# 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 research in this paper not only provides a new solution for UAV swarm intelligence control, but also offers new research ideas and tools for optimization in the field of swarm intelligence control. In future work, further improvements will be made to the double-layer deep reinforcement learning-based swarm intelligence control design method, focusing on four main aspects: (1) comparing the simulation results of the swarm intelligence control system with the experimental results in real scenarios; (2) extending the swarm intelligence control algorithm to three-dimensional spaces, among other areas; (3) using simpler architectures (e.g., attention-based models) instead of multiple deep networks (CNN, LSTM, and DNN) to reduce computational costs; and (4) applying swarm intelligent control algorithm to more fields, such as unmanned transportation and industrial intelligence, etc. Conceptualization, G.Y. ; methodology, X.Y. ; software, G.H. and R.Z. ; validation, X.Y. ; formal analysis, L.H. ; investigation, X.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data sharing does not apply to this article. The authors declare no conflicts of interest. ## References

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