Value Directed Exploration in Multi-Armed Bandits with Structured Priors

Bence Cserna, Marek Petrik, Reazul Hasan Russel, Wheeler Ruml
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:838-847, 2017.

Abstract

Multi-armed bandits are a quintessential machine learning problem requiring balancing exploration with exploitation. While there has been progress in developing algorithms with strong theoretical guarantees, there has been less focus on practical near-optimal finite-time performance. In this paper, we propose an algorithm for Bayesian multi-armed bandits that utilizes approximate value functions. Building on previous work on UCB and Gittins index, we introduce linearly-separable value functions that capture the benefit of exploration when choosing the next arm to pull. Our algorithm enjoys a sub-linear performance guarantee and our simulation results confirm its strength in problems with structured priors. The simplicity and generality of our approach makes it a strong candidate for use in more complex multi-armed bandit problems.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-cserna17a, title = {Value Directed Exploration in Multi-Armed Bandits with Structured Priors}, author = {Cserna, Bence and Petrik, Marek and Russel, Reazul Hasan and Ruml, Wheeler}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {838--847}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/cserna17a/cserna17a.pdf}, url = {https://proceedings.mlr.press/r15/cserna17a.html}, abstract = {Multi-armed bandits are a quintessential machine learning problem requiring balancing exploration with exploitation. While there has been progress in developing algorithms with strong theoretical guarantees, there has been less focus on practical near-optimal finite-time performance. In this paper, we propose an algorithm for Bayesian multi-armed bandits that utilizes approximate value functions. Building on previous work on UCB and Gittins index, we introduce linearly-separable value functions that capture the benefit of exploration when choosing the next arm to pull. Our algorithm enjoys a sub-linear performance guarantee and our simulation results confirm its strength in problems with structured priors. The simplicity and generality of our approach makes it a strong candidate for use in more complex multi-armed bandit problems.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Value Directed Exploration in Multi-Armed Bandits with Structured Priors %A Bence Cserna %A Marek Petrik %A Reazul Hasan Russel %A Wheeler Ruml %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-cserna17a %I PMLR %P 838--847 %U https://proceedings.mlr.press/r15/cserna17a.html %V R15 %X Multi-armed bandits are a quintessential machine learning problem requiring balancing exploration with exploitation. While there has been progress in developing algorithms with strong theoretical guarantees, there has been less focus on practical near-optimal finite-time performance. In this paper, we propose an algorithm for Bayesian multi-armed bandits that utilizes approximate value functions. Building on previous work on UCB and Gittins index, we introduce linearly-separable value functions that capture the benefit of exploration when choosing the next arm to pull. Our algorithm enjoys a sub-linear performance guarantee and our simulation results confirm its strength in problems with structured priors. The simplicity and generality of our approach makes it a strong candidate for use in more complex multi-armed bandit problems. %Z Reissued by PMLR on 04 October 2026.
APA
Cserna, B., Petrik, M., Russel, R.H. & Ruml, W.. (2017). Value Directed Exploration in Multi-Armed Bandits with Structured Priors. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:838-847 Available from https://proceedings.mlr.press/r15/cserna17a.html. Reissued by PMLR on 04 October 2026.

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