3 papers
cs.LG2026
Learning in Markov Decision Processes with Exogenous Dynamics
Davide Maran, Davide Salaorni, Marcello Restelli
Reinforcement learning algorithms are typically designed for generic Markov Decision Processes (MDPs), where any state-action pair can lead to an arbitrary transition distribution.…
cs.LG2024
Finite Sample Bounds for Non-Parametric Regression: Optimal Sample Efficiency and Space Complexity
Davide Maran, Marcello Restelli
We address the problem of learning an unknown smooth function and its derivatives from noisy pointwise evaluations under the supremum norm. While classical nonparametric regression…
cs.LG2024
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
Davide Maran, Alberto Maria Metelli, Matteo Papini +1
Achieving the no-regret property for Reinforcement Learning (RL) problems in continuous state and action-space environments is one of the major open problems in the field. Existing…