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