# Design of Swarm Intelligence Control Based on Double-Layer Deep Reinforcement Learning
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Related Work** & **Strengths** & **Weaknesses** & **Comparison to This** \\
**Category** & & & & & **Work** \\ \hline \multirow{6}{*}{ Centralized Control} & [PERSON] et al. (2011), & & & This work addresses the shortcomings of centralized control by decoupling local and global optimization, enhancing scalability and adaptability in complex scenarios \\  & [PERSON] et al. (2012), & & & & \\  & [PERSON] et al. (2013), & & & & \\  & [PERSON] et al. (2014), & & & & \\  & [PERSON] et al. (2015) & & & & \\  & [PERSON] et al. (2015) & & & & \\ \hline \multirow{6}{*}{ Distributed Control} & [PERSON] et al. (2016), & Better fault tolerance & Increased decision complexity, load balancing issues, lack of effective information & This work resolves the coordination problem in distributed control by introducing a double-layer \\  & [PERSON] et al. (2017), & & & & \\  & [PERSON] et.al. (2018), & & & \\  & [PERSON] et al. (2019), & & & \\  & [PERSON] et al. (2019) & & & \\  & [PERSON] et al. (2019) & & & \\  & & & & \\ \hline \multirow{6}{*}{ Hybrid Control} & [PERSON] et al. (2011), & & Higher collaboration complexity, insufficient advantages of centralized and reduced control flexibility & This work optimizes the balance between task completion and energy efficiency using a double-layer framework that improves both local and global optimization \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related works. Hybrid control combines the advantages of centralized and distributed control, integrating central controller management and agent autonomy to improve system robustness and scalability. However, it also introduces challenges, including increased collaboration complexity, insufficient global optimization capabilities, and reduced control reliability. To solve these problems, [PERSON] et al. [21] proposed a hybrid aggregation adaptive control algorithm to resolve UAV swarm conflicts by determining optimal hybrid weights, thereby optimizing the performance in complex environments. [PERSON] et al. [22] combined linear active disturbance rejection control with a hybrid particle swarm optimization algorithm to ensure high controller reliability while optimizing the global maximum power output. [PERSON] et al. [23] proposed a gorilla troops optimizer based on a optimal reinforcement learning controller to enhance the frequency control of hybrid power systems. [PERSON] et al. [24] investigated coordination mechanisms in multi-objective deep reinforcement learning to minimize energy costs and delay conflicts in UAV swarms. [PERSON] et al. [25] proposed a distributed collaborative search method based on a multi-agent reinforcement learning algorithm, which improves global optimization capabilities in complex environments and is applicable to large-scale scenarios. Although hybrid control can combine the advantages of both, it still faces the problem of balancing task optimization and energy efficiency. Especially in complex tasks and dynamic environments, the effectiveness and flexibility of hybrid control are still insufficient. A comparison of existing research approaches, their contributions, and their limitations is summarized in Table 1, which highlights the key differences between prior studies and the proposed method. Despite significant progress in optimizing complex scenarios, current swarm intelligence control methods still face several limitations. These include incomplete decoupling between local and global optimization, as well as limited dynamic adaptability. Hence, achieving the simultaneous optimization of energy efficiency and task completion remains challenging. To resolve these challenges, this paper proposes a swarm intelligence control design method based on double-layer deep reinforcement learning.