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Patrick MacAlpine and Peter Stone. Overlapping Layered Learning. Artificial Intelligence, 254:21–43, Elsevier, January 2018.
Official version from Publisher's Webpage
Accompanying videos at http://www.cs.utexas.edu/ AustinVilla/sim/3dsimulation/overlappingLayeredLearning.html
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Layered learning is a hierarchical machine learning paradigm thatenables learning of complex behaviors by incrementally learning a series of sub-behaviors. A key feature of layered learning is that higher layers directly depend on the learned lower layers. In its original formulation, lower layers were frozen prior to learning higher layers. This article considers a major extension to the paradigm that allows learning certain behaviors independently, and then later stitching them together by learning at the "seams" where their influences overlap. The UT Austin Villa 2014 RoboCup 3D simulation team, using such overlapping layered learning, learned a total of 19 layered behaviors for a simulated soccer-playing robot, organized both in series and in parallel. To the best of our knowledge this is more than three times the number of layered behaviors in any prior layered learning system. Furthermore, the complete learning process is repeated on four additional robot body types, showcasing its generality as a paradigm for efficient behavior learning. The resulting team won the RoboCup 2014 championship with an undefeated record, scoring 52 goals and conceding none. This article includes a detailed experimental analysis of the team's performance and the overlapping layered learning approach that led to its success.
@article{AIJ18-MacAlpine,
title = {Overlapping Layered Learning},
journal = {Artificial Intelligence},
volume = {254},
pages = {21--43},
month = {January},
year = {2018},
issn = {0004-3702},
doi = {https://doi.org/10.1016/j.artint.2017.09.001},
url = {https://www.sciencedirect.com/science/article/pii/S0004370217301066},
author = {Patrick MacAlpine and Peter Stone},
publisher = {Elsevier},
abstract = {Layered learning is a hierarchical machine learning paradigm that
enables learning of complex behaviors by incrementally learning a series of
sub-behaviors. A key feature of layered learning is that higher layers directly
depend on the learned lower layers. In its original formulation, lower layers
were frozen prior to learning higher layers. This article considers a major
extension to the paradigm that allows learning certain behaviors independently,
and then later stitching them together by learning at the "seams" where their
influences overlap. The UT Austin Villa 2014 RoboCup 3D simulation team, using
such overlapping layered learning, learned a total of 19 layered behaviors for
a simulated soccer-playing robot, organized both in series and in parallel. To
the best of our knowledge this is more than three times the number of layered
behaviors in any prior layered learning system. Furthermore, the complete
learning process is repeated on four additional robot body types, showcasing
its generality as a paradigm for efficient behavior learning. The resulting
team won the RoboCup 2014 championship with an undefeated record, scoring 52
goals and conceding none. This article includes a detailed experimental
analysis of the team's performance and the overlapping layered learning
approach that led to its success.},
wwwnote = {Official version from <a href="https://authors.elsevier.com/a/1Vtn--c5F-HA">Publisher's Webpage</a><br>
Accompanying videos at <a href="http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/overlappingLayeredLearning.html">http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/overlappingLayeredLearning.html</a>}
}
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