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Planning and Learning in Risk-Aware Restless Multi-Arm Bandit

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16 oct. 2024   11h00 — 12h00

Nima Akbarzadeh HEC Montréal, Canada

Nima Akbarzadeh

Séminaire en format hybride au GERAD local 4488 ou Zoom.

In restless multi-arm bandits, a central agent is tasked with optimally distributing limited resources across several bandits (arms), with each arm being a Markov decision process. In this work, we generalize the traditional restless multi-arm bandit problem with a risk-neutral objective by incorporating risk-awareness. We establish indexability conditions for the case of a risk-aware objective. In addition, we address the learning problem when the true transition probabilities are unknown by proposing a Thompson sampling approach and show that it achieves bounded regret that scales sublinearly with the number of episodes and quadratically with the number of arms. The efficacy of our method in reducing risk exposure in restless multi-arm bandits is illustrated through a set of numerical experiments.

Olivier Bahn responsable
Erick Delage responsable

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Activité hybride au GERAD
Zoom et salle 4488
Pavillon André-Aisenstadt
Campus de l'Université de Montréal
2920, chemin de la Tour

Montréal Québec H3T 1J4
Canada

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