| The Importance of Non-Markovianity in Maximum State Entropy Exploration M Mutti, R De Santi, M Restelli ICML 2022, 2022 | 17 | 2022 |
| Challenging Common Assumptions in Convex Reinforcement Learning M Mutti, R De Santi, P De Bartolomeis, M Restelli NeurIPS 2022, 2022 | 11 | 2022 |
| Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization M Mutti, R De Santi, E Rossi, JF Calderon, M Bronstein, M Restelli AAAI 2022, 2022 | 8* | 2022 |
| Non-Markovian Policies for Unsupervised Reinforcement Learning in Multiple Environments P Maldini, M Mutti, R De Santi, M Restelli ICML 2022 Workshop: First Workshop on Pre-training: Perspectives, Pitfalls …, 0 | 1* | |
| 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 |
| Convex Reinforcement Learning in Finite Trials M Mutti, R De Santi, P De Bartolomeis, M Restelli Journal of Machine Learning Research 24 (250), 1-42, 2023 | | 2023 |
Marcello RestelliAssociate Professor, Politecnico di MilanoVerified email at polimi.it
