Research on Autonomous Decision-Making of Multi-UAV Air Combat Based on Deep Reinforcement Learning

To solve the autonomous decision-making problem of multi-UAV air combat, a multi-UAV reinforcement learning self-game autonomous decision-making method for air combat with parameter sharing is proposed. Firstly, a self-game policy pool training framework is designed. The game ability of the multi-UAV autonomous decision-making model is improved by self-game confrontation with a policy pool containing different training generations. Secondly, a parameter-sharing mechanism is proposed to train homogeneous UAVs to improve the efficiency of reinforcement learning training and the utilization of interactive data. Finally, a multi-UAV autonomous decision-making model using the multi-agent deep deterministic policy gradient algorithm is designed so that it can explore effectively in the continuous action space. In addition, experiments are designed to verify the effectiveness and migration of the designed algorithm and training framework.