Manoeuvre decision-making of unmanned aerial vehicles in air combat based on an expert actor-based soft actor critic algorithm

Abstract

The demand for autonomous motion control of unmanned aerial vehicles in air combat is boosted as taking the initiative in combat appears more and more crucial. Unmanned aerial vehicles inability to manoeuvre autonomously during air combat that features highly dynamic and uncertain manoeuvres of the enemy; however, limits their combat capabilities, which proves to be very challenging. To meet the challenge, this article proposes an autonomous manoeuvre decision model using an expert actor-based soft actor critic algorithm that reconstructs empirical replay buffer with expert experience. Specifically, the algorithm uses a small amount of expert experience to increase the diversity of the samples, which can largely improve the exploration and utilisation efficiency of deep reinforcement learning. And to simulate the complex battlefield environment, a one-to-one air combat model is established and the concept of missile's attack region is introduced. The model enables the one-to-one air combat to be simulated under different initial battlefield situations. Simulation results show that the expert actor-based soft actor critic algorithm can find the most favourable policy for unmanned aerial vehicles to defeat the opponent faster, and converge more quickly, compared with the soft actor critic algorithm.