UCAV Air Combat Maneuver Decisions Based on a Proximal Policy Optimization Algorithm with Situation Reward Shaping

Autonomous maneuver decision by an unmanned combat air vehicle (UCAV) is a critical part of air combat that requires both flight safety and tactical maneuvering. In this paper, an unmanned combat air vehicle air combat maneuver decision method based on a proximal policy optimization algorithm (PPO) is proposed. Firstly, a motion model of the unmanned combat air vehicle and a situation assessment model of air combat was established to describe the motion situation of the unmanned combat air vehicle. An enemy maneuver policy based on a situation assessment with a greedy algorithm was also proposed for air combat confrontation, which aimed to verify the performance of the proximal policy optimization algorithm. Then, an action space based on a basic maneuver library and a state observation space of the proximal policy optimization algorithm were constructed, and a reward function with situation reward shaping was designed for accelerating the convergence rate. Finally, a simulation of air combat confrontation was carried out, which showed that the agent using the proximal policy optimization algorithm learned to combine a series of basic maneuvers, such as diving, climb and circling, into tactical maneuvers and eventually defeated the enemy. The winning rate of the proximal policy optimization algorithm reached 62%, and the corresponding losing rate was only 11%.

Automated summary

This paper proposes a method for unmanned combat air vehicle air combat maneuver decision based on the proximal policy optimization algorithm. The method is validated through a simulation experiment, demonstrating its effectiveness.