Autonomous underwater vehicle control using reinforcement learning policy search methods

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Show simple item record El-Fakdi Sencianes, Andrés Carreras Pérez, Marc Palomeras Rovira, Narcís Ridao Rodríguez, Pere 2005
dc.identifier.citation El-Fakdi, A., Carreras, M., Palomeras, N., i Ridao, P. (2005). Autonomous underwater vehicle control using reinforcement learning policy search methods. Oceans 2005 - Europe, 2, 793 - 798. Recuperat 06 maig 2010, a
dc.identifier.isbn 0-7803-9103-9
dc.description.abstract Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.relation.ispartof © Oceans 2005 - Europe, 2005, vol. 2, p. 793-798
dc.relation.ispartofseries Articles publicats (D-ATC)
dc.rights Tots els drets reservats
dc.subject Aprenentatge per reforç
dc.subject Robots autònoms -- Sistemes de control
dc.subject Vehicles submergibles -- Sistemes de control
dc.subject Autonomous robots -- Control systems
dc.subject Reinforcement learning
dc.subject Submersibles -- Control systems
dc.title Autonomous underwater vehicle control using reinforcement learning policy search methods
dc.type info:eu-repo/semantics/article

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