1 citations · 3 across the 12 of their papers we have counts for
14 papers · 1 filter
Parameter-Free Heavy-Tailed Bandits
Gianmarco Genalti, Alberto Maria Metelli
Heavy-tailed distributions arise naturally in sequential decision-making problems such as financial investment, online advertising, and network management, where rare but extreme o…
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…
Catoni-Style Change Point Detection for Regret Minimization in Non-Stationary Heavy-Tailed Bandits
Gianmarco Genalti, Sujay Bhatt, Nicola Gatti +1
Regret minimization in stochastic non-stationary bandits gained popularity over the last decade, as it can model a broad class of real-world problems, from advertising to recommend…
Achieving Regret in Average-Reward POMDPs with Known Observation Models
Alessio Russo, Alberto Maria Metelli, Marcello Restelli
We tackle average-reward infinite-horizon POMDPs with an unknown transition model but a known observation model, a setting that has been previously addressed in two limiting ways:…