學習像專業《反恐精英》玩家一樣移動

Learning to Move Like Professional Counter-Strike Players

August 25, 2024

作者: David Durst, Feng Xie, Vishnu Sarukkai, Brennan Shacklett, Iuri Frosio, Chen Tessler, Joohwan Kim, Carly Taylor, Gilbert Bernstein, Sanjiban Choudhury, Pat Hanrahan, Kayvon Fatahalian

cs.AI

摘要

在多人第一人稱射擊遊戲中，如《反恐精英：全球攻勢》（CS:GO），協調移動是高級戰略遊戲中的關鍵組成部分。然而，團隊協調的複雜性和流行遊戲地圖中存在的各種條件使得為每種情況編寫手工製作的移動策略變得不切實際。我們展示了可以採用數據驅動方法來為CS:GO創建類似人類移動控制器的可能性。我們整理了一個包含123小時專業遊戲玩法軌跡的團隊移動數據集，並使用該數據集來訓練基於Transformer的移動模型，為遊戲中“Retakes”回合的所有玩家生成類似人類的團隊移動。重要的是，移動預測模型是高效的。對所有玩家進行推斷每個遊戲步驟的成本低於0.5毫秒（攤銷成本）在單個CPU核心上，使其在當今商業遊戲中的應用成為可能。人類評估者評估我們的模型行為更像人類，比商業可用機器人和專家編寫的程序化移動控制器（根據“人類”TrueSkill評分高16％至59％）更接近人類。通過涉及遊戲機器人自我對戰的實驗，我們展示了我們的模型執行簡單形式的團隊合作，做出更少的常見移動錯誤，並產生類似於專業CS:GO比賽中觀察到的移動分佈、玩家壽命和擊殺位置。

English

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.