no code implementations • 10 May 2024 • Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli
We consider the problem of learning an $\varepsilon$-optimal policy in a general class of continuous-space Markov decision processes (MDPs) having smooth Bellman operators.
no code implementations • 6 Feb 2024 • Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restell
Obtaining no-regret guarantees for reinforcement learning (RL) in the case of problems with continuous state and/or action spaces is still one of the major open challenges in the field.
1 code implementation • 12 Dec 2022 • Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran, Marco Mussi, Marcello Restelli, Nicola Gatti, Alberto Maria Metelli
Autoregressive processes naturally arise in a large variety of real-world scenarios, including stock markets, sales forecasting, weather prediction, advertising, and pricing.
no code implementations • 7 Dec 2022 • Davide Maran, Alberto Maria Metelli, Marcello Restelli
In this paper, we study BC with the goal of providing theoretical guarantees on the performance of the imitator policy in the case of continuous actions.
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.