Executive Summary
This paper proposes a tactical intent-driven method for autonomous air combat behavior generation, addressing challenges in deep reinforcement learning such as interpretability and transferability. It introduces a dueling-noisy-multi-step DQN algorithm to improve value function approximation, exploration efficiency, and network generalization. The method demonstrates superior performance and stability, offering tactical support for intelligent air combat decision-making.
Why It Matters
This document is crucial for defense analysts as it details advancements in AI-driven autonomous air combat, directly impacting future aerial warfare doctrines and the development of intelligent unmanned combat systems.
Key Takeaways
- A novel tactical intent-driven method enhances interpretability and adaptability of AI in autonomous air combat.
- The dueling-noisy-multi-step DQN algorithm improves stability and convergence speed in complex air combat scenarios.
- Reward design is critical, as conflicts between sparse and dense rewards can lead to suboptimal AI strategies.
- The proposed method offers tactical support for intelligent decision-making in air combat.
Strategic Relevance
This research directly contributes to the development of highly autonomous and intelligent air combat systems, potentially shifting the balance of power in aerial warfare by enabling more effective and adaptable unmanned platforms. It highlights the critical role of AI in future military operations and the need for robust, interpretable decision-making algorithms.