This work contributes to developing an agent based on deep reinforcement learning capable of acting in a beyond visual range (BVR) air combat simulation environment. The paper presents an overview of building an agent representing a high-performance fighter aircraft that can learn and improve its role in BVR combat over time based on rewards calculated using operational metrics. Also, through self-play experiments, it expects to generate new air combat tactics never seen before. Finally, we hope to examine a real pilot's ability, using virtual simulation, to interact in the same environment with the trained agent and compare their performances. This research will contribute to the air combat training context by developing agents that can interact with real pilots to improve their performances in air defense missions.

翻译：本研究致力于开发一种基于深度强化学习、能够在超视距（BVR）空战模拟环境中自主行动的智能体。本文概述了构建代表高性能战斗机的智能体过程，该智能体能够根据基于作战指标计算的奖励，随时间学习并提升其在超视距作战中的角色表现。此外，通过自我博弈实验，预期将生成此前从未出现过的新型空战战术。最后，我们希望检验真实飞行员在虚拟仿真中与训练好的智能体在同一环境中交互的能力，并比较双方的性能表现。通过开发能够与真实飞行员交互以提升其在防空任务中性能的智能体，本研究将为空战训练领域做出贡献。