1 citations · 1 across the 18 of their papers we have counts for
20 papers
Learning a Ranking from Human Feedback in Log-Concave Random Utility Models
Diego Alovisetti, Marco Mussi, Alberto Maria Metelli
We study the problem of recovering the ranking of a fixed set of items according to their unknown numerical utilities. At each interaction with the environment, a learner presents…
Reusing Past Samples in Proximal Policy Optimization: When and How Does It Help?
Alessandro Montenegro, Riccardo Venturelli, Marco Mussi +2
Among on-policy deep reinforcement learning methods, Proximal Policy Optimization (PPO) has become the de facto standard, due to its consistently strong empirical performance acros…
On Generalizing Preference-based Reinforcement Learning to Multi-Objective Problems
Simone Drago, Marco Mussi, Leonardo Bianconi +1
We study reinforcement learning (RL) from pairwise human-generated trajectory comparisons. We generalize preference-based RL by formalizing a novel setting in which the expert can…
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…
State and Action Factorization in Power Grids
Gianvito Losapio, Davide Beretta, Marco Mussi +2
The increase of renewable energy generation towards the zero-emission target is making the problem of controlling power grids more and more challenging. The recent series of compet…
Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting
Gianmarco Genalti, Marco Mussi, Nicola Gatti +3
Rested and Restless Bandits are two well-known bandit settings that are useful to model real-world sequential decision-making problems in which the expected reward of an arm evolve…
Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with Bernoulli Rewards
Marco Mussi, Simone Drago, Alberto Maria Metelli
We consider Kernelized Bandits (KBs) to optimize a function belonging to the Reproducing Kernel Hilbert Space (RKHS) . Mainstream…
Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
Alessandro Montenegro, Marco Mussi, Matteo Papini +1
Constrained Reinforcement Learning (CRL) tackles sequential decision-making problems where agents are required to achieve goals by maximizing the expected return while meeting doma…
Learning Optimal Deterministic Policies with Stochastic Policy Gradients
Alessandro Montenegro, Marco Mussi, Alberto Maria Metelli +1
Policy gradient (PG) methods are successful approaches to deal with continuous reinforcement learning (RL) problems. They learn stochastic parametric (hyper)policies by either expl…
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…
Autoregressive Bandits
Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran +4
Autoregressive processes naturally arise in a large variety of real-world scenarios, including stock markets, sales forecasting, weather prediction, advertising, and pricing. When…
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…
Dynamical Linear Bandits
Marco Mussi, Alberto Maria Metelli, Marcello Restelli
In many real-world sequential decision-making problems, an action does not immediately reflect on the feedback and spreads its effects over a long time frame. For instance, in onli…
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…