Reinforcement Learning in Online Advertising: Challenges, Prospects, and Trust

Jingwen Cai, Johanna Björklund
Reliable and Trustworthy Artificial Intelligence 2025, PMLR 310:1-10, 2025.

Abstract

The central decision-making processes involved in online advertising are often supported by Reinforcement Learning (RL), which serves to optimise long-term accumulative re- wards through interactions with evolving environments. While RL’s potential in various real-world applications has been reviewed in extant survey works, the specific ways RL algorithms address online advertising challenges remain unchartered. Therefore, this paper reviews RL applications in this practice area, identifying core challenges and key issues including trust concerns. We categorize reviewed work based on problem domains and propose potential directions for future research. Our goal is to bridge the cross-disciplinary gap in this field, offering perspectives and guidance for researchers and practitioners.

Cite this Paper

BibTeX
@InProceedings{pmlr-v310-cai25a, title = {Reinforcement Learning in Online Advertising: Challenges, Prospects, and Trust}, author = {Cai, Jingwen and Bj{\"o}rklund, Johanna}, booktitle = {Reliable and Trustworthy Artificial Intelligence 2025}, pages = {1--10}, year = {2025}, editor = {Nguyen, Hoang D. and Le, Duc-Trong and Björklund, Johanna and Vu, Xuan-Son}, volume = {310}, series = {Proceedings of Machine Learning Research}, month = {12 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v310/main/assets/cai25a/cai25a.pdf}, url = {https://proceedings.mlr.press/v310/cai25a.html}, abstract = {The central decision-making processes involved in online advertising are often supported by Reinforcement Learning (RL), which serves to optimise long-term accumulative re- wards through interactions with evolving environments. While RL’s potential in various real-world applications has been reviewed in extant survey works, the specific ways RL algorithms address online advertising challenges remain unchartered. Therefore, this paper reviews RL applications in this practice area, identifying core challenges and key issues including trust concerns. We categorize reviewed work based on problem domains and propose potential directions for future research. Our goal is to bridge the cross-disciplinary gap in this field, offering perspectives and guidance for researchers and practitioners.} }
Endnote
%0 Conference Paper %T Reinforcement Learning in Online Advertising: Challenges, Prospects, and Trust %A Jingwen Cai %A Johanna Björklund %B Reliable and Trustworthy Artificial Intelligence 2025 %C Proceedings of Machine Learning Research %D 2025 %E Hoang D. Nguyen %E Duc-Trong Le %E Johanna Björklund %E Xuan-Son Vu %F pmlr-v310-cai25a %I PMLR %P 1--10 %U https://proceedings.mlr.press/v310/cai25a.html %V 310 %X The central decision-making processes involved in online advertising are often supported by Reinforcement Learning (RL), which serves to optimise long-term accumulative re- wards through interactions with evolving environments. While RL’s potential in various real-world applications has been reviewed in extant survey works, the specific ways RL algorithms address online advertising challenges remain unchartered. Therefore, this paper reviews RL applications in this practice area, identifying core challenges and key issues including trust concerns. We categorize reviewed work based on problem domains and propose potential directions for future research. Our goal is to bridge the cross-disciplinary gap in this field, offering perspectives and guidance for researchers and practitioners.
APA
Cai, J. & Björklund, J.. (2025). Reinforcement Learning in Online Advertising: Challenges, Prospects, and Trust. Reliable and Trustworthy Artificial Intelligence 2025, in Proceedings of Machine Learning Research 310:1-10 Available from https://proceedings.mlr.press/v310/cai25a.html.

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