#Air combat maneuvers rolling series#

The winning rate of the proximal policy optimization algorithm reached 62%, and the corresponding losing rate was only 11%.Ī rule-based expert system, which needs less effort to engineer and possesses high interpretability, constructs production rules with predicate logic similar to IF–ELSE–THEN.

#Air combat maneuvers rolling series#

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. 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. 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. 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. In this paper, an unmanned combat air vehicle air combat maneuver decision method based on a proximal policy optimization algorithm (PPO) is proposed.

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.