Learning to Move Like Professional Counter-Strike Players
Authors: name: David Durst, name: Feng Xie, name: Vishnu Sarukkai, name: Brennan Shacklett, name: Iuri Frosio, name: Chen Tessler, name: Joohwan Kim, name: Carly Taylor, name: Gilbert Bernstein, name: Sanjiban Choudhury, name: Pat Hanrahan, name: Kayvon Fatahalian

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.