Knowledge-Guided Agent-Tactic-Aware Learning for StarCraft Micromanagement

Knowledge-Guided Agent-Tactic-Aware Learning for StarCraft Micromanagement

Yue Hu, Juntao Li, Xi Li, Gang Pan, Mingliang Xu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 1471-1477. https://doi.org/10.24963/ijcai.2018/204

As an important and challenging problem in artificial intelligence (AI) game playing, StarCraft micromanagement involves a dynamically adversarial game playing process with complex multi-agent control within a large action space. In this paper, we propose a novel knowledge-guided agent-tactic-aware learning scheme, that is, opponent-guided tactic learning (OGTL), to cope with this micromanagement problem. In principle, the proposed scheme takes a two-stage cascaded learning strategy which is capable of not only transferring the human tactic knowledge from the human-made opponent agents to our AI agents but also improving the adversarial ability. With the power of reinforcement learning, such a knowledge-guided agent-tactic-aware scheme has the ability to guide the AI agents to achieve high winning-rate performances while accelerating the policy exploration process in a tactic-interpretable fashion. Experimental results demonstrate the effectiveness of the proposed scheme against the state-of-the-art approaches in several benchmark combat scenarios.
Keywords:
Heuristic Search and Game Playing: Game Playing and Machine Learning
Machine Learning Applications: Game Playing