Research on Multi-UAV Aggregation Task Based on GA-TD3 Algorithm

• Published in: Chinese Control Conference pp. 5560 - 5565
• Main Authors: Zhang, Yaozhong, Ding, Meiyan, Yuan, Yao, Zhang, Jiandong, Shi, Guoqing, Yang, Qiming
• Format: Conference Proceeding
• Language: English
• Published: Technical Committee on Control Theory, Chinese Association of Automation 28.07.2024
• Subjects: Accuracy; aggregation task; Autonomous aerial vehicles; Costs; critic network-assisted evaluation mechanism; Evolutionary computation; GA-TD3 algorithm; learning-based crossover operator; Reinforcement learning; Simulation; TD3 algorithm; Training
• ISSN: 1934-1768
• DOI: 10.23919/CCC63176.2024.10661924

The aggregation task is one of the typical tasks of Unmanned A erial Vehicle (UAV) operations and has always been a hot research topic. With the development of technology and the continuous proves of war, multi-UAV have significant advantages in combat compared to single UAV. To address the intelligent control problem of multi-UAV aggregation task, this paper proposes a Twin Delayed Deep Deterministic policy gradient (TD3) algorithm based on an evolutionary algorithm and introduces an evolutionary reinforcement learning framework. A learning cross operator is proposed to make offspring inherit parent features through training. To avoid high interaction costs, a critic network-assisted evaluation mechanism is proposed, and a response control decision is designed to combine real and virtual fitness to improve accuracy. Therefore, a Twin Delayed Deep Deterministic policy gradient algorithm combining Genetic Algorithm enhanced by learning cross factors and auxiliary evaluation (GA-TD3) is proposed. Simulation results show that GA-TD3 has better training effects and generalization.