5 papers · 1 filter
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…
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…
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…
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…
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…