A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
dc.contributor.author
dc.date.accessioned
2010-05-05T13:00:51Z
dc.date.available
2010-05-03T15:06:42Z
2010-05-05T13:00:51Z
dc.date.issued
2005
dc.identifier.citation
Carreras, M., Yuh, J., Batlle, J., i Ridao, P. (2005). A behavior-based scheme using reinforcement learning for autonomous underwater vehicles. IEEE Journal of Oceanic Engineering, 30, 2, 416-427. Recuperat 05 maig 2010, a http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1522520
dc.identifier.issn
0364-9059
dc.identifier.uri
dc.description.abstract
This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
IEEE
dc.relation.isformatof
Reproducció digital del document publicat a: http://dx.doi.org/10.1109/JOE.2004.835805
dc.relation.ispartof
© Oceanic Engineering, 2005, vol. 30, p. 416-427
dc.relation.ispartofseries
Articles publicats (D-ATC)
dc.rights
Tots els drets reservats
dc.subject
dc.title
A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.identifier.doi