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A Study of Layered Learning Strategies Applied to Individual Behaviors in Robot Soccer.
David
L. Leottau, Javier Ruiz-del-Solar, Patrick
MacAlpine, and Peter Stone.
In Luis Almeida, Jianmin Ji, Gerald Steinbauer,
and Sean Luke, editors, RoboCup-2015: Robot Soccer World Cup XIX, Lecture Notes in Artificial Intelligence, Springer
Verlag, Berlin, 2016.
Hierarchical task decomposition strategies allow robots and agents in general to address complex decision-making tasks. Layered learning is a hierarchical machine learning paradigm where a complex behavior is learned from a series of incrementally trained sub-tasks. This paper describes how layered learning can be applied to design individual behaviors in the context of soccer robotics. Three different layered learning strategies are implemented and analyzed using a ball-dribbling behavior as a case study. Performance indices for evaluating dribbling speed and ball-control are defined and measured. Experimental results validate the usefulness of the implemented layered learningstrategies showing a trade-off between performance and learning speed.
@incollection{LNAI15-Leottau,
author = {David L. Leottau and Javier Ruiz-del-Solar and Patrick MacAlpine and Peter Stone},
title = {A Study of Layered Learning Strategies Applied to Individual Behaviors in Robot Soccer},
booktitle = {{R}obo{C}up-2015: Robot Soccer World Cup {XIX}},
Editor={Luis Almeida and Jianmin Ji and Gerald Steinbauer and Sean Luke},
Publisher="Springer Verlag",
address="Berlin",
year="2016",
series="Lecture Notes in Artificial Intelligence",
abstract={
Hierarchical task decomposition strategies allow robots and agents in
general to address complex decision-making tasks. Layered learning is a
hierarchical machine learning paradigm where a complex behavior is learned from
a series of incrementally trained sub-tasks. This paper describes how layered
learning can be applied to design individual behaviors in the context of soccer
robotics. Three different layered learning strategies are implemented and
analyzed using a ball-dribbling behavior as a case study. Performance indices
for evaluating dribbling speed and ball-control are defined and measured.
Experimental results validate the usefulness of the implemented layered learning
strategies showing a trade-off between performance and learning speed.
},
}
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