Self-supervised Contrastive Learning for Predicting Game Strategies

Young Jae Lee, Insung Baek, Uk Jo, Jaehoon Kim, Jinsoo Bae, Keewon Jeong, Seoung Bum Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Many games enjoyed by players primarily consist of a matching system that allows the player to cooperate or compete with other players with similar scores. However, the method of matching only the play score can easily lose interest because it does not consider the opponent’s playstyle or strategy. In this study, we propose a self-supervised contrastive learning framework that can enhance the understanding of game replay data to create a more sophisticated matching system. We use actor-critic-based reinforcement learning agents to collect many replay data. We define a positive pair and negative examples to perform contrastive learning. Positive pair is defined by sampling from the frames of the same replay data, otherwise negatives. To evaluate the performance of the proposed framework, we use Facebook ELF, a real-time strategy game, to collect replay data and extract data features from pre-trained neural networks. Furthermore, we apply k-means clustering with the extracted features to visually demonstrate that different play patterns and proficiencies can be clustered appropriately. We present our clustering results on replay data and show that the proposed framework understands the nature of the data with consecutive frames.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2022 Intelligent Systems Conference IntelliSys Volume 1
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031160714
Publication statusPublished - 2023
EventIntelligent Systems Conference, IntelliSys 2022 - Virtual, Online
Duration: 2022 Sep 12022 Sep 2

Publication series

NameLecture Notes in Networks and Systems
Volume542 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceIntelligent Systems Conference, IntelliSys 2022
CityVirtual, Online


  • Game matching system
  • Reinforcement learning
  • Self-supervised contrastive learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


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