# Design of Swarm Intelligence Control Based on Double-Layer Deep Reinforcement Learning
###### Abstract  
Traditional methods have limitations regarding efficient collaboration and dynamic response in complex dynamic environments. Although existing swarm intelligence control methods possess certain adaptive optimization capabilities, they still face challenges in individual and global collaborative optimization and adaptability. To address this challenge, a swarm intelligence control design method based on double-layer deep reinforcement learning (D-DRL) is proposed. This method uses a double architecture where the inner layer is responsible for dynamic decision-making and behavior optimization, and the outer layer manages resource allocation and strategy optimization. The dynamic interaction between the inner and outer layers, coupled with multi-level collaborative optimization, enhances the system's adaptability and operating performance. The results of the unmanned aerial vehicle (UAV) swarm case study show that our method achieves effective convergence and outperforms existing swarm intelligence control approaches. Specifically, it simultaneously optimizes energy efficiency and task completion amount with a superior performance. This improvement significantly enhances the comprehensive task effectiveness of the swarm. s +
Footnote †: journal: applied sciences  
article  
[color=gray!20,linecolor=gray! system. The diagram encompasses typical application scenarios, such as smart cities, unmanned transportation, and industrial production, highlighting the extensive application and significance of swarm intelligence control in complex environments. Therefore, swarm intelligence control provides new ideas and tools for solving coordination and optimization problems in complex dynamic environments. Its wide applicability and practical significance make it a valuable solution across various fields. In practical applications, swarm intelligence control needs to achieve multi-objective collaborative optimization, such as key indicators like energy efficiency and task completion amount. However, as system scale increases and interactions grow more complex, traditional swarm control methods gradually reveal their limitations [4]. The specific reason can be summarized into two aspects. On the one hand, simple agents lack sufficient adaptive adjustment capabilities in dynamic environments due to limitations in computing, perception, and action capabilities. The shortcoming intensifies the conflict between local and global optimization, ultimately hindering overall system performance improvement. On the other hand, there is a strong coupling between the behavior optimization of agents and the global optimization of the system. The existing methods currently lack a dynamic adaptive mechanism for multi-objective collaborative optimization, and it is difficult to effectively decouple individual behavior optimization from global optimization. In the dynamic and complex environment, the inability to balance the relationship between the individual optimal solution and the global optimal solution affects the overall performance  
Figure 1: The operational framework of swarm intelligence control in complex dynamic environments. The unmanned transportation on the upper left and industrial production on the upper right represent typical scenarios for applying swarm intelligence control in different fields. The dynamic interactive module above the center forms a closed loop of the urban environment below and the group intelligent control scene above through information sharing. of the system [5]. For example, in UAV swarm control, it is necessary to ensure task completion amount while minimizing energy cost and enhancing system intelligence. Efficient energy utilization not only extends the system operation time, but also reduces the cost and enhances the task completion efficiency [6]. However, when scheduling UAV swarms in complex dynamic environments, balancing the conflict between individual behaviors and global objectives, as well as enhancing UAV adaptability, remains a challenge that affects the system's performance [7].