LEARNING BY EXAMPLE - Reinforcement Learning Techniques for Real Autonomous Underwater Cable Tracking

Andres El-Fakdi, Marc Carreras, Javier Antich, Alberto Ortiz

2008

Abstract

This paper proposes a field application of a high-level Reinforcement Learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a Direct Policy Search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICT INEU AUV .

Paper Citation

in Harvard Style

El-Fakdi A., Carreras M., Antich J. and Ortiz A. (2008). LEARNING BY EXAMPLE - Reinforcement Learning Techniques for Real Autonomous Underwater Cable Tracking . In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8111-31-9, pages 61-68. DOI: 10.5220/0001490500610068

in Bibtex Style

@conference{icinco08,
author={Andres El-Fakdi and Marc Carreras and Javier Antich and Alberto Ortiz},
title={LEARNING BY EXAMPLE - Reinforcement Learning Techniques for Real Autonomous Underwater Cable Tracking},
booktitle={Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2008},
pages={61-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001490500610068},
isbn={978-989-8111-31-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - LEARNING BY EXAMPLE - Reinforcement Learning Techniques for Real Autonomous Underwater Cable Tracking
SN - 978-989-8111-31-9
AU - El-Fakdi A.
AU - Carreras M.
AU - Antich J.
AU - Ortiz A.
PY - 2008
SP - 61
EP - 68
DO - 10.5220/0001490500610068