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
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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David Durst
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Feng Xie
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Vishnu Sarukkai
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2024-08-27T12:45:13.356Z
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Vishnu Sarukkai
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Brennan Shacklett
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2024-08-27T12:45:19.183Z
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Brennan Shacklett
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Iuri Frosio
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Chen Tessler
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2025-03-19T09:48:59.881Z
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Chen Tessler
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ctessler
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Joohwan Kim
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Carly Taylor
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Gilbert Bernstein
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Sanjiban Choudhury
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2025-01-07T15:54:05.674Z
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Sanjiban Choudhury
user
sc2582
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Pat Hanrahan
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Kayvon Fatahalian
admin_assigned
2024-08-27T12:46:28.465Z
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Kayvon Fatahalian
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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
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.
A transformer-based model generates efficient and human-like team movement in CS:GO through data-driven learning from professional gameplay.
transformer-based movement model data-driven approach team movement dataset CS:GO human-like movement movement prediction model inference efficiency TrueSkill rating self-play experiments teamwork player lifetimes kill locations