1 citations · 1 across the 18 of their papers we have counts for
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Online Dynamic Pricing of Complementary Products
Marco Mussi, Marcello Restelli
Traditional pricing paradigms, once dominated by static models and rule-based heuristics, are increasingly being replaced by dynamic, data-driven approaches powered by machine lear…
Power Grid Control with Graph-Based Distributed Reinforcement Learning
Carlo Fabrizio, Gianvito Losapio, Marco Mussi +2
The necessary integration of renewable energy sources, combined with the expanding scale of power networks, presents significant challenges in controlling modern power grids. Tradi…
Generalized Kernelized Bandits: A Novel Self-Normalized Bernstein-Like Dimension-Free Inequality and Regret Bounds
Alberto Maria Metelli, Simone Drago, Marco Mussi
We study the regret minimization problem in the novel setting of generalized kernelized bandits (GKBs), where we optimize an unknown function belonging to a reproducing kerne…
Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning
Davide Salaorni, Vincenzo De Paola, Samuele Delpero +9
In recent years, \emph{Reinforcement Learning} (RL) has made remarkable progress, achieving superhuman performance in a wide range of simulated environments. As research moves towa…
Learning Deterministic Policies with Policy Gradients in Constrained Markov Decision Processes
Alessandro Montenegro, Leonardo Cesani, Marco Mussi +2
Constrained Reinforcement Learning (CRL) addresses sequential decision-making problems where agents are required to achieve goals by maximizing the expected return while meeting do…
Reusing Trajectories in Policy Gradients Enables Fast Convergence
Alessandro Montenegro, Federico Mansutti, Marco Mussi +2
Policy gradient (PG) methods are a class of effective reinforcement learning algorithms, particularly when dealing with continuous control problems. They rely on fresh on-policy da…
A Refined Analysis of UCBVI
Simone Drago, Marco Mussi, Alberto Maria Metelli
In this work, we provide a refined analysis of the UCBVI algorithm (Azar et al., 2017), improving both the bonus terms and the regret analysis. Additionally, we compare our version…