LIGHT-WEIGHT REINFORCEMENT LEARNING WITH FUNCTION APPROXIMATION FOR REAL-LIFE CONTROL TASKS

Kary Främling

Abstract

Despite the impressive achievements of reinforcement learning (RL) in playing Backgammon already in the beginning of the 90’s, relatively few successful real-world applications of RL have been reported since then. This could be due to the tendency of RL research to focus on discrete Markov Decision Processes that make it difficult to handle tasks with continuous-valued features. Another reason could be a tendency to develop continuously more complex mathematical RL models that are difficult to implement and operate. Both of these issues are addressed in this paper by using the gradient-descent Sarsa() method together with a Normalised Radial Basis Function neural net. The experimental results on three typical benchmark control tasks show that these methods outperform most previously reported results on these tasks, while remaining computationally feasible to implement even as embedded software. Therefore the presented results can serve as a reference both regarding learning performance and computational applicability of RL for real-life applications.

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Paper Citation


in Harvard Style

Främling K. (2008). LIGHT-WEIGHT REINFORCEMENT LEARNING WITH FUNCTION APPROXIMATION FOR REAL-LIFE CONTROL TASKS . In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8111-30-2, pages 127-134. DOI: 10.5220/0001484001270134


in Bibtex Style

@conference{icinco08,
author={Kary Främling},
title={LIGHT-WEIGHT REINFORCEMENT LEARNING WITH FUNCTION APPROXIMATION FOR REAL-LIFE CONTROL TASKS},
booktitle={Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2008},
pages={127-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001484001270134},
isbn={978-989-8111-30-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - LIGHT-WEIGHT REINFORCEMENT LEARNING WITH FUNCTION APPROXIMATION FOR REAL-LIFE CONTROL TASKS
SN - 978-989-8111-30-2
AU - Främling K.
PY - 2008
SP - 127
EP - 134
DO - 10.5220/0001484001270134