Situation assessment in Real-Time Strategy (RTS) games is crucial for understanding decision-making in complex adversarial environments. However, existing methods remain limited in processing multi-dimensional feature information and temporal dependencies. Here we propose a tri-dimensional Space-Time-Feature Transformer (TSTF Transformer) architecture, which efficiently models battlefield situations through three independent but cascaded modules: spatial attention, temporal attention, and feature attention. On a dataset comprising 3,150 adversarial experiments, the 8-layer TSTF Transformer demonstrates superior performance: achieving 58.7% accuracy in the early game (~4% progress), significantly outperforming the conventional Timesformer's 41.8%; reaching 97.6% accuracy in the mid-game (~40% progress) while maintaining low performance variation (standard deviation 0.114). Meanwhile, this architecture requires fewer parameters (4.75M) compared to the baseline model (5.54M). Our study not only provides new insights into situation assessment in RTS games but also presents an innovative paradigm for Transformer-based multi-dimensional temporal modeling.

翻译：即时战略游戏中的态势评估对于理解复杂对抗环境下的决策制定至关重要。然而，现有方法在处理多维特征信息与时间依赖性方面仍存在局限。本文提出了一种三维时空特征Transformer架构，通过三个独立但级联的模块——空间注意力、时间注意力和特征注意力——高效建模战场态势。在包含3,150组对抗实验的数据集上，8层TSTF Transformer展现出卓越性能：在游戏早期阶段（约4%进度）达到58.7%的准确率，显著优于传统Timesformer的41.8%；在游戏中期（约40%进度）达到97.6%的准确率，同时保持较低的性能波动（标准差0.114）。此外，该架构所需参数量（4.75M）少于基线模型（5.54M）。本研究不仅为RTS游戏态势评估提供了新见解，也为基于Transformer的多维时间建模提供了创新范式。