Alberto Maria Metelli | DeepAI

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∙ 08/29/2023

Pure Exploration under Mediators' Feedback

Stochastic multi-armed bandits are a sequential-decision-making framewor...

0 Riccardo Poiani, et al. ∙

∙ 06/19/2023

Nonlinear Feature Aggregation: Two Algorithms driven by Theory

Many real-world machine learning applications are characterized by a hug...

0 Paolo Bonetti, et al. ∙

∙ 05/10/2023

An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-Markov Decision Processes

A large variety of real-world Reinforcement Learning (RL) tasks is chara...

0 Gianluca Drappo, et al. ∙

∙ 05/07/2023

Truncating Trajectories in Monte Carlo Reinforcement Learning

In Reinforcement Learning (RL), an agent acts in an unknown environment ...

0 Riccardo Poiani, et al. ∙

∙ 04/25/2023

Towards Theoretical Understanding of Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) denotes a powerful family of algori...

0 Alberto Maria Metelli, et al. ∙

∙ 04/11/2023

A Tale of Sampling and Estimation in Discounted Reinforcement Learning

The most relevant problems in discounted reinforcement learning involve ...

0 Alberto Maria Metelli, et al. ∙

∙ 03/26/2023

Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis

One of the central issues of several machine learning applications on re...

0 Paolo Bonetti, et al. ∙

∙ 03/14/2023

Information-Theoretic Regret Bounds for Bandits with Fixed Expert Advice

We investigate the problem of bandits with expert advice when the expert...

0 Khaled Eldowa, et al. ∙

∙ 03/04/2023

Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control

Uncertainty quantification has been extensively used as a means to achie...

0 Amarildo Likmeta, et al. ∙

∙ 02/15/2023

Best Arm Identification for Stochastic Rising Bandits

Stochastic Rising Bandits is a setting in which the values of the expect...

0 Marco Mussi, et al. ∙

∙ 12/12/2022

Autoregressive Bandits

Autoregressive processes naturally arise in a large variety of real-worl...

0 Francesco Bacchiocchi, et al. ∙

∙ 12/07/2022

Tight Performance Guarantees of Imitator Policies with Continuous Actions

Behavioral Cloning (BC) aims at learning a policy that mimics the behavi...

0 Davide Maran, et al. ∙

∙ 12/07/2022

Stochastic Rising Bandits

This paper is in the field of stochastic Multi-Armed Bandits (MABs), i.e...

0 Alberto Maria Metelli, et al. ∙

∙ 11/21/2022

Simultaneously Updating All Persistence Values in Reinforcement Learning

In reinforcement learning, the performance of learning agents is highly ...

0 Luca Sabbioni, et al. ∙

∙ 11/16/2022

Dynamical Linear Bandits

In many real-world sequential decision-making problems, an action does n...

0 Marco Mussi, et al. ∙

∙ 07/25/2022

Optimizing Empty Container Repositioning and Fleet Deployment via Configurable Semi-POMDPs

With the continuous growth of the global economy and markets, resource i...

0 Riccardo Poiani, et al. ∙

∙ 07/08/2022

Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management

Warehouse Management Systems have been evolving and improving thanks to ...

0 Julen Cestero, et al. ∙

∙ 05/20/2022

ARLO: A Framework for Automated Reinforcement Learning

Automated Reinforcement Learning (AutoRL) is a relatively new area of re...

0 Marco Mussi, et al. ∙

∙ 12/13/2021

Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization

Learning in a lifelong setting, where the dynamics continually evolve, i...

0 Pierre Liotet, et al. ∙

∙ 12/15/2020

Policy Optimization as Online Learning with Mediator Feedback

Policy Optimization (PO) is a widely used approach to address continuous...

3 Alberto Maria Metelli, et al. ∙

∙ 02/17/2020

Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning

The choice of the control frequency of a system has a relevant impact on...

0 Alberto Maria Metelli, et al. ∙

∙ 09/09/2019

Gradient-Aware Model-based Policy Search

Traditional model-based reinforcement learning approaches learn a model ...

0 Pierluca D'Oro, et al. ∙

∙ 09/09/2019

Policy Space Identification in Configurable Environments

We study the problem of identifying the policy space of a learning agent...

0 Alberto Maria Metelli, et al. ∙

∙ 07/17/2019

Feature Selection via Mutual Information: New Theoretical Insights

Mutual information has been successfully adopted in filter feature-selec...

0 Mario Beraha, et al. ∙

∙ 09/17/2018

Policy Optimization via Importance Sampling

Policy optimization is an effective reinforcement learning approach to s...

0 Alberto Maria Metelli, et al. ∙

∙ 06/14/2018

Configurable Markov Decision Processes

In many real-world problems, there is the possibility to configure, to a...

0 Alberto Maria Metelli, et al. ∙

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