In disaster scenarios, establishing robust emergency communication networks is critical, and unmanned aerial vehicles (UAVs) offer a promising solution to rapidly restore connectivity. However, organizing UAVs to form multi-hop networks in large-scale dynamic environments presents significant challenges, including limitations in algorithmic scalability and the vast exploration space required for coordinated decision-making. To address these issues, we propose MRLMN, a novel framework that integrates multi-agent reinforcement learning (MARL) and large language models (LLMs) to jointly optimize UAV agents toward achieving optimal networking performance. The framework incorporates a grouping strategy with reward decomposition to enhance algorithmic scalability and balance decision-making across UAVs. In addition, behavioral constraints are applied to selected key UAVs to improve the robustness of the network. Furthermore, the framework integrates LLM agents, leveraging knowledge distillation to transfer their high-level decision-making capabilities to MARL agents. This enhances both the efficiency of exploration and the overall training process. In the distillation module, a Hungarian algorithm-based matching scheme is applied to align the decision outputs of the LLM and MARL agents and define the distillation loss. Extensive simulation results validate the effectiveness of our approach, demonstrating significant improvements in network performance over the MAPPO baseline and other comparison methods, including enhanced coverage and communication quality.

Sensing noise and complex decision-making pose critical challenges to active search for lost persons amid disasters, impeding efficient rescue efforts. We introduce CARL, a collaborative altitude-adaptive reinforcement learning framework for UAV swarms. CARL integrates confidence-informed assessment with Sparse Bayesian Learning to diminish the noise impact on sensor performance, and an altitude-adaptive planner for collaborative active search strategy. Simulation experiments with up to 50 targets and 10 UAVs demonstrate CARL’s superior performance compared to baseline methods in lost person active search scenarios.

Utilizing unmanned aerial vehicles (UAVs) as mobile access points can assist urban communication systems in establishing emergency networks in disaster scenarios. In this paper, to organize UAVs in large-scale environments for networking purposes, we propose a multi-agent reinforcement learning (MARL) model, in which the design of a selective parameter sharing mechanism and a grouping strategy enhances the model’s scalability. Furthermore, the model adopts a reward mechanism based on intrinsic motivation, using the Large Language Model (LLM), to accelerate the optimization process. Numerical results demonstrate that this algorithm outperforms existing alternatives.

Utilizing unmanned aerial vehicles (UAVs) as mobile access points can assist urban communication systems in establishing emergency networks in disaster scenarios. However, in large-scale dynamic environments, the extensive exploration space makes effective collaboration among a large number of UAVs challenging. In this paper, to schedule the deployment of UAVs for networking purposes, we propose a novel approach, MAEN, using multi-agent reinforcement learning. The grouping and information sharing mechanisms in MAEN enable the algorithm to easily scale up the number of UAVs to dozens and address the issue of strategy equilibrium. Additionally, a reward decomposition module is designed to handle coordination and task allocation among UAVs. Experimental results demonstrate that the algorithm outperforms existing algorithms in terms of ground device coverage and communication quality.