Item
Carreras Pérez, Marc
Yuh, Junku Batlle i Grabulosa, Joan Ridao RodrÃguez, Pere |
|
2005 | |
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 | |
application/pdf | |
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 | |
0364-9059 | |
http://hdl.handle.net/10256/2169 | |
eng | |
IEEE | |
Reproducció digital del document publicat a: http://dx.doi.org/10.1109/JOE.2004.835805 Articles publicats (D-ATC) |
|
© Oceanic Engineering, 2005, vol. 30, p. 416-427 | |
Tots els drets reservats | |
Algorismes computacionals
Aprenentatge per reforç Intel·ligència artificial Robots autònoms Xarxes neuronals (Informà tica) Vehicles submergibles Artificial intelligence Autonomous robots Computer algorithms Neural networks (Computer science) Reinforcement learning Submersibles |
|
A behavior-based scheme using reinforcement learning for autonomous underwater vehicles | |
info:eu-repo/semantics/article | |
DUGiDocs |