Layered Autonomous Decision Framework and DDQN-Enhanced Training for the BVR Air Game

Modern beyond visual range air game is characterized by high dynamic complexity and multi-factor interweaving, making it challenging for traditional decision-making methods. This paper constructs an autonomous decision-making framework for a single-plane beyond visual range air game that encompasses the entire game process. We design tactical rules with both offensive and defensive capabilities and formulate an autonomous air game decision-making method. This method is primarily driven by rules and is complemented by an intelligent maneuver decision-making approach based on the Double Deep Q-Network (DDQN). Simulation results demonstrate that our designed method effectively enhances the maneuvering decision-making ability of aircraft and has promising application prospects.