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The Impact of Batch Learning in Stochastic Bandits
Danil Provodin · Pratik Gajane · Mykola Pechenizkiy · Maurits Kaptein

We consider a special case of bandit problems, namely batched bandits. Motivated by natural restrictions of recommender systems and e-commerce platforms, we assume that a learning agent observes responses batched in groups over a certain time period. Unlike previous work, we consider a more practically relevant batch-centric scenario of batch learning. We provide a policy-agnostic regret analysis and demonstrate upper and lower bounds for the regret of a candidate policy. Our main theoretical results show that the impact of batch learning can be measured in terms of online behavior. Finally, we demonstrate the consistency of the theoretical results by conducting empirical experiments and reflect on the choice of the optimal batch size.

Author Information

Danil Provodin (TU Eindhoven)
Pratik Gajane (Eindhoven University of Technology)
Mykola Pechenizkiy (TU Eindhoven)
Maurits Kaptein (Tilburg University)

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