3 citations · 16 across the 20 of their papers we have counts for
21 papers
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…
Thompson Sampling-like Algorithms for Stochastic Rising Bandits
Marco Fiandri, Alberto Maria Metelli, Francesco Trovò
Stochastic rising rested bandit (SRRB) is a setting where the arms' expected rewards increase as they are pulled. It models scenarios in which the performances of the different opt…
Rising Rested Bandits: Lower Bounds and Efficient Algorithms
Marco Fiandri, Alberto Maria Metelli, Francesco Trov`o
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 opt…
Sliding-Window Thompson Sampling for Non-Stationary Settings
Marco Fiandri, Alberto Maria Metelli, Francesco Trovò
Non-stationary multi-armed bandits (NS-MABs) model sequential decision-making problems in which the expected rewards of a set of actions, a.k.a.~arms, evolve over time. In this pap…
Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion
Martino Bernasconi, Matteo Castiglioni, Andrea Celli +3
Bayesian persuasion studies how an informed sender should influence beliefs of rational receivers who take decisions through Bayesian updating of a common prior. We focus on the on…
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…
Constrained Phi-Equilibria
Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi +2
The computational study of equilibria involving constraints on players' strategies has been largely neglected. However, in real-world applications, players are usually subject to c…
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…
Last-iterate Convergence to Trembling-hand Perfect Equilibria
Martino Bernasconi, Alberto Marchesi, Francesco Trovò
Designing efficient algorithms to find Nash equilibrium (NE) refinements in sequential games is of paramount importance in practice. Indeed, it is well known that the NE has severa…
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…
A privacy-preserving tests optimization algorithm for epidemics containment
Alessandro Nuara, Francesco Trovò, Nicola Gatti
The SARS-CoV-2 outbreak changed the everyday life of almost all the people over the world.Currently, we are facing with the problem of containing the spread of the virus both using…
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…
Learning Probably Approximately Correct Maximin Strategies in Simulation-Based Games with Infinite Strategy Spaces
Alberto Marchesi, Francesco Trovò, Nicola Gatti
We tackle the problem of learning equilibria in simulation-based games. In such games, the players' utility functions cannot be described analytically, as they are given through a…
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…
Machine Learning Techniques for Stackelberg Security Games: a Survey
Giuseppe De Nittis, Francesco Trovò
The present survey aims at presenting the current machine learning techniques employed in security games domains. Specifically, we focused on papers and works developed by the Team…
Truthful Learning Mechanisms for Multi-Slot Sponsored Search Auctions with Externalities
Nicola Gatti, Alessandro Lazaric, Marco Rocco +1
Sponsored search auctions constitute one of the most successful applications of microeconomic mechanisms. In mechanism design, auctions are usually designed to incentivize advertis…