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Riccardo De Santi
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2020 – today
- 2024
- [c6]Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi:
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning. ICLR 2024 - [c5]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 - [c4]Riccardo De Santi, Manish Prajapat, Andreas Krause:
Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods. ICML 2024 - [i6]Riccardo De Santi, Manish Prajapat, Andreas Krause:
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods. CoRR abs/2407.09905 (2024) - [i5]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
- [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) - [c3]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 - [i4]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
- [c2]Mirco Mutti, Riccardo De Santi, Marcello Restelli:
The Importance of Non-Markovianity in Maximum State Entropy Exploration. ICML 2022: 16223-16239 - [c1]Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli:
Challenging Common Assumptions in Convex Reinforcement Learning. NeurIPS 2022 - [i3]Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli:
Challenging Common Assumptions in Convex Reinforcement Learning. CoRR abs/2202.01511 (2022) - [i2]Mirco Mutti, Riccardo De Santi, Marcello Restelli:
The Importance of Non-Markovianity in Maximum State Entropy Exploration. CoRR abs/2202.03060 (2022) - [i1]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)
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