# Design of Swarm Intelligence Control Based on Double-Layer Deep Reinforcement Learning
## 3 Double-Layer Deep Reinforcement Learning Design for Swarm Intelligence Control
### Bi-Directional Dynamic Interaction Process and Evolutionary Game Mechanism
The choice of the UAV swarm control algorithm can delay the turning point of task completion efficiency and improve task completion efficiency. (4) For comprehensive task effectiveness, D-DRL performs the best in enhancing comprehensive task effectiveness, ensuring that the UAV swarm control system maximizes task completion amount based on the task completion efficiency (energy efficiency). Compared to other intelligent control algorithms without adjustment mechanisms, RBCA exhibits greater stability and resistance to change in complex dynamic environments. Deep reinforcement learning not only has a high learning ability, but also self-regulation and collaboration capabilities. While the RBCA partially mitigates the trade-off between energy efficiency and task completion amount, its learning mechanism struggles under dynamic conditions. Specifically, changes in agent behavior or environmental variations may cause delays in adaptation responses, ultimately hindering the swarm's ability to achieve desired outcomes during collaborative tasks. The experimental results shown in Figures 5-7 indicate that D-DRL maximizes the task completion amount while ensuring system task completion efficiency (energy efficiency), leading to the highest comprehensive task effectiveness for the swarm. Simple models (such as RBCA) can alleviate undesirable evolution to some extent but cannot adapt to complex task requirements in the long term. In contrast, the double-layer learning framework effectively mitigates the conflict between energy efficiency and task completion amount, improving the adaptability of the UAV swarm in complex environments. To ensure the reliability of the results, this paper conducts experiments with 10 different random seeds and averages the results, as shown in Table 6. The table lists key metrics related to the task, including the mean values of task income, energy cost, task completion amount, task completion efficiency, task throughput, and comprehensive task effectiveness. The experimental results demonstrate that D-DRL outperforms other methods in all metrics, confirming its superiority in UAV swarm control. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Income** & **Amount** & **Cost** & **Throughput** & **Efficiency** & **Effectiveness** \\ \hline
**D-DRL** & **1111.11 \(\pm\) 0.00** & **417.77 \(\pm\) 4.98** & **1229.89 \(\pm\) 6.29** & **0.42 \(\pm\) 0.00** & **27.66 \(\pm\) 1.31** & **11/650.87 \(\pm\) 457.51** \\
**ABC-DRL** & 1082.61 \(\pm\) 4.44 & 186.57 \(\pm\) 9.27 & 1325.13 \(\pm\) 20.69 & 0.19 \(\pm\) 0.01 & 1.55 \(\pm\) 3.77 & 268.73 \(\pm\) 698.61 \\
**DRL** & 1111.11 \(\pm\) 0.00 & 404.99 \(\pm\) 2.25 & 1241.00 \(\pm\) 9.55 & 0.40 \(\pm\) 0.00 & 22.87 \(\pm\) 2.20 & 9260.62 \(\pm\) 884.89 \\
**ABC** & 1062.44 \(\pm\) 13.93 & 192.68 \(\pm\) 13.54 & 1341.52 \(\pm\) 24.42 & 0.19 \(\pm\) 0.01 & \(-\)8.39 \(\pm\) 3.26 & \(-\)1629.32 \(\pm\) 691.03 \\
**GA** & 1050.78 \(\pm\) 13.85 & 18248 \(\pm\) 13.65 & 1321.32 \(\pm\) 21.98 & 0.18 \(\pm\) 0.01 & \(-\)6.87 \(\pm\) 2.59 & \(-\)1269.14 \(\pm\) 538.49 \\
**RBCA** & 1016.56 \(\pm\) 14.43 & 159.19 \(\pm\) 10.04 & 1283.91 \(\pm\) 18.20 & 0.16 \(\pm\) 0.01 & \(-\)4.24 \(\pm\) 2.68 & \(-\)686.86 \(\pm\) 453.65 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of indicators after averaging the results of 10 random experimental groups. ## 5 Conclusions and Future Work

This paper proposes a swarm intelligence control design method based on double-layer deep reinforcement learning. By constructing a double-layer deep reinforcement learning framework, this method addresses the challenge of simultaneously optimizing energy efficiency and task completion. This challenge arises from the incomplete separation of individual and global optimization, as well as limited dynamic adaptability. This approach significantly enhances the swarm's comprehensive task effectiveness.