Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach
This research aims to improve air combat training for pilots by developing agents that can interact with them in simulations, but it appears incremental as it applies existing deep reinforcement learning methods to a specific domain.
This paper tackled the problem of developing an autonomous agent for beyond visual range air combat using deep reinforcement learning, with the result being an agent that learns and improves based on operational metrics and is expected to generate new tactics through self-play, though no concrete numbers are provided.
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.