title: Learning to Move Like Professional Counter-Strike Players
summary: 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.
paper_authors._id: 66cd4a993bd2c33f8779a413
paper_authors.name: David Durst
paper_authors._id: 66cd4a993bd2c33f8779a414
paper_authors.name: Feng Xie
paper_authors._id: 66cd4a993bd2c33f8779a415
paper_authors.name: Vishnu Sarukkai
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paper_authors.fullname: Vishnu Sarukkai
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paper_authors.user: vsanimator
paper_authors._id: 66cd4a993bd2c33f8779a416
paper_authors.name: Brennan Shacklett
paper_authors.status: admin_assigned
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paper_authors.fullname: Brennan Shacklett
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paper_authors.user: shacklettbp
paper_authors._id: 66cd4a993bd2c33f8779a417
paper_authors.name: Iuri Frosio
paper_authors._id: 66cd4a993bd2c33f8779a418
paper_authors.name: Chen Tessler
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paper_authors.fullname: Chen Tessler
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paper_authors.user: ctessler
paper_authors._id: 66cd4a993bd2c33f8779a419
paper_authors.name: Joohwan Kim
paper_authors._id: 66cd4a993bd2c33f8779a41a
paper_authors.name: Carly Taylor
paper_authors._id: 66cd4a993bd2c33f8779a41b
paper_authors.name: Gilbert Bernstein
paper_authors._id: 66cd4a993bd2c33f8779a41c
paper_authors.name: Sanjiban Choudhury
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paper_authors.fullname: Sanjiban Choudhury
paper_authors.type: user
paper_authors.user: sc2582
paper_authors._id: 66cd4a993bd2c33f8779a41d
paper_authors.name: Pat Hanrahan
paper_authors._id: 66cd4a993bd2c33f8779a41e
paper_authors.name: Kayvon Fatahalian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-27T12:46:28.465Z
paper_authors._id: 6501ceb6d6583ade3dff5399
paper_authors.avatarUrl: /avatars/07fff5e56b9969642060acc43dd238fe.svg
paper_authors.fullname: Kayvon Fatahalian
paper_authors.type: user
paper_authors.user: kayvonf
paper_title: Learning to Move Like Professional Counter-Strike Players
paper_summary: 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.
paper_ai_summary: A transformer-based model generates efficient and human-like team movement in CS:GO through data-driven learning from professional gameplay.
paper_ai_keywords: 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