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Conference Paper: Fast-maneuvering target seeking based on double-action Q-learning

TitleFast-maneuvering target seeking based on double-action Q-learning
Authors
KeywordsMoving object navigation
Q-learning
Reinforcement learning
Issue Date2007
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2007, v. 4571 LNAI, p. 653-666 How to Cite?
AbstractIn this paper, a reinforcement learning method called DAQL is proposed to solve the problem of seeking and homing onto a fast maneuvering target, within the context of mobile robots. This Q-learning based method considers both target and obstacle actions when determining its own action decisions, which enables the agent to learn more effectively in a dynamically changing environment. It particularly suits fast-maneuvering target cases, in which maneuvers of the target are unknown a priori. Simulation result depicts that the proposed method is able to choose a less convoluted path to reach the target when compared to the ideal proportional navigation (IPN) method in handling fast maneuvering and randomly moving target. Furthermore, it can learn to adapt to the physical limitation of the system and do not require specific initial conditions to be satisfied for successful navigation towards the moving target. © Springer-Verlag Berlin Heidelberg 2007.
Persistent Identifierhttp://hdl.handle.net/10722/99416
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorNgai, DCKen_HK
dc.contributor.authorYung, NHCen_HK
dc.date.accessioned2010-09-25T18:29:12Z-
dc.date.available2010-09-25T18:29:12Z-
dc.date.issued2007en_HK
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2007, v. 4571 LNAI, p. 653-666en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/99416-
dc.description.abstractIn this paper, a reinforcement learning method called DAQL is proposed to solve the problem of seeking and homing onto a fast maneuvering target, within the context of mobile robots. This Q-learning based method considers both target and obstacle actions when determining its own action decisions, which enables the agent to learn more effectively in a dynamically changing environment. It particularly suits fast-maneuvering target cases, in which maneuvers of the target are unknown a priori. Simulation result depicts that the proposed method is able to choose a less convoluted path to reach the target when compared to the ideal proportional navigation (IPN) method in handling fast maneuvering and randomly moving target. Furthermore, it can learn to adapt to the physical limitation of the system and do not require specific initial conditions to be satisfied for successful navigation towards the moving target. © Springer-Verlag Berlin Heidelberg 2007.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.subjectMoving object navigationen_HK
dc.subjectQ-learningen_HK
dc.subjectReinforcement learningen_HK
dc.titleFast-maneuvering target seeking based on double-action Q-learningen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYung, NHC:nyung@eee.hku.hken_HK
dc.identifier.authorityYung, NHC=rp00226en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-37249037454en_HK
dc.identifier.hkuros134881en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-37249037454&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4571 LNAIen_HK
dc.identifier.spage653en_HK
dc.identifier.epage666en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridNgai, DCK=9332358900en_HK
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK

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