学习像专业反恐精英玩家一样移动

摘要

在《反恐精英：全球攻势》（CS:GO）等多人第一人称射击游戏中，协调移动是高水平战略游戏的关键组成部分。然而，团队协作的复杂性和流行游戏地图中存在的各种条件使得为每种情况编写手工制作的移动策略变得不切实际。我们展示了可以采用数据驱动的方法为CS:GO创建类似人类的移动控制器。我们整理了一个团队移动数据集，包括123小时的专业游戏轨迹，并使用该数据集训练了一个基于Transformer的移动模型，为游戏中“反扑”回合的所有玩家生成类似人类的团队移动。重要的是，移动预测模型高效。在单个CPU核心上，为所有玩家执行推断每个游戏步骤的时间不到0.5毫秒（摊销成本），使其在当今商业游戏中可行。人类评估者评估表明，我们的模型比商业可用的机器人和专家编写的程序化移动控制器更像人类（根据“类人”TrueSkill评分高出16%至59%）。通过涉及游戏内机器人自我对战的实验，我们展示了我们的模型执行简单形式的团队合作，减少了常见的移动错误，并产生了类似于专业CS:GO比赛中观察到的移动分布、玩家寿命和击杀位置。

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.

学习像专业反恐精英玩家一样移动

Learning to Move Like Professional Counter-Strike Players

摘要

Summary

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