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Authors: Andres El-Fakdi ; Marc Carreras and Pere Ridao

Affiliation: Institute of Informatics and Applications, University of Girona, Spain

ISBN: 972-8865-30-9

Keyword(s): Robot Learning, Autonomous robots.

Related Ontology Subjects/Areas/Topics: Informatics in Control, Automation and Robotics ; Mobile Robots and Autonomous Systems ; Robotics and Automation ; Space and Underwater Robotics

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 sub sea 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.

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Paper citation in several formats:
El-Fakdi A.; Carreras M.; Ridao P. and (2005). DIRECT GRADIENT-BASED REINFORCEMENT LEARNING FOR ROBOT BEHAVIOR LEARNING.In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 972-8865-30-9, pages 225-231. DOI: 10.5220/0001188902250231

@conference{icinco05,
author={Andres El{-}Fakdi and Marc Carreras and Pere Ridao},
title={DIRECT GRADIENT-BASED REINFORCEMENT LEARNING FOR ROBOT BEHAVIOR LEARNING},
booktitle={Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2005},
pages={225-231},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001188902250231},
isbn={972-8865-30-9},
}

TY - CONF

JO - Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - DIRECT GRADIENT-BASED REINFORCEMENT LEARNING FOR ROBOT BEHAVIOR LEARNING
SN - 972-8865-30-9
AU - El-Fakdi, A.
AU - Carreras, M.
AU - Ridao, P.
PY - 2005
SP - 225
EP - 231
DO - 10.5220/0001188902250231

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