Scaling Tangled Program Graphs to Visual Reinforcement Learning in ViZDoom
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Smith:2018:EuroGP,
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author = "Robert J. Smith and Malcolm I. Heywood",
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title = "Scaling Tangled Program Graphs to Visual Reinforcement
Learning in {ViZDoom}",
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booktitle = "EuroGP 2018: Proceedings of the 21st European
Conference on Genetic Programming",
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year = "2018",
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month = "4-6 " # apr,
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editor = "Mauro Castelli and Lukas Sekanina and
Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
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series = "LNCS",
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volume = "10781",
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publisher = "Springer Verlag",
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address = "Parma, Italy",
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pages = "135--150",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-77552-4",
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DOI = "doi:10.1007/978-3-319-77553-1_9",
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abstract = "A tangled program graph framework (TPG) was recently
proposed as an emergent process for decomposing tasks
and simultaneously composing solutions by organizing
code into graphs of teams of programs. The initial
evaluation assessed the ability of TPG to discover
agents capable of playing Atari game titles under the
Arcade Learning Environment. This is an example of
visual reinforcement learning, i.e. agents are evolved
directly from the frame buffer without recourse to hand
designed features. TPG was able to evolve solutions
competitive with state-of-the-art deep reinforcement
learning solutions, but at a fraction of the
complexity. One simplification assumed was that the
visual input could be down sampled from a 210 x 160
resolution to 42 x 32. In this work, we consider the
challenging 3D first person shooter environment of
ViZDoom and require that agents be evolved at the
original visual resolution of 320 x 240 pixels. To do
so, we address issues with task scenarios performing
fitness evaluation over multiple tasks. The resulting
TPG solutions retain all the emergent properties of the
original work as well as the computational efficiency.
Moreover, solutions appear to generalize across
multiple task scenarios, whereas equivalent solutions
from deep reinforcement learning have focused on single
task scenarios alone.",
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notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
conjunction with EvoCOP2018, EvoMusArt2018 and
EvoApplications2018",
- }
Genetic Programming entries for
Robert J Smith
Malcolm Heywood
Citations