3 citations · 16 across the 47 of their papers we have counts for
15 papers · 1 filter
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…
Information Capacity Regret Bounds for Bandits with Mediator Feedback
Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Maria Metelli +1
This work addresses the mediator feedback problem, a bandit game where the decision set consists of a number of policies, each associated with a probability distribution over a com…
Inverse Reinforcement Learning with Sub-optimal Experts
Riccardo Poiani, Gabriele Curti, Alberto Maria Metelli +1
Inverse Reinforcement Learning (IRL) techniques deal with the problem of deducing a reward function that explains the behavior of an expert agent who is assumed to act optimally in…