Deep Reinforcement Learning-Based Air Defense Decision-Making Using Potential Games

This study addresses the challenge of intelligent decision-making for command-and-control systems in air defense combat operations. Current autonomous decision-making systems suffer from limited rationality and insufficient intelligence during operation processes. Recent studies have proposed methods based on deep reinforcement learning (DRL) to address these issues. However, DRL methods typically face challenges related to weak interpretability, lack of convergence guarantees, and high-computing power requirements. To address these issues, a novel technique for large-scale air defense decision-making by combining a DRL technique with game theory is discussed. The proposed method transforms the target assignment problem into a potential game that provides theoretical guarantees for Nash equilibrium (NE) from a distributed perspective. The air-defense decision problem is decomposed into separate target selection and target assignment problems. A DRL method is used to solve the target selection problem, while the target assignment problem is translated into a target assignment optimization game. This game is proven to be an exact potential game with theoretical convergence guarantees for an NE. Having simulated the proposed decision-making method using a digital battlefield environment, the effectiveness of the proposed method is demonstrated.