A hierarchical reinforcement learning method on Multi UCAV air combat

In the recent years, the unmanned combat aerial vehicle (UCAV) techniques is a hot topic of research. Many researches are studying how to use to fulfill missions and defend enemies based on simulation platforms. Different AI agents have been constructed to control virtual UCAVs to perform tasks on simulation platforms. Rule based AI heavily depends on human knowledge and lacks of flexibility. They cannot adapt to the changing environment. Reinforcement learning based AI has advantages over rule based AI as its depend less on human knowledge. In this paper a hierarchical reinforcement learning method is proposed on Multi-UCAV air combat based on simulation platform. The experiment results showed that the hierarchical approach can outperform state-of-the-art air combat method.