3 citations · 11 across the 26 of their papers we have counts for
4 papers · 1 filter
Reward-Free Policy Space Compression for Reinforcement Learning
Mirco Mutti, Stefano Del Col, Marcello Restelli
In reinforcement learning, we encode the potential behaviors of an agent interacting with an environment into an infinite set of policies, the policy space, typically represented b…
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…