Air combat intelligent decision-making method based on self-play and deep reinforcement learning

Air combat is an important element in the three-dimensional nature of war, and intelligent air combat has become a hotspot and focus of research in the military field both domestically and internationally. Deep reinforcement learning is an important technological approach to achieving air combat intelligence. To address the challenge of constructing high-level opponents in single agent training method, a self-play based air combat agent training method is proposed, and a visualization research platform is built to develop a decision-making agent for close-range air combat. The field knowledge of pilots is embedded in the design process of the agent’s observation, action, and reward, training the agent to convergence. Simulation experiments show that the air combat tactics of agent gradually improves by self-play training, achieving a win rate of over 70% against the decision making by single agent training and the emerging of the strategies similar to human “single/double loop” tactics.