Learning to Move Like Professional Counter-Strike Players

Durst, David; Xie, Feng; Sarukkai, Vishnu; Shacklett, Brennan; Frosio, Iuri; Tessler, Chen; Kim, Joohwan; Taylor, Carly; Bernstein, Gilbert; Choudhury, Sanjiban; Hanrahan, Pat; Fatahalian, Kayvon

Learning to Move Like Professional Counter-Strike Players

Files

v43i8_cgf15173.pdf (12.08 MB)

learningtomovelikeprofessionalcounter-strikeplayers-supplemental.pdf (322.06 KB)

user_study.zip (245.53 MB)

Date

2024

Authors

Durst, David
Xie, Feng
Sarukkai, Vishnu
Shacklett, Brennan
Frosio, Iuri
Tessler, Chen
Kim, Joohwan
Taylor, Carly
Bernstein, Gilbert
Choudhury, Sanjiban
Hanrahan, Pat
Fatahalian, Kayvon

Publisher

The Eurographics Association and John Wiley & Sons Ltd.

Abstract

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.

CCS Concepts: Software and its engineering → Interactive games; Computing methodologies → Learning from demonstrations

        @article{10.1111:cgf.15173
,
journal = {Computer Graphics Forum},
title = {{Learning to Move Like Professional Counter-Strike Players
}},
author = {Durst, David and 
Xie, Feng and 
Hanrahan, Pat and 
Fatahalian, Kayvon and 
Sarukkai, Vishnu and 
Shacklett, Brennan and 
Frosio, Iuri and 
Tessler, Chen and 
Kim, Joohwan and 
Taylor, Carly and 
Bernstein, Gilbert and 
Choudhury, Sanjiban
},
year = {2024
},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.
},
ISSN = {1467-8659
},
DOI = {10.1111/cgf.15173
}
}