UAV swarm air combat maneuver decision-making method based on multi-agent reinforcement learning and transferring

During short-range air combat involving unmanned aircraft vehicle (UAV) swarms, UAVs must make accurate maneuver decisions based on information from both enemy and friendly UAVs. This dual requirement of competition and cooperation presents a significant challenge in the field of unmanned air combat. In this paper, a method based on multi-agent reinforcement learning (MARL) is proposed to address this issue. An actor network containing three subnetworks that can handle different types of situational information is designed. Hence, the results from simpler one-on-one scenarios are leveraged to enhance the complex swarm air combat training process. Separate state spaces for local and global information are designed for the actor and critic networks. A detailed reward function is proposed to encourage participation. To prevent lazy participants in air combat, a reward assignment operation is applied to distribute these dense rewards. Simulation testing and ablation experiments demonstrate that both the transfer operation and reward assignment operation can effectively deal with the swarm air combat scenario, and reflect the effectiveness of the proposed method.