Timezone: »

 
Multi-Armed Bandit Problem with Temporally-Partitioned Rewards
Giulia Romano · Andrea Agostini · Francesco Trovò · Nicola Gatti · Marcello Restelli

@
in Women in Machine Learning »

There is a rising interest in industrial online applications where data becomes available sequentially. Inspired by the recommendation of playlists to users where their preferences can be collected during the listening of the entire playlist, we study a novel bandit setting, namely Multi-Armed Bandit with Temporally-Partitioned Rewards (TP-MAB), in which the stochastic reward associated with the pull of an arm is partitioned over a finite number of consecutive rounds following the pull. This setting, unexplored so far to the best of our knowledge, is a natural extension of delayed-feedback bandits to the case in which rewards may be dilated over a finite-time span after the pull instead of being fully disclosed in a single, potentially delayed round. We provide two algorithms to address TP-MAB problems, namely, TP-UCB-FR and TP-UCB-EW, which exploit the partial information disclosed by the reward collected over time. We show that our algorithms provide better asymptotical regret upper bounds than delayed-feedback bandit algorithms when a property characterizing a broad set of reward structures of practical interest, namely α-smoothness, holds. We also empirically evaluate their performance across a wide range of settings, both synthetically generated and from a real-world media recommendation problem.

[ Topia] 

Author Information

Giulia Romano (Politecnico di Milano)
Andrea Agostini (Politecnico di Milano)
Francesco Trovò (Politecnico di Milano)
Nicola Gatti (Politecnico di Milano)
Marcello Restelli (Politecnico di Milano)

More from the Same Authors