Emergent Tangled Graph Representations for Atari Game Playing Agents
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Kelly:2017:EuroGP,
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author = "Stephen Kelly and Malcolm I. Heywood",
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title = "Emergent Tangled Graph Representations for Atari Game
Playing Agents",
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booktitle = "EuroGP 2017: Proceedings of the 20th European
Conference on Genetic Programming",
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year = "2017",
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month = "19-21 " # apr,
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editor = "Mauro Castelli and James McDermott and
Lukas Sekanina",
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series = "LNCS",
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volume = "10196",
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publisher = "Springer Verlag",
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address = "Amsterdam",
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pages = "64--79",
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organisation = "species",
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note = "best paper",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-55695-6",
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DOI = "doi:10.1007/978-3-319-55696-3_5",
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abstract = "Organizing code into coherent programs and relating
different programs to each other represents an
underlying requirement for scaling genetic programming
to more difficult task domains. Assuming a model in
which policies are defined by teams of programs, in
which team and program are represented using
independent populations and coevolved, has previously
been shown to support the development of variable sized
teams. In this work, we generalize the approach to
provide a complete framework for organizing multiple
teams into arbitrarily deep/wide structures through a
process of continuous evolution; hereafter the Tangled
Program Graph (TPG). Benchmarking is conducted using a
subset of 20 games from the Arcade Learning Environment
(ALE), an Atari 2600 video game emulator. The games
considered here correspond to those in which deep
learning was unable to reach a threshold of play
consistent with that of a human. Information provided
to the learning agent is limited to that which a human
would experience. That is, screen capture sensory
input, Atari joystick actions, and game score. The
performance of the proposed approach exceeds that of
deep learning in 15 of the 20 games, with 7 of the 15
also exceeding that associated with a human level of
competence. Moreover, in contrast to solutions from
deep learning, solutions discovered by TPG are also
very `sparse'. Rather than assuming that all of the
state space contributes to every decision, each action
in TPG is resolved following execution of a subset of
an individual's graph. This results in significantly
lower computational requirements for model building
than presently the case for deep learning.",
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notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
inconjunction with EvoCOP2017, EvoMusArt2017 and
EvoApplications2017",
- }
Genetic Programming entries for
Stephen Kelly
Malcolm Heywood
Citations