Emergent Policy Discovery for Visual Reinforcement Learning Through Tangled Program Graphs: A Tutorial
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{kelly:2018:GPTP,
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author = "Stephen Kelly and Robert J. Smith and
Malcolm I. Heywood",
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title = "Emergent Policy Discovery for Visual Reinforcement
Learning Through Tangled Program Graphs: A Tutorial",
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booktitle = "Genetic Programming Theory and Practice XVI",
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year = "2018",
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editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman",
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pages = "37--57",
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address = "Ann Arbor, USA",
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month = "17-20 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-030-04734-4",
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URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_3",
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DOI = "doi:10.1007/978-3-030-04735-1_3",
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abstract = "Tangled Program Graphs (TPG) represents a framework by
which multiple programs can be organized to cooperate
and decompose a task with minimal a priori information.
TPG agents begin with least complexity and
incrementally coevolve to discover a complexity
befitting the nature of the task. Previous research has
demonstrated the TPG framework under visual
reinforcement learning tasks from the Arcade Learning
Environment and VizDoom first person shooter game that
are competitive with those from Deep Learning. However,
unlike Deep Learning the emergent constructive
properties of TPG results in solutions that are orders
of magnitude simpler, thus execution never needs
hardware support. In this work, our goal is to provide
a tutorial overview demonstrating how the emergent
properties of TPG have been achieved as well as
providing specific examples of decompositions
discovered under the VizDoom task.",
- }
Genetic Programming entries for
Stephen Kelly
Robert J Smith
Malcolm Heywood
Citations