Reinforcement Learning for Real-Time Decision-Making in autonomous air combat systems

This study examines the application of Reinforcement Learning (RL) in the domain of autonomous air combat systems, emphasizing real-time decision-making. Autonomy in air combat necessitates a framework that can adapt to dynamic environments and make instantaneous, strategic decisions. RL, with its ability to learn optimal policies through interaction with the environment, presents a potent solution for such systems. The core contribution of this research is the development of a novel RL algorithm tailored for high-dimensional, continuous state and action spaces inherent in air combat scenarios. This algorithm integrates state-of-the-art techniques in deep learning with a custom reward structure designed to promote effective combat strategies. The architecture is evaluated in a simulated air combat environment, demonstrating superior performance against traditional control strategies and other machine learning approaches. Results reveal that the proposed system not only excels in one-on-one engagements but also scales efficiently to handle complex multi-agent combat scenarios. This paper also delves into the challenges of computational constraints in real-time applications and proposes solutions for the same. The findings suggest that RL-based systems could significantly enhance the capabilities of autonomous air combat vehicles, paving the way for advanced military AI applications.