3-Dimensional convolutional neural networks for predicting StarCraft II results and extracting key game situations

Insung Baek, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In real-time strategy games, players collect resources, control various units, and create strategies to win. The creation of winning strategies requires accurately analyzing previous games; therefore, it is important to be able to identify the key situations that determined the outcomes of those games. However, previous studies have mainly focused on predicting game results. In this study, we propose a methodology to predict outcomes and to identify information about the turning points that determine outcomes in StarCraft II, one of the most popular real-time strategy games. We used replay data from StarCraft II that is similar to video data providing continuous multiple images. First, we trained a result prediction model using 3D-residual networks (3D-ResNet) and replay data to improve prediction performance by utilizing in-game spatiotemporal information. Second, we used gradient-weighted class activation mapping to extract information defining the key situations that significantly influenced the outcomes of the game. We then proved that the proposed method outperforms by comparing 2D-residual networks (2D-ResNet) using only one time-point information and 3D-ResNet with multiple time-point information. We verified the usefulness of our methodology on a 3D-ResNet with a gradient class activation map linked to a StarCraft II replay dataset.

Original languageEnglish
Article numbere0264550
JournalPloS one
Issue number3 March
Publication statusPublished - 2022 Mar

Bibliographical note

Publisher Copyright:
© 2022 Baek, Kim. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ASJC Scopus subject areas

  • General


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