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Título:
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A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
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Autor/a:
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Carreras Pérez, Marc; Yuh, Junku; Batlle i Grabulosa, Joan; Ridao Rodríguez, Pere
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Abstract:
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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 |
Fecha de creación:
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17-05-2010 |
Materia(s):
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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 |
Derechos:
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Tots els drets reservats |
Tipo de documento:
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Artículo |
Editor:
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IEEE
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Compartir:
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