3 citations · 16 across the 20 of their papers we have counts for
11 papers · 1 filter
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…
A Reinforcement Learning Approach for Optimal Control in Microgrids
Davide Salaorni, Federico Bianchi, Francesco Trovò +1
The increasing integration of renewable energy sources (RESs) is transforming traditional power grid networks, which require new approaches for managing decentralized energy produc…
Best Arm Identification for Stochastic Rising Bandits
Marco Mussi, Alessandro Montenegro, Francesco Trovó +2
Stochastic Rising Bandits (SRBs) model sequential decision-making problems in which the expected reward of the available options increases every time they are selected. This settin…
Stochastic Rising Bandits
Alberto Maria Metelli, Francesco Trovò, Matteo Pirola +1
This paper is in the field of stochastic Multi-Armed Bandits (MABs), i.e., those sequential selection techniques able to learn online using only the feedback given by the chosen op…
Dynamic Pricing with Volume Discounts in Online Settings
Marco Mussi, Gianmarco Genalti, Alessandro Nuara +3
According to the main international reports, more pervasive industrial and business-process automation, thanks to machine learning and advanced analytic tools, will unlock more tha…
Sequential Information Design: Learning to Persuade in the Dark
Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi +2
We study a repeated information design problem faced by an informed sender who tries to influence the behavior of a self-interested receiver. We consider settings where the receive…
Multi-Armed Bandit Problem with Temporally-Partitioned Rewards: When Partial Feedback Counts
Giulia Romano, Andrea Agostini, Francesco Trovò +2
There is a rising interest in industrial online applications where data becomes available sequentially. Inspired by the recommendation of playlists to users where their preferences…
ARLO: A Framework for Automated Reinforcement Learning
Marco Mussi, Davide Lombarda, Alberto Maria Metelli +2
Automated Reinforcement Learning (AutoRL) is a relatively new area of research that is gaining increasing attention. The objective of AutoRL consists in easing the employment of Re…
Safe Online Bid Optimization with Return on Investment and Budget Constraints
Matteo Castiglioni, Alessandro Nuara, Giulia Romano +3
In online marketing, the advertisers aim to balance achieving high volumes and high profitability. The companies' business units address this tradeoff by maximizing the volumes whi…
Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns
Alessandro Nuara, Francesco Trovò, Nicola Gatti +1
Pay-per-click advertising includes various formats (\emph{e.g.}, search, contextual, social) with a total investment of more than 200 billion USD per year worldwide. An advertiser…
Unimodal Thompson Sampling for Graph-Structured Arms
Stefano Paladino, Francesco Trovò, Marcello Restelli +1
We study, to the best of our knowledge, the first Bayesian algorithm for unimodal Multi-Armed Bandit (MAB) problems with graph structure. In this setting, each arm corresponds to a…