用游戏数据构建简化版战场模拟器，专攻战略规划研究
A Data-Driven Discretized CS:GO Simulation Environment to Facilitate Strategic Multi-Agent Planning Research
- 用路径点系统将复杂动作抽象为离散决策
- 仅靠移动指令还原真实比赛回放，匹配度高
- 适合研究多智能体策略与行为生成的学者
复杂多智能体交互的仿真环境需在高保真度与计算效率间取得平衡。我们提出DECOY，一种新型多智能体仿真框架，将3D地形中的长期战略规划抽象为高层离散化模拟，同时保持低层环境细节。以《反恐精英：全球攻势》（CS:GO）为测试平台，该框架仅基于移动决策进行战术定位模拟，无需显式建模瞄准、射击等底层机制。核心是路径点系统，用于简化和离散化连续状态与动作，并结合基于真实赛事数据训练的神经预测与生成模型，重建事件结果。大量评估表明，由人类数据生成的比赛回放与原游戏高度一致。我们公开了该仿真环境，为战略多智能体规划与行为生成研究提供有力工具。
原文摘要 · Abstract (English)
Modern simulation environments for complex multi-agent interactions must balance high-fidelity detail with computational efficiency. We present DECOY, a novel multi-agent simulator that abstracts strategic, long-horizon planning in 3D terrains into high-level discretized simulation while preserving low-level environmental fidelity. Using Counter-Strike: Global Offensive (CS:GO) as a testbed, our framework accurately simulates gameplay using only movement decisions as tactical positioning -- without explicitly modeling low-level mechanics such as aiming and shooting. Central to our approach is a waypoint system that simplifies and discretizes continuous states and actions, paired with neural predictive and generative models trained on real CS:GO tournament data to reconstruct event outcomes. Extensive evaluations show that replays generated from human data in DECOY closely match those observed in the original game. Our publicly available simulation environment provides a valuable tool for advancing research in strategic multi-agent planning and behavior generation.
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