Naturally Interpretable Control Policies via Graph-Based Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Nadizar:2024:EuroGP,
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author = "Giorgia Nadizar and Eric Medvet and Dennis G. Wilson",
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editor = "Mario Giacobini and Bing Xue and Luca Manzoni",
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title = "Naturally Interpretable Control Policies via
Graph-Based Genetic Programming",
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booktitle = "EuroGP 2024: Proceedings of the 27th European
Conference on Genetic Programming",
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year = "2024",
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volume = "14631",
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series = "LNCS",
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publisher = "Springer",
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address = "Aberystwyth",
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month = "3-5 " # apr,
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organisation = "EvoStar, Species",
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note = "Best paper",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
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pages = "73--89",
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abstract = "In most high-risk applications, interpretability is
crucial for ensuring system safety and trust. However,
existing research often relies on hard-to-understand,
highly parameterized models, such as neural networks.
In this paper, we focus on the problem of policy search
in continuous observations and actions spaces. We
leverage two graph-based Genetic Programming (GP)
techniques, Cartesian Genetic Programming (CGP) and
Linear Genetic Programming (LGP), to develop effective
yet interpretable control policies. Our experimental
evaluation on eight continuous robotic control
benchmarks shows competitive results compared to
state-of-the-art Reinforcement Learning (RL)
algorithms. Moreover, we find that graph-based GP tends
towards small, interpretable graphs even when
competitive with RL. By examining these graphs, we are
able to explain the discovered policies, paving the way
for trustworthy AI in the domain of continuous
control.",
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isbn13 = "978-3-031-56957-9",
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DOI = "doi:10.1007/978-3-031-56957-9_5",
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notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in
conjunction with EvoCOP2024, EvoMusArt2024 and
EvoApplications2024",
- }
Genetic Programming entries for
Giorgia Nadizar
Eric Medvet
Dennis G Wilson
Citations