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:本论文旨在利用深度强化学习方法构建一种能够在超视距空战模拟环境中操作的自主智能体。文章概括了开发一种代表高性能战斗机的代理的过程,该代理可以根据计算得出的操作指标奖励来学习和提高其在超视距空战中的作用。通过自我对抗实验,该研究旨在生成从未见过的新的空战战术。最后,我们希望通过使用虚拟模拟器来检验真实飞行员在相同环境中与经过训练的代理的表现,并加以比较。该研究将开发出可以与真实飞行员互动以提高空防任务表现的代理,对空战训练上的应用具有重要意义。