Deep surrogate assisted MAP-elites for automated hearthstone deckbuilding
Document Type
Conference Proceeding
Publication Date
7-8-2022
Abstract
We study the problem of efficiently generating high-quality and diverse content in games. Previous work on automated deckbuilding in Hearthstone shows that the quality diversity algorithm MAP-Elites can generate a collection of high-performing decks with diverse strategic gameplay. However, MAP-Elites requires a large number of expensive evaluations to discover a diverse collection of decks. We propose assisting MAP-Elites with a deep surrogate model trained online to predict game outcomes with respect to candidate decks. MAP-Elites discovers a diverse dataset to improve the surrogate model accuracy while the surrogate model helps guide MAP-Elites towards promising new content. In a Hearthstone deck-building case study, we show that our approach improves the sample efficiency of MAP-Elites and outperforms a model trained offline with random decks, as well as a linear surrogate model baseline, setting a new state-of-the-art for quality diversity approaches in automated Hearthstone deckbuilding. We include the source code for all the experiments at: https://github.com/icaros-usc/EvoStone2.
Identifier
85135206557 (Scopus)
ISBN
[9781450392372]
Publication Title
Gecco 2022 Proceedings of the 2022 Genetic and Evolutionary Computation Conference
External Full Text Location
https://doi.org/10.1145/3512290.3528718
First Page
158
Last Page
167
Recommended Citation
Zhang, Yulun; Fontaine, Matthew C.; Hoover, Amy K.; and Nikolaidis, Stefanos, "Deep surrogate assisted MAP-elites for automated hearthstone deckbuilding" (2022). Faculty Publications. 2802.
https://digitalcommons.njit.edu/fac_pubs/2802
