no code implementations • 21 Nov 2022 • Luca Sabbioni, Luca Al Daire, Lorenzo Bisi, Alberto Maria Metelli, Marcello Restelli
In reinforcement learning, the performance of learning agents is highly sensitive to the choice of time discretization.
no code implementations • 11 May 2022 • Pierre Liotet, Davide Maran, Lorenzo Bisi, Marcello Restelli
When the agent's observations or interactions are delayed, classic reinforcement learning tools usually fail.
1 code implementation • ICML 2020 • Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi, Luca Sabbioni, Marcello Restelli
The choice of the control frequency of a system has a relevant impact on the ability of reinforcement learning algorithms to learn a highly performing policy.
no code implementations • 6 Dec 2019 • Lorenzo Bisi, Luca Sabbioni, Edoardo Vittori, Matteo Papini, Marcello Restelli
In real-world decision-making problems, for instance in the fields of finance, robotics or autonomous driving, keeping uncertainty under control is as important as maximizing expected returns.