Optimal Control of Fluid Restless Multi-armed Bandits: A Machine Learning Approach.
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| Title: | Optimal Control of Fluid Restless Multi-armed Bandits: A Machine Learning Approach. |
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| Authors: | Bertsimas, Dimitris1 (AUTHOR) dbertsim@mit.edu, Kim, Cheol Woo2 (AUTHOR) acwkim@mit.edu, Niño-Mora, José3 (AUTHOR) jose.nino@uc3m.es |
| Source: | Machine Learning. Mar2026, Vol. 115 Issue 3, p1-32. 32p. |
| Abstract: | We present a novel machine learning framework for the optimal control of fluid restless multi-armed bandit problems (FRMABPs) with state equations that are either affine or quadratic in the state variables. By establishing fundamental properties of FRMABPs, we develop an efficient numerical algorithm that generates a comprehensive training set by solving multiple instances with diverse initial states. We further enhance this training set by applying a nonlinear transformation to the feature vectors, leveraging structural properties of FRMABPs. A time-dependent state feedback policy is then learned using Optimal Classification Trees with Hyperplane Splits (OCT-H). We test our approach on machine maintenance, epidemic control, and fisheries control problems, demonstrating that our method yields high-quality state feedback policies. Furthermore, once a policy is learned, it achieves a speed-up of up to 26 million times compared to the direct numerical algorithm. [ABSTRACT FROM AUTHOR] |
| Copyright of Machine Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192233904 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimal Control of Fluid Restless Multi-armed Bandits: A Machine Learning Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bertsimas%2C+Dimitris%22">Bertsimas, Dimitris</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dbertsim@mit.edu</i><br /><searchLink fieldCode="AR" term="%22Kim%2C+Cheol+Woo%22">Kim, Cheol Woo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> acwkim@mit.edu</i><br /><searchLink fieldCode="AR" term="%22Niño-Mora%2C+José%22">Niño-Mora, José</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> jose.nino@uc3m.es</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Machine+Learning%22">Machine Learning</searchLink>. Mar2026, Vol. 115 Issue 3, p1-32. 32p. – Name: Abstract Label: Abstract Group: Ab Data: We present a novel machine learning framework for the optimal control of fluid restless multi-armed bandit problems (FRMABPs) with state equations that are either affine or quadratic in the state variables. By establishing fundamental properties of FRMABPs, we develop an efficient numerical algorithm that generates a comprehensive training set by solving multiple instances with diverse initial states. We further enhance this training set by applying a nonlinear transformation to the feature vectors, leveraging structural properties of FRMABPs. A time-dependent state feedback policy is then learned using Optimal Classification Trees with Hyperplane Splits (OCT-H). We test our approach on machine maintenance, epidemic control, and fisheries control problems, demonstrating that our method yields high-quality state feedback policies. Furthermore, once a policy is learned, it achieves a speed-up of up to 26 million times compared to the direct numerical algorithm. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Machine Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=192233904 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10994-026-07022-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 1 Titles: – TitleFull: Optimal Control of Fluid Restless Multi-armed Bandits: A Machine Learning Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bertsimas, Dimitris – PersonEntity: Name: NameFull: Kim, Cheol Woo – PersonEntity: Name: NameFull: Niño-Mora, José IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08856125 Numbering: – Type: volume Value: 115 – Type: issue Value: 3 Titles: – TitleFull: Machine Learning Type: main |
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