Task assignment in ground-to-air confrontation based on multiagent deep reinforcement learning

The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events. Aiming at the problems where existing task assignment methods are applied to ground-to-air confrontation, there is low efficiency in dealing with complex tasks, and there are interactive conflicts in multiagent systems. This study proposes a multiagent architecture based on a one-general agent with multiple narrow agents (OGMN) to reduce task assignment conflicts. Considering the slow speed of traditional dynamic task assignment algorithms, this paper proposes the proximal policy optimization for task assignment of general and narrow agents (PPO-TAGNA) algorithm. The algorithm based on the idea of the optimal assignment strategy algorithm and combined with the training framework of deep reinforcement learning (DRL) adds a multihead attention mechanism and a stage reward mechanism to the bilateral band clipping PPO algorithm to solve the problem of low training efficiency. Finally, simulation experiments are carried out in the digital battlefield. The multiagent architecture based on OGMN combined with the PPO-TAGNA algorithm can obtain higher rewards faster and has a higher win ratio. By analyzing agent behavior, the efficiency, superiority and rationality of resource utilization of this method are verified.