Multi-agent Reinforcement Learning-Based UAV Swarm Confrontation: Integrating QMIX Algorithm with Artificial Potential Field Method
Chao Zhang, Zhangyan Wu, Zhaoxin Li, Hao Xu, Zhihao Xue, Rongrong Qian · 2024
As an important area of machine learning, re-inforcement learning has specific applicability in multi-agent systems (including UAV swarms). In this article, we use re-inforcement learning algorithm (i.e., the QMIX algorithm) to resolve the problem of UAV swarm confrontation, considering the condition of asymmetric confrontation under which the adversary's combat power is much stronger than our own. First, after constructing the system model, we develop the QMIX algorithm by designing the state space, action space, and reward function. Second, we propose a confrontation strategy that integrates decisions made by the QMIX algorithm and the artificial potential field method for UAV swarm confrontation. Finally, the experimental results show that our proposed confrontation strategy has a 72% higher win rate compared to the QMIX algorithm under asymmetric confrontation conditions.