Collaborative Decision-making in Heterogeneous UAV Swarms based on Multi-agent Deep Reinforcement Learning

Addressing the complexity of collaborative decision-making within heterogeneous UAV swarms in dynamic scenarios, and the difficulty in understanding the overall mission, this paper presents the AM-Qmix algorithm. The algorithm incorporates the prioritized multi-pool of experience replay approach into deep reinforcement learning for heterogeneous multi-agent systems, enhancing the learning capabilities of the UAV swarm. Additionally, through local behavioral guidance strategies, the algorithm improves UAVs' understanding and execution efficiency for specific tasks, thereby increasing the collaborative decision-making capacity of the entire swarm. Simulation experiments conducted on collaborative material transport tasks with heterogeneous UAV swarms have demonstrated the superiority of our algorithm in resolving issues of collaborative decision-making among heterogeneous UAV swarms.