The Application of Co-evolutionary Genetic Programming and TD(1) Reinforcement Learning in Large-Scale Strategy Game VCMI
Created by W.Langdon from
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- @InProceedings{conf/kesamsta/WilisowskiD15,
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title = "The Application of Co-evolutionary Genetic Programming
and {TD}(1) Reinforcement Learning in Large-Scale
Strategy Game {VCMI}",
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author = "Lukasz Wilisowski and Rafal Drezewski",
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booktitle = "9th KES International Conference on Agent and
Multi-Agent Systems: Technologies and Applications,
KES-AMSTA 2015",
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year = "2015",
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editor = "Gordan Jezic and Robert J. Howlett and
Lakhmi C. Jain",
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volume = "38",
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series = "Smart Innovation, Systems and Technologies",
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pages = "81--93",
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address = "Sorrento, Italy",
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month = jun # " 17-19",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, neural
networks, strategy games",
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isbn13 = "978-3-319-19728-9",
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bibdate = "2017-05-21",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/kesamsta/kesamsta2015.html#WilisowskiD15",
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DOI = "doi:10.1007/978-3-319-19728-9_7",
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abstract = "VCMI is a new, open-source project that could become
one of the biggest testing platform for modern AI
algorithms in the future. Its complex environment and
turn-based game play make it a perfect system for any
AI driven solution. It also has a large community of
active players which improves the testability of target
algorithms. This paper explores VCMI's environment and
tries to assess its complexity by providing a base
solution for battle handling problem using two global
optimisation algorithms: Co-Evolution of Genetic
Programming Trees and TD(1) algorithm with Back
Propagation neural network. Both algorithms have been
used in VCMI to evolve battle strategies through a
fully autonomous learning process. Finally, the
obtained strategies have been tested against existing
solutions and compared with players' best tactics.",
- }
Genetic Programming entries for
Lukasz Wilisowski
Rafal Drezewski
Citations