IOS-RAFS: Intelligent Opponent Selection Training Framework via Fuzzy Logic-Based Switching between “Rule-AI” Dual Strategy Libraries

In recent years, reinforcement learning-based aerial combat strategy design has shown significant advantages in decision-making efficiency and flexibility. However, improper opponent strategy selection during training may hinder the agent’s ability to handle diverse and complex scenarios, reducing the generalization and performance of its strategies. To address this, this paper proposes “IOS-RAFS”, a framework that dynamically adjusts opponent levels at different stages to provide a more diverse and adaptive environment. In the IOS-RAFS training framework, IOS refers to intelligent opponent selection, RA represents the “Rule-AI” dual strategy libraries, which are established to enhance the flexibility and diversity of opponent strategies, and FS represents a fuzzy logic-based method for switching between opponent strategy libraries, periodically selecting the strategy library for the next training phase based on training trends and model performance. Finally, the effectiveness of the proposed algorithm is validated through comparisons with the landmark methods in aerial combat, namely, the genetic fuzzy tree (GFT) algorithm and the expert-designed weapon engagement zones (WEZs) algorithm, demonstrating a significant improvement in rewards and achieving a win rate of at least 82.3%.