Top-$k$ Combinatorial Bandits with Full-Bandit Feedback

Idan Rejwan, Yishay Mansour
Proceedings of the 31st International Conference on Algorithmic Learning Theory, PMLR 117:752-776, 2020.

Abstract

Top-$k$ Combinatorial Bandits generalize multi-armed bandits, where at each round any subset of $k$ out of $n$ arms may be chosen and the sum of the rewards is gained. We address the full-bandit feedback, in which the agent observes only the sum of rewards, in contrast to the semi-bandit feedback, in which the agent observes also the individual arms’ rewards. We present the Combinatorial Successive Accepts and Rejects (CSAR) algorithm, which generalizes SAR (Bubeck et al., 2013) for top-k combinatorial bandits. Our main contribution is an efficient sampling scheme that uses Hadamard matrices in order to estimate accurately the individual arms’ expected rewards. We discuss two variants of the algorithm, the first minimizes the sample complexity and the second minimizes the regret. We also prove a lower bound on sample complexity, which is tight for $k=O(1)$. Finally, we run experiments and show that our algorithm outperforms other methods.

Cite this Paper


BibTeX
@InProceedings{pmlr-v117-rejwan20a, title = {Top-$k$ Combinatorial Bandits with Full-Bandit Feedback}, author = {Rejwan, Idan and Mansour, Yishay}, booktitle = {Proceedings of the 31st International Conference on Algorithmic Learning Theory}, pages = {752--776}, year = {2020}, editor = {Kontorovich, Aryeh and Neu, Gergely}, volume = {117}, series = {Proceedings of Machine Learning Research}, month = {08 Feb--11 Feb}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v117/rejwan20a/rejwan20a.pdf}, url = {http://proceedings.mlr.press/v117/rejwan20a.html}, abstract = {Top-$k$ Combinatorial Bandits generalize multi-armed bandits, where at each round any subset of $k$ out of $n$ arms may be chosen and the sum of the rewards is gained. We address the full-bandit feedback, in which the agent observes only the sum of rewards, in contrast to the semi-bandit feedback, in which the agent observes also the individual arms’ rewards. We present the Combinatorial Successive Accepts and Rejects (CSAR) algorithm, which generalizes SAR (Bubeck et al., 2013) for top-k combinatorial bandits. Our main contribution is an efficient sampling scheme that uses Hadamard matrices in order to estimate accurately the individual arms’ expected rewards. We discuss two variants of the algorithm, the first minimizes the sample complexity and the second minimizes the regret. We also prove a lower bound on sample complexity, which is tight for $k=O(1)$. Finally, we run experiments and show that our algorithm outperforms other methods.} }
Endnote
%0 Conference Paper %T Top-$k$ Combinatorial Bandits with Full-Bandit Feedback %A Idan Rejwan %A Yishay Mansour %B Proceedings of the 31st International Conference on Algorithmic Learning Theory %C Proceedings of Machine Learning Research %D 2020 %E Aryeh Kontorovich %E Gergely Neu %F pmlr-v117-rejwan20a %I PMLR %P 752--776 %U http://proceedings.mlr.press/v117/rejwan20a.html %V 117 %X Top-$k$ Combinatorial Bandits generalize multi-armed bandits, where at each round any subset of $k$ out of $n$ arms may be chosen and the sum of the rewards is gained. We address the full-bandit feedback, in which the agent observes only the sum of rewards, in contrast to the semi-bandit feedback, in which the agent observes also the individual arms’ rewards. We present the Combinatorial Successive Accepts and Rejects (CSAR) algorithm, which generalizes SAR (Bubeck et al., 2013) for top-k combinatorial bandits. Our main contribution is an efficient sampling scheme that uses Hadamard matrices in order to estimate accurately the individual arms’ expected rewards. We discuss two variants of the algorithm, the first minimizes the sample complexity and the second minimizes the regret. We also prove a lower bound on sample complexity, which is tight for $k=O(1)$. Finally, we run experiments and show that our algorithm outperforms other methods.
APA
Rejwan, I. & Mansour, Y.. (2020). Top-$k$ Combinatorial Bandits with Full-Bandit Feedback. Proceedings of the 31st International Conference on Algorithmic Learning Theory, in Proceedings of Machine Learning Research 117:752-776 Available from http://proceedings.mlr.press/v117/rejwan20a.html.

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