Deep Relationship Graph Reinforcement Learning for Multi-Aircraft Air Combat

Air combat Artificial Intelligence (AI) has attracted increasing attentions from aeronautics engineers and artificial intelligence researchers. However, it is often of great difficulties for the existing methods to solve the collaboration problems in multi-aircraft air combat due to their high complexity incurred by combination explosion. In view of this, we propose a Deep Relationship Graph Reinforcement Learning (DRGRL) algorithm for multi-aircraft collaboration. Specifically, DRGRL significantly simplifies the complex situation space via abstracting the original problem into a symbolic form. Besides, a novel Air Combat Relationship Graph (ACRG) is introduced to represent the learned collaboration pattern, which concentrates on the most important combat relationships for tactic decision making. Consequently, experiments are conducted in an air combat simulation environment named WUKONG. The comprehensive experimental results demonstrate that DRGRL could evidently learn some valuable collaboration patterns and achieve better combat performance than state-of-the-art air combat AI methods.