3 citations · 16 across the 47 of their papers we have counts for
48 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…
Sample complexity of variance-reduced policy gradient: weaker assumptions and lower bounds
Gabor Paczolay, Matteo Papini, Alberto Maria Metelli +2
Several variance-reduced versions of REINFORCE based on importance sampling achieve an improved sample complexity to find an -stationary point, under an unrealistic…
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…
Actor-Critic with Active Importance Sampling
Majid Molaei, Gabor Paczolay, Matteo Papini +2
This paper introduces the Active-Importance-Sampling Actor-Critic (AISAC) algorithm, an extension of the Actor-Critic framework for reducing variance in policy gradient estimation.…
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…
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…
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…
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:…
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…
Statistical Analysis of Policy Space Compression Problem
Majid Molaei, Marcello Restelli, Alberto Maria Metelli +1
Policy search methods are crucial in reinforcement learning, offering a framework to address continuous state-action and partially observable problems. However, the complexity of e…
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
Davide Maran, Alberto Maria Metelli, Matteo Papini +1
Achieving the no-regret property for Reinforcement Learning (RL) problems in continuous state and action-space environments is one of the major open problems in the field. Existing…
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach
Riccardo Poiani, Nicole Nobili, Alberto Maria Metelli +1
Policy evaluation via Monte Carlo (MC) simulation is at the core of many MC Reinforcement Learning (RL) algorithms (e.g., policy gradient methods). In this context, the designer of…
Efficient Learning of POMDPs with Known Observation Model in Average-Reward Setting
Alessio Russo, Alberto Maria Metelli, Marcello Restelli
Dealing with Partially Observable Markov Decision Processes is notably a challenging task. We face an average-reward infinite-horizon POMDP setting with an unknown transition model…
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…
Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis
Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance. Previous works have…
A Provably Efficient Option-Based Algorithm for both High-Level and Low-Level Learning
Gianluca Drappo, Alberto Maria Metelli, Marcello Restelli
Hierarchical Reinforcement Learning (HRL) approaches have shown successful results in solving a large variety of complex, structured, long-horizon problems. Nevertheless, a full th…
Optimal Multi-Fidelity Best-Arm Identification
Riccardo Poiani, Rémy Degenne, Emilie Kaufmann +2
In bandit best-arm identification, an algorithm is tasked with finding the arm with highest mean reward with a specified accuracy as fast as possible. We study multi-fidelity best-…
Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs
Davide Maran, Alberto Maria Metelli, Matteo Papini +1
We consider the problem of learning an -optimal policy in a general class of continuous-space Markov decision processes (MDPs) having smooth Bellman operators. Given a…
Policy Gradient with Active Importance Sampling
Matteo Papini, Giorgio Manganini, Alberto Maria Metelli +1
Importance sampling (IS) represents a fundamental technique for a large surge of off-policy reinforcement learning approaches. Policy gradient (PG) methods, in particular, signific…