Multi-task Learning in Atari Video Games with Emergent Tangled Program Graphs
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Kelly:2017:GECCO,
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author = "Stephen Kelly and Malcolm I. Heywood",
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title = "Multi-task Learning in {Atari} Video Games with
Emergent Tangled Program Graphs",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4920-8",
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address = "Berlin, Germany",
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pages = "195--202",
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size = "8 pages",
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URL = "http://doi.acm.org/10.1145/3071178.3071303",
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DOI = "doi:10.1145/3071178.3071303",
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acmid = "3071303",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, emergent
modularity, multi-task learning",
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month = "15-19 " # jul,
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abstract = "The Atari 2600 video game console provides an
environment for investigating the ability to build
artificial agent behaviours for a variety of games
using a common interface. Such a task has received
attention for addressing issues such as: 1) operation
directly from a high-dimensional game screen; and 2)
partial observability of state. However, a general
theme has been to assume a common machine learning
algorithm, but completely retrain the model for each
game title. Success in this respect implies that agent
behaviours can be identified without hand crafting game
specific attributes/actions. This work advances current
state-of-the-art by evolving solutions to play multiple
titles from the same run. We demonstrate that in
evolving solutions to multiple game titles, agent
behaviours for an individual game as well as single
agents capable of playing all games emerge from the
same evolutionary run. Moreover, the computational cost
is no more than that used for building solutions for a
single title. Finally, while generally matching the
skill level of controllers from neuro-evolution/deep
learning, the genetic programming solutions evolved
here are several orders of magnitude simpler, resulting
in real-time operation at a fraction of the cost.",
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notes = "Also known as \cite{Kelly:2017:MLA:3071178.3071303}
GECCO-2017 A Recombination of the 26th International
Conference on Genetic Algorithms (ICGA-2017) and the
22nd Annual Genetic Programming Conference (GP-2017)",
- }
Genetic Programming entries for
Stephen Kelly
Malcolm Heywood
Citations