Accelerating Tangled Program Graph Evolution under Visual Reinforcement Learning Tasks with Mutation and Multi-actions
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Bayer:2021:GPTP,
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author = "Caleidgh Bayer and Ryan Amaral and Robert Smith and
Alexandru Ianta and Malcolm Heywood",
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title = "Accelerating Tangled Program Graph Evolution under
Visual Reinforcement Learning Tasks with Mutation and
Multi-actions",
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booktitle = "Genetic Programming Theory and Practice XVIII",
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year = "2021",
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editor = "Wolfgang Banzhaf and Leonardo Trujillo and
Stephan Winkler and Bill Worzel",
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series = "Genetic and Evolutionary Computation",
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pages = "1--19",
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address = "East Lansing, USA",
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month = "19-21 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-981-16-8112-7",
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DOI = "doi:10.1007/978-981-16-8113-4_1",
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abstract = "Tangled Program Graphs (TPG) represents a genetic
programming framework in which emergent modularity
incrementally composes programs into teams of programs
into graphs of teams of programs. To date, the
framework has been demonstrated on reinforcement
learning tasks with stochastic partially observable
state spaces or time series prediction. However,
evolving solutions to reinforcement tasks often
requires agents to demonstrate/ juggle multiple
properties simultaneously. Hence, we are interesting in
maintaining a population of diverse agents.
Specifically, agent performance on a reinforcement
learning task controls how much of the task they are
exposed to. Premature convergence might therefore
preclude solving aspects of a task that the agent only
later encounters. Moreover, pointless complexity may
also result in which graphs largely consist of
hitchhikers. In this research we benchmark the use of
rampant mutation (multiple mutations applied
simultaneously for offspring creation) and action
programs (multiple actions per state). Several
parameterizations are also introduced that potentially
penalize the introduction of hitchhikers. Benchmarking
over five VizDoom tasks demonstrates that rampant
mutation reduces the likelihood of encountering
pathologically bad offspring while action programs
appears to improve performance in four out of five
tasks. Finally, use of TPG parameterizations that
actively limit the complexity of solutions appears to
result in very efficient low dimensional solutions that
generalize best across all combinations of 3, 4 and 5
VizDoom tasks.",
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notes = "Part of \cite{Banzhaf:2021:GPTP} published after the
workshop in 2022",
- }
Genetic Programming entries for
Caleidgh Bayer
Ryan Amaral
Robert J Smith
Alexandru Ianta
Malcolm Heywood
Citations