StarCraft adversary-agent challenge for pursuit–evasion game

Abstract

A reinforcement learning environment with adversary agents is proposed in this work for pursuit–evasion game in the presence of fog of war, which is of both scientific significance and practical importance in aerospace applications. One of the most popular learning environments, StarCraft, is adopted here and the associated mini-games are analyzed to identify its potential applications and limitations for training adversary agents. The key contribution includes the analysis of the best performance that an intelligent agent could be achieved by incorporating control and differential game theory into the specific reinforcement learning environment, and the development of a StarCraft adversary-agents challenge (SAAC) environment by extending the current StarCraft mini-games. The subsequent study showcases the use of this learning environment and the effectiveness of an adversary agent for evaders. Overall, along with rapidly-emerging reinforcement learning technologies, the proposed SAAC environment should benefit pursuit–evasion studies in particular and aerospace applications in general. Last but not least, the corresponding code is available at GitHub.