3 citations · 5 across the 12 of their papers we have counts for
3 papers · 1 filter
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization
Mirco Mutti, Riccardo De Santi, Emanuele Rossi +3
In the sequential decision making setting, an agent aims to achieve systematic generalization over a large, possibly infinite, set of environments. Such environments are modeled as…
The Importance of Non-Markovianity in Maximum State Entropy Exploration
Mirco Mutti, Riccardo De Santi, Marcello Restelli
In the maximum state entropy exploration framework, an agent interacts with a reward-free environment to learn a policy that maximizes the entropy of the expected state visitations…
Challenging Common Assumptions in Convex Reinforcement Learning
Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis +1
The classic Reinforcement Learning (RL) formulation concerns the maximization of a scalar reward function. More recently, convex RL has been introduced to extend the RL formulation…