UAV cooperative air combat maneuver decision based on multi-agent reinforcement learning

In order to improve the autonomous ability of unmanned aerial vehicles (UAV) to implement air combat mission, many artificial intelligence-based autonomous air combat maneuver decision-making studies have been carried out, but these studies are often aimed at individual decision-making in 1v1 scenarios which rarely happen in actual air combat. Based on the research of the 1v1 autonomous air combat maneuver decision, this paper builds a multi-UAV cooperative air combat maneuver decision model based on multi-agent reinforcement learning. Firstly, a bidirectional recurrent neural network (BRNN) is used to achieve communication between UAV individuals, and the multi-UAV cooperative air combat maneuver decision model under the actor-critic architecture is established. Secondly, through combining with target allocation and air combat situation assessment, the tactical goal of the formation is merged with the reinforcement learning goal of every UAV, and a cooperative tactical maneuver policy is generated. The simulation results prove that the multi-UAV cooperative air combat maneuver decision model established in this paper can obtain the cooperative maneuver policy through reinforcement learning, the cooperative maneuver policy can guide UAVs to obtain the overall situational advantage and defeat the opponents under tactical cooperation.