Finding the bandit in a graph: Sequential search-and-stop - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Finding the bandit in a graph: Sequential search-and-stop

Résumé

We consider the problem where an agent wants to find a hidden object that is randomly located in some vertex of a directed acyclic graph (DAG) according to a fixed but possibly unknown distribution. The agent can only examine vertices whose in-neighbors have already been examined. In this paper, we address a learning setting where we allow the agent to stop before having found the object and restart searching on a new independent instance of the same problem. Our goal is to maximize the total number of hidden objects found given a time budget. The agent can thus skip an instance after realizing that it would spend too much time on it. Our contributions are both to the search theory and multi-armed bandits. If the distribution is known, we provide a quasi-optimal and efficient stationary strategy. If the distribution is unknown, we additionally show how to sequentially approximate it and, at the same time, act near-optimally in order to collect as many hidden objects as possible.
Fichier principal
Vignette du fichier
perrault2019finding.pdf (680.06 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02387465 , version 1 (29-11-2019)

Identifiants

  • HAL Id : hal-02387465 , version 1

Citer

Pierre Perrault, Vianney Perchet, Michal Valko. Finding the bandit in a graph: Sequential search-and-stop. 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), Apr 2019, Okinawa, Japan. ⟨hal-02387465⟩
78 Consultations
136 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More