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A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
Carreras Pérez, Marc; Yuh, Junku; Batlle i Grabulosa, Joan; Ridao Rodríguez, Pere
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
17-05-2010
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
Tots els drets reservats
Artículo
IEEE
         

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