinforcement Learning
AM Metelli, M Mutti, M Restelli
AISTATS 2023, 2023
22023
Persuading Farsighted Receivers in MDPs: the Power of Honesty
M Bernasconi, M Castiglioni, A Marchesi, M Mutti
NeurIPS 2023, 2023
12023
Unsupervised Reinforcement Learning via State Entropy Maximization
M Mutti
PhD Thesis, Università di Bologna, 2023
12023
Non-Markovian Policies for Unsupervised Reinforcement Learning in Multiple Environments
P Maldini, M Mutti, R De Santi, M Restelli
First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward at …, 2022
12022
How to Explore with Belief: State Entropy Maximization in POMDPs
R Zamboni, D Cirino, M Restelli, M Mutti
arXiv preprint arXiv:2406.02295, 2024
2024
Test-Time Regret Minimization in Meta Reinforcement Learning
M Mutti, A Tamar
arXiv preprint arXiv:2406.02282, 2024
2024
How to Scale Inverse RL to Large State Spaces? A Provably Efficient Approach
F Lazzati, M Mutti, AM Metelli
arXiv e-prints, arXiv: 2406.03812, 2024
2024
Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms
F Lazzati, M Mutti, AM Metelli
arXiv preprint arXiv:2402.15392, 2024
2024
A Framework for Partially Observed Reward-States in RLHF
C Kausik, M Mutti, A Pacchiano, A Tewari
arXiv preprint arXiv:2402.03282, 2024
2024
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
M Mutti, R De Santi, M Restelli, A Marx, G Ramponi
arXiv preprint arXiv:2310.07518, 2023
2023
Configurable Markov Decision Processes
M Mutti
Master's Thesis, Politecnico di Milano, 2018
2018
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Articles 1–20