1. [Publications](/index.php/publications) 2. Learning to Move Like Professional Counter-Strike Players # Learning to Move Like Professional Counter-Strike Players ![](/sites/default/files/styles/wide/public/publications/New%20Bitmap%20image.jpg?itok=Dodvpk_6) 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. ## Authors David Durst (Stanford) F. Xie (Activision Blizzard) V. Sarukkai (Stanford) Brennan Shacklett (Stanford) [Iuri Frosio](/index.php/person/iuri-frosio) [Chen Tessler](/index.php/person/chen-tessler) [Joohwan Kim](/index.php/person/joohwan-kim) C. Taylor (Activision Blizzard) G. Bernstein (University of Washington) S. Choudhury (Cornell) P. Hanrahan, (Stanford) Kayvon Fatahalian (Stanford) ## Publication Date Wednesday, September 18, 2024 ## Published in [The 23rd ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA 2024)](https://computeranimation.org/program.html) ## Research Area [Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence) [Computer Graphics](/index.php/research-area/computer-graphics) [Esports](/index.php/research-area/esports) [Generative AI](/index.php/research-area/generative-ai) [Human Computer Interaction](/index.php/research-area/human-computer-interaction) [Robotics](/index.php/research-area/robotics) ## External Links [Link to the paper](https://dl.acm.org/doi/10.1111/cgf.15173) ## Uploaded Files [v43i8\_cgf15173.pdf](https://d1qx31qr3h6wln.cloudfront.net/publications/v43i8_cgf15173.pdf "Open file in new window")12.08 MB ## Copyright Copyright by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or . The definitive version of this paper can be found at ACM's Digital Library .