Durst, DavidXie, FengSarukkai, VishnuShacklett, BrennanFrosio, IuriTessler, ChenKim, JoohwanTaylor, CarlyBernstein, GilbertChoudhury, SanjibanHanrahan, PatFatahalian, KayvonSkouras, MelinaWang, He2024-08-202024-08-2020241467-8659https://doi.org/10.1111/cgf.15173https://diglib.eg.org/handle/10.1111/cgf15173In 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 demonstrationsSoftware and its engineering → Interactive gamesComputing methodologies → Learning from demonstrations10.1111/cgf.1517312 pages