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A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

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

IEEE

Author: Carreras Pérez, Marc
Yuh, Junku
Batlle i Grabulosa, Joan
Ridao Rodríguez, Pere
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
Document access: http://hdl.handle.net/2072/58627
Language: eng
Publisher: IEEE
Rights: Tots els drets reservats
Subject: 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
Title: A behavior-based scheme using reinforcement learning for autonomous underwater vehicles
Type: info:eu-repo/semantics/article
Repository: Recercat

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