Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/62217
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Huang, J | en_US |
dc.creator | Li, X | en_US |
dc.creator | Wang, T | en_US |
dc.date.accessioned | 2016-12-19T08:59:06Z | - |
dc.date.available | 2016-12-19T08:59:06Z | - |
dc.identifier.issn | 0018-9286 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/62217 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Huang, J., Li, X., & Wang, T. (2015). Mean-field linear-quadratic-Gaussian (LQG) games for stochastic integral systems. IEEE Transactions on Automatic Control, 61(9), 2670-2675 is available at https://doi.org/10.1109/TAC.2015.2506620 | en_US |
dc.subject | Controlled stochastic delay system | en_US |
dc.subject | Fredholm equation | en_US |
dc.subject | Mean field LQG games | en_US |
dc.subject | Stochastic Volterra equation | en_US |
dc.subject | ϵ-Nash equilibrium | en_US |
dc.title | Mean-field Linear-Quadratic-Gaussian (LQG) games for stochastic integral systems | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 2670 | en_US |
dc.identifier.epage | 2675 | en_US |
dc.identifier.volume | 61 | en_US |
dc.identifier.issue | 9 | en_US |
dc.identifier.doi | 10.1109/TAC.2015.2506620 | en_US |
dcterms.abstract | In this technical note, we formulate and investigate a class of mean-field linear-quadratic-Gaussian (LQG) games for stochastic integral systems. Unlike other literature on mean-field games where the individual states follow the controlled stochastic differential equations (SDEs), the individual states in our large-population system are characterized by a class of stochastic Volterra-type integral equations. We obtain the Nash certainty equivalence (NCE) equation and hence derive the set of associated decentralized strategies. The ϵ-Nash equilibrium properties are also verified. Due to the intrinsic integral structure, the techniques and estimates applied here are significantly different from those existing results in mean-field LQG games for stochastic differential systems. For example, some Fredholm equation in the mean-field setup is introduced for the first time. As for applications, two types of stochastic delayed systems are formulated as the special cases of our stochastic integral system, and relevant mean-field LQG games are discussed. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on automatic control, Sept. 2016, v. 61, no. 9, p. 2670-2675 | en_US |
dcterms.isPartOf | IEEE transactions on automatic control | en_US |
dcterms.issued | 2016-09 | - |
dc.identifier.isi | WOS:000382686800039 | - |
dc.identifier.scopus | 2-s2.0-84984991435 | - |
dc.identifier.ros | 2016000214 | - |
dc.identifier.eissn | 1558-2523 | en_US |
dc.identifier.rosgroupid | 2016000213 | - |
dc.description.ros | 2016-2017 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.validate | 201804_a bcma | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AMA-0559 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | PolyU | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6673602 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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Huang_Mean-field_Linear-Quadratic-Gaussian_Games.pdf | Pre-Published version | 831.34 kB | Adobe PDF | View/Open |
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