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Population seeding techniques for Rolling Horizon Evolution in General Video Game Playing

Gaina, RD and Lucas, SM and Perez-Liebana, D (2017) Population seeding techniques for Rolling Horizon Evolution in General Video Game Playing. In: IEEE Congress on Evolutionary Computation (CEC), 2017, 2017-06-05 - 2017-06-08, San Sebastian, Spain.

1704.06942v1.pdf - Accepted Version

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While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative. However, there is little attention paid to population initialization techniques in the setting of general real-time video games. Therefore, this paper proposes the use of population seeding to improve the performance of Rolling Horizon Evolution and presents the results of two methods, One Step Look Ahead and Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus with multiple evolution parameter values (population size and individual length). An in-depth analysis is carried out between the results of the seeding methods and the vanilla Rolling Horizon Evolution. In addition, the paper presents a comparison to a Monte Carlo Tree Search algorithm. The results are promising, with seeding able to boost performance significantly over baseline evolution and even match the high level of play obtained by the Monte Carlo Tree Search.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Notes: Proceedings of the IEEE Conference on Evolutionary Computation 2017
Uncontrolled Keywords: cs.AI; cs.NE
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 08 Sep 2017 10:57
Last Modified: 15 Jan 2022 00:50

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