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)

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

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)

Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods. - Riccardo De Santi; Manish Prajapat; Andreas Krause 001 (ICML, 2024)

Provable Maximum Entropy Manifold Exploration via Diffusion Models. - Riccardo De Santi; Marin Vlastelica; Ya-Ping Hsieh; Zebang Shen; Niao He; Andreas Krause 001 (CoRR, 2025)