Learning to Move Like Professional Counter‐Strike Players

In multiplayer, first‐person shooter games like Counter‐Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high‐level strategic play. However, the complexity of team coordination and the variety of conditions present in popular game maps make it impractical to author h...

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Bibliographic Details
Published in:Computer graphics forum Vol. 43; no. 8
Main Authors: Durst, D., Xie, F., Sarukkai, V., Shacklett, B., Frosio, I., Tessler, C., Kim, J., Taylor, C., Bernstein, G., Choudhury, S., Hanrahan, P., Fatahalian, K.
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.12.2024
Subjects:
Controllers
Critical components
Datasets
Games
Human motion
Players
Prediction models
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:In multiplayer, first‐person shooter games like Counter‐Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high‐level strategic play. However, the complexity of team coordination and the variety of conditions present in popular game maps make it impractical to author hand‐crafted movement policies for every scenario. We show that it is possible to take a data‐driven approach to creating human‐like movement controllers for CS:GO. We curate a team movement dataset comprising 123 hours of professional game play traces, and use this dataset to train a transformer‐based movement model that generates human‐like team movement for all players in a “Retakes” round of the game. Importantly, the movement prediction model is efficient. Performing inference for all players takes less than 0.5 ms per game step (amortized cost) on a single CPU core, making it plausible for use in commercial games today. Human evaluators assess that our model behaves more like humans than both commercially‐available bots and procedural movement controllers scripted by experts (16% to 59% higher by TrueSkill rating of “human‐like”). Using experiments involving in‐game bot vs. bot self‐play, we demonstrate that our model performs simple forms of teamwork, makes fewer common movement mistakes, and yields movement distributions, player lifetimes, and kill locations similar to those observed in professional CS:GO match play.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.15173