# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 6 Conclusions  
In the paper, we address the critical challenges in emergency communication network (ECN) deployment by proposing a deep reinforcement learning (DRL) framework with a fully convolutional value network architecture. Emergency communication networks face significant complexities in disaster scenarios, including dynamic environmental changes, heterogeneous infrastructure availability, and the urgent need for rapid deployment. Traditional rule-based and heuristic approaches struggle to adapt to these dynamic conditions, often resulting in suboptimal resource allocation and delayed response times, while manual deployment is constrained by limited expertise and prolonged decision-making processes. The proposed architecture leverages end-to-end feature extraction to overcome the \"curse of dimensionality\" inherent in topology planning, enabling multi-dimensional spatial decision-making that accounts for factors such as network resilience, resource constraints, and coverage requirements. By integrating deep value networks, the framework dynamically evaluates potential hub placements in real-time, prioritizing critical areas while balancing trade-offs between connectivity, latency, and energy efficiency.  
Figure 7: The impact of (**a**) experience replay capacities, (**b**) learning rate, and (**c**) update interval on total rewards.  
Experimental results demonstrate that the proposed method is highly effective in planning emergency communication hub elements, achieving faster convergence speed and more robust performance in complex environments compared to traditional strategies. This approach aligns with the emerging trends in deep reinforcement learning-driven network optimization, which has shown promise in addressing challenges such as resource scheduling in aerial-assisted networks and resilient routing in hybrid sensor systems. By overcoming critical limitations in scalability, adaptability, and operational efficiency under high-stress disaster conditions, this research contributes to the deployment of emergency communication networks. Furthermore, with appropriate adjustments to the establishment function and input states, the method can be adapted to other scenarios, such as logistics distribution center site selection and urban infrastructure planning, highlighting its strong generalization capability and broad applicability.  
Conceptualization, B.H., Y.L., R.Y., C.Y. and H.M.; methodology, B.H.; software, Y.S.; validation, C.Y. and Y.T.; formal analysis, Y.W.; investigation, Y.Z.; resources, B.H. and Y.L.; data curation, B.H. and Y.L.; writing--original draft preparation, B.H. and Y.L.; writing--review and editing, H.M., R.Y. and C.Y.; visualization, Y.W. and Y.Z.; supervision, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by unit 32125. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.