3 citations · 16 across the 47 of their papers we have counts for
8 papers · 1 filter
Tight Performance Guarantees of Imitator Policies with Continuous Actions
Davide Maran, Alberto Maria Metelli, Marcello Restelli
Behavioral Cloning (BC) aims at learning a policy that mimics the behavior demonstrated by an expert. The current theoretical understanding of BC is limited to the case of finite a…
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…
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…
Simultaneously Updating All Persistence Values in Reinforcement Learning
Luca Sabbioni, Luca Al Daire, Lorenzo Bisi +2
In reinforcement learning, the performance of learning agents is highly sensitive to the choice of time discretization. Agents acting at high frequencies have the best control oppo…
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…
Optimizing Empty Container Repositioning and Fleet Deployment via Configurable Semi-POMDPs
Riccardo Poiani, Ciprian Stirbu, Alberto Maria Metelli +1
With the continuous growth of the global economy and markets, resource imbalance has risen to be one of the central issues in real logistic scenarios. In marine transportation, thi…
Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management
Julen Cestero, Marco Quartulli, Alberto Maria Metelli +1
Warehouse Management Systems have been evolving and improving thanks to new Data Intelligence techniques. However, many current optimizations have been applied to specific cases or…
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…