This contribution explores the flocking dynamic behavior of multi-agent networks with limited communication resources, where there are both cooperative and antagonistic relationships among agents. Taking into account the shortage of communication resources, a distributed control protocol with edge-asynchronous communications is designed, in which only part of the communication links are awakened at each time instant to perform information transmission. A product approach of super-stochastic matrices is utilized to rigorously check the convergence of the dynamic model. With this method, algebraic conditions on cooperative and antagonistic relationships among agents are established so that the entire multi-agent network emerges with flocking dynamics. At last, theoretical results are tested and verified through computer simulations.

Unmanned Aerial Vehicles (UAVs) can be deployed as aerial wireless base stations which dynamically cover the wireless communication networks for Ground Users (GUs). The most challenging problem is how to control multi-UAVs to achieve on-demand coverage of wireless communication networks while maintaining connectivity among them. In this paper, the cooperative trajectory optimization of UAVs is studied to maximize the communication efficiency in the dynamic deployment of UAVs for emergency communication scenarios. We transform the problem into a Markov game problem and propose a distributed trajectory optimization algorithm, Double-Stream Attention multi-agent Actor-Critic (DSAAC), based on Multi-Agent Deep Reinforcement Learning (MADRL). The throughput, safety distance, and power consumption of UAVs are comprehensively taken into account for designing a practical reward function. For complex emergency communication scenarios, we design a double data stream network structure that provides a capacity for the Actor network to process state changes. Thus, UAVs can sense the movement trends of the GUs as well as other UAVs. To establish effective cooperation strategies for UAVs, we develop a hierarchical multi-head attention encoder in the Critic network. This encoder can reduce the redundant information through the attention mechanism, which resolves the problem of the curse of dimensionality as the number of both UAVs and GUs increases. We construct a simulation environment for emergency networks with multi-UAVs and compare the effects of the different numbers of GUs and UAVs on algorithms. The DSAAC algorithm improves communication efficiency by 56.7%, throughput by 71.2%, energy saving by 19.8%, and reduces the number of crashes by 57.7%.

Large-scale post-disaster target search missions pose significant challenges for Unmanned Aerial Vehicles (UAVs) due to the complex and unpredictable environments. Heterogeneous swarms that combine the benefits of multiple UAVs can enhance adaptability and search efficiency. Therefore, a leader-follower strategy is employed for heterogeneous multi-UAVs in this paper, where fixed-wing UAVs serve as leaders to provide dynamic relay communication coverage, while multi-rotor UAVs act as followers for multi-target search tasks. We construct a cooperative graph to model relationships among neighboring UAVs and design importance indices for both cooperation and search targets to optimize this graph, streamlining state observation vectors. We then introduce Graph Attention Networks (GAT) to aggregate information from adjacent nodes in the cooperative graph. Additionally, we employ Multi-Agent Reinforcement Learning (MARL) to optimize flight trajectories separately for fixed-wing and multi-rotor UAVs, enhancing cooperation efficiency in multi-target searches. Based on the above methods, we propose a Graph Attention Network multi-agent Actor-Critic (GATAC) algorithm, which effectively addresses the dimension explosion problem that arises as the number of agents increases. Simulation results demonstrate that the GATAC algorithm outperforms existing methods in terms of target search, energy consumption and broken link duration. Finally, we establish a multi-UAV cooperative validation platform, and experimental trajectory plots confirm the stable performance of the GATAC algorithm in real-world environments.

Highly flexible Automated Guided Vehicles (AGVs) are interconnected via Industrial Wireless Control Networks (IWCNs) in Multi-access Edge Computing (MEC)-assisted smart factories. The MEC alleviates the lack of computational resources in AGV systems through task offloading. However, IWCNs with limited communication resources struggle to support the highly concurrent offloading of AGVs. In the distributed AGV systems with multi-MEC servers, AGV mobility leads to uneven distribution across MEC server areas, potentially resulting in severe competition for communication resources. Therefore, in this paper, we design a Transferable joint Task Offloading and Multi-Channel Access (T2OMCA) algorithm based on multi-agent deep reinforcement learning. Specifically, AGV observations are modelled as graphs, in which edge relationships are learned through Transformer. This enables AGVs to utilize domain information to collaborate and alleviate concurrent offloading. Moreover, the T2OMCA algorithm converts network input into fixed embeddings to accommodate varying numbers of AGVs. Finally, to encourage exploration in the high-dimensional action space, the T2OMCA algorithm introduces a noisy network and a prioritized experience replay mechanism. Extensive simulations show that the T2OMCA algorithm outperforms existing algorithms in terms of average completion rate, processing delay, and access conflict rate under time-varying AGV topologies.

Unmanned Aerial Vehicles (UAVs) possess high maneuverability and wide viewing angles, rendering them ideal as flying base stations for resource-constrained Internet of Things (IoT) sensors. For real-time information acquisition and sustainable energy support for numerous IoT devices, an appropriate number of UAVs are required to efficiently deployed for data and energy transfer tasks. However, existing methods face challenges in minimizing the average Age of Information (AoI) due to the complex coupling between trajectory planning and transmission scheduling decisions and the need for efficient coordination in resource-constrained UAV networks. These domain-specific challenges require specialized solutions that effectively balance information freshness and energy efficiency. To address these challenges, we first decompose the scheduling problem into two subproblems: trajectory optimization and transmission optimization. Based on this decomposition, we propose a Hierarchical Trajectory Optimization and Transmission Scheduling (HTOTS) algorithm based on hierarchical reinforcement learning. The HTOTS algorithm employs Deep Reinforcement Learning to sense environmental states in real-time and dynamically adjusts UAV flight trajectories and information acquisition, ensuring an effective balance between data and energy transfer. These subproblems are solved alternately through hierarchical reinforcement learning, which significantly reduces the complexity of each subproblem and improves convergence efficiency. Simulation results show that the proposed HTOTS algorithm outperforms existing methods in terms of average AoI and energy efficiency for various network scales and energy constraints.