Deep ensemble learning of tactics to control the main force in a real-time strategy game

Professional StarCraft game players are likely to focus on the management of the most important group of units (called the main force) during gameplay. Although macro-level skills have been observed in human game replays, there has been little study of the high-level knowledge used for tactical decision-making, nor exploitation thereof to create AI modules. In this paper, we propose a novel tactical decision-making model that makes decisions to control the main force. We categorized the future movement direction of the main force into six classes (e.g., toward the enemy’s main base). The model learned to predict the next destination of the main force based on the large amount of experience represented in replays of human games. To obtain training data, we extracted information from 12,057 replay files produced by human players and obtained the position and movement direction of the main forces through a novel detection algorithm. We applied convolutional neural networks and a Vision Transformer to deal with the high-dimensional state representation and large state spaces. Furthermore, we analyzed human tactics relating to the main force. Model learning success rates of 88.5%, 76.8%, and 56.9% were achieved for the top-3, -2, and -1 accuracies, respectively. The results show that our method is capable of learning human macro-level intentions in real-time strategy games.