DASF-GRL: Dynamic Agent-Scaling framework with game-augmented reinforcement learning for defensive counter-air operations
AI Analysis
The Dynamic Agent-Scaling Framework with Game-Augmented Reinforcement Learning (DASF-GRL) enhances Defensive Counter-Air operations by dynamically adjusting defender populations based on real-time threats. This framework integrates advanced AI techniques to improve decision-making and defense success rates against intelligent aerial threats.
Key Takeaways
- DASF-GRL dynamically scales defender populations in response to real-time threats.
- The framework uses a hybrid imitation-reinforcement training strategy with attention mechanisms.
- Incorporates a safety barrier function based on differential game theory to enhance policy reliability.
- Developed a DCA simulation platform for reinforcement learning validation.
- Demonstrated improved defense success rates and convergence speed in scenarios with multiple attackers.
Why It Matters
This development is significant as it addresses vulnerabilities in existing air defense systems by enhancing the adaptability and effectiveness of Defensive Counter-Air operations. The integration of AI and game theory into military strategy represents a major advancement in counter-UAS capabilities, crucial for protecting critical infrastructure against sophisticated aerial threats.
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[Expert Systems with Applications]
[Volume 298, Part B], 1 March 2026, 129702
DASF-GRL: Dynamic Agent-Scaling framework with game-augmented reinforcement learning for defensive counter-air operations
Author links open overlay panelYuxuanChenab, HeLuoabcd, GuoqiangWangabcd
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[https://doi.org/10.1016/j.eswa.2025.129702] [Get rights and content]
Abstract
Defensive Counter-Air (DCA) operations are pivotal for modern air defense, but existing studies are limited by static defender populations and oversimplified attacker models. We address these limitations with the Dynamic Agent-Scaling Framework with Game-Augmented Reinforcement Learning (DASF-GRL), which dynamically scales defender populations based on real-time threat levels. Specifically, we introduce a hybrid imitation-reinforcement training strategy that integrates attention mechanisms into critic networks to enable dynamic agent scaling. By incorporating a safety barrier function rooted in differential game theory, we constrain agents’ action spaces and enhance policy reliability. Furthermore, we developed a DCA simulation platform supporting reinforcement learning validation and designed a novel Apollonius-based penetration strategy for attackers to improve algorithmic robustness. Experiments demonstrate that DASF-GRL adaptively adjusts defender populations across scenarios involving 20, 40, and 60 attackers, markedly outperforming baseline methods in convergence speed and defense success rates. This framework offers novel theoretical paradigms and practical tools for intelligent decision-making in DCA environments.
Introduction
In modern warfare, intelligent aerial weapons have become pivotal military assets, underscoring their strategic importance in both air defense and offensive operations. From Middle Eastern conflicts to the Russia-Ukraine war, intelligent attack clusters equipped with wide-area coverage and precision strike capabilities have threatened critical defender assets, revealing vulnerabilities in existing air defense systems. Within this context, Defensive Counter-Air (DCA) operations have evolved into a critical component of military strategic planning (United States Department of the Air Force, 2019). The primary objective of DCA is to minimize airspace penetration by intercepting threats, thereby safeguarding essential military infrastructure and ensuring airspace security. These missions often deploy Unmanned Aerial Vehicles (UAVs) or highly autonomous collaborative combat platforms. Enhanced by advanced artificial intelligence algorithms, such systems autonomously execute complex decision-making