Quantized-data resource allocation for reinforcement learning cooperative control of networked Euler-Lagrange agents with input saturation.
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| Title: | Quantized-data resource allocation for reinforcement learning cooperative control of networked Euler-Lagrange agents with input saturation. |
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| Authors: | Ding, Teng-Fei1 (AUTHOR), Song, Li-Ping1 (AUTHOR), Ge, Ming-Feng1 (AUTHOR) gemf@cug.edu.cn |
| Source: | Journal of the Franklin Institute. Nov2023, Vol. 360 Issue 16, p12076-12100. 25p. |
| Subjects: | Resource allocation, Group work in education, State feedback (Feedback control systems), Lyapunov stability |
| Abstract: | This paper investigates the resource allocation problem of networked Euler-Lagrange agents (NELAs) with quantized-data interactions and input saturation in the framework of reinforcement learning (RL). We propose a hierarchical control strategy that includes a distributed resource allocation estimator (DRAE) and a local RL linear sliding mode controller (RL-LSMC). Specifically, the DRAE based on the gradient descent and state feedback is proposed, which aims to achieve optimal resource allocation by estimated states. The local RL-LSMC is designed through utilizing the feedback of critic neural network and the approximation capacity of actor neural network, which prompts the states of the NELAs to track the optimal estimated states. Several sufficient conditions are established with the help of Lyapunov stability argument. Finally, the effectiveness of the proposed hierarchical control algorithm is verified by the two simulation examples. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the Franklin Institute is the property of Pergamon Press - An Imprint of Elsevier Science 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: 173031824 AccessLevel: 6 PubType: Periodical PubTypeId: serialPeriodical PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Quantized-data resource allocation for reinforcement learning cooperative control of networked Euler-Lagrange agents with input saturation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ding%2C+Teng-Fei%22">Ding, Teng-Fei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Li-Ping%22">Song, Li-Ping</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ge%2C+Ming-Feng%22">Ge, Ming-Feng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gemf@cug.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Franklin+Institute%22">Journal of the Franklin Institute</searchLink>. Nov2023, Vol. 360 Issue 16, p12076-12100. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Group+work+in+education%22">Group work in education</searchLink><br /><searchLink fieldCode="DE" term="%22State+feedback+%28Feedback+control+systems%29%22">State feedback (Feedback control systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Lyapunov+stability%22">Lyapunov stability</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper investigates the resource allocation problem of networked Euler-Lagrange agents (NELAs) with quantized-data interactions and input saturation in the framework of reinforcement learning (RL). We propose a hierarchical control strategy that includes a distributed resource allocation estimator (DRAE) and a local RL linear sliding mode controller (RL-LSMC). Specifically, the DRAE based on the gradient descent and state feedback is proposed, which aims to achieve optimal resource allocation by estimated states. The local RL-LSMC is designed through utilizing the feedback of critic neural network and the approximation capacity of actor neural network, which prompts the states of the NELAs to track the optimal estimated states. Several sufficient conditions are established with the help of Lyapunov stability argument. Finally, the effectiveness of the proposed hierarchical control algorithm is verified by the two simulation examples. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of the Franklin Institute is the property of Pergamon Press - An Imprint of Elsevier Science 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jfranklin.2023.09.012 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 12076 Subjects: – SubjectFull: Resource allocation Type: general – SubjectFull: Group work in education Type: general – SubjectFull: State feedback (Feedback control systems) Type: general – SubjectFull: Lyapunov stability Type: general Titles: – TitleFull: Quantized-data resource allocation for reinforcement learning cooperative control of networked Euler-Lagrange agents with input saturation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ding, Teng-Fei – PersonEntity: Name: NameFull: Song, Li-Ping – PersonEntity: Name: NameFull: Ge, Ming-Feng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00160032 Numbering: – Type: volume Value: 360 – Type: issue Value: 16 Titles: – TitleFull: Journal of the Franklin Institute Type: main |
| ResultId | 1 |
