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