On-line reinforcement learning for trajectory following with unknown faults

dc.contributor.authorSohége, Yves
dc.contributor.authorProvan, Gregory
dc.date.accessioned2019-01-28T12:55:15Z
dc.date.available2019-01-28T12:55:15Z
dc.date.issued2018-12
dc.date.updated2019-01-28T12:35:55Z
dc.description.abstractReinforcement learning (RL) is a key method for providing robots with appropriate control algorithms. Controller blending is a technique for combining the control output of several controllers. In this article we use on-line RL to learn an optimal blending of controllers for novel faults. Since one cannot anticipate all possible fault states, which are exponential in the number of possible faults, we instead apply learning on the effects the faults have on the system. We use a quadcopter pathfollowing simulation in the presence of unknown rotor actuator faults for which the system has not been tuned. We empirically demonstrate the effectiveness of our novel on-line learning framework on a quadcopter trajectory following task with unknown faults, even after a small number of learning cycles. The authors are not aware of any other use of on-line RL for fault tolerant control under unknown faults.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationSohége, Y. and Provan, G. (2018) 'On-line reinforcement learning for trajectory following with unknown faults', Proceedings of the 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2018), Dublin, Ireland, 6-7 December, pp. 1-12. Available at: http://ceur-ws.org/Vol-2259/aics_27.pdf (Accessed: 28 January 2019)en
dc.identifier.endpage12en
dc.identifier.issn1613-0073
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/7366
dc.language.isoenen
dc.publisherCEUR-WS.orgen
dc.relation.ispartof26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2018)
dc.relation.urihttp://ceur-ws.org/Vol-2259/aics_27.pdf
dc.relation.urihttp://ceur-ws.org/Vol-2259/
dc.rights© 2018, the Authors. Copying permitted for private and academic purposes.en
dc.subjectReinforcement learningen
dc.subjectFault-tolerant controlen
dc.subjectQuadcopter controlen
dc.titleOn-line reinforcement learning for trajectory following with unknown faultsen
dc.typeConference itemen
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