Research on Multi-Target Allocation Algorithm for UAV Swarms Based on IA2C Reinforcement Learning

In the target distribution area, unmanned aerial vehicle (UAV) swarms perform tasks of discovering, identifying, and capturing mobile target groups. The key link is the effective allocation of target groups. This study proposes an automatic target allocation strategy for UAV swarms based on the independent advantage actor-critic (IA2C) reinforcement learning algorithm, aiming to enhance the autonomous allocation ability of UAV swarms. IA2C is scalable in a distributed environment and effectively solves the problems of multi-agent collaboration and competition by enabling independent learning for each agent. Through this method, UAV swarms can efficiently strike more targets within a limited time while reducing the risk of being intercepted, thereby maximizing the overall strike efficiency.