Hierarchical Multi-Agent Reinforcement Learning Architecture: A high-level agent formulates strategy by selecting optimal low-level agents, which are specialized to execute evasive manoeuvres for different threat scenarios.

Automated, Adaptive Curriculum Learning via a Genetic Algorithm: Efficient Exploration guided by a Learning-Progress-Aware Fitness Function.

Efficient Decision Support: Provides commanders with actionable tactical plans (e.g., countermeasures, evasion courses) for complex threat situations.