The Application of Co-evolutionary Genetic Programming and TD(1) Reinforcement Learning in Large-Scale Strategy Game VCMI

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{conf/kesamsta/WilisowskiD15,

• title = "The Application of Co-evolutionary Genetic Programming and {TD}(1) Reinforcement Learning in Large-Scale Strategy Game {VCMI}",
• author = "Lukasz Wilisowski and Rafal Drezewski",
• booktitle = "9th KES International Conference on Agent and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2015",
• year = "2015",
• editor = "Gordan Jezic and Robert J. Howlett and Lakhmi C. Jain",
• volume = "38",
• series = "Smart Innovation, Systems and Technologies",
• pages = "81--93",
• address = "Sorrento, Italy",
• month = jun # " 17-19",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, neural networks, strategy games",
• isbn13 = "978-3-319-19728-9",
• bibdate = "2017-05-21",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/kesamsta/kesamsta2015.html#WilisowskiD15",
• DOI = "doi:10.1007/978-3-319-19728-9_7",
• 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