Agentic AI-Empowered Reliable Routing in Interference-Aware UAV Networks

Spatial-Aware Graph Attention Agentic AI for Embodied UAV Edge Computing Networks

The evolution of uncrewed aerial vehicles (UAVs) into embodied intelligent agents in the low-altitude economy is reshaping edge computing networks. However, the high mobility of UAVs induces severe topology dynamics, limiting the efficacy of traditional fully connected multiagent reinforcement learning because of dimensionality and credit assignment challenges. Furthermore, existing graph attention approaches neglect explicit communication boundary constraints, leading to mismatches between value evaluation and physical topology. To address these challenges, this paper proposes the spatial-aware graph attention multiagent twin delayed deep deterministic policy gradient (SAGA-MATD3) algorithm. By embedding a dynamic spatial masking mechanism based on the communication radius into the critic network, the proposed method enforces physical reachability constraints and attenuates extraneous noise. Simulation results demonstrate that SAGA-MATD3 significantly reduces service latency and improves fairness under an acceptable energy-consumption tradeoff, achieving a 37.1% improvement in convergence reward and enabling the self-organization of robust load-balanced mesh topologies.

AoI Minimization in Multi-UAV Edge Computing via Influence-Aware Heterogeneous Federated Multiagent Reinforcement Learning

Joint Trajectory and Spectrum Optimization for Anti-Jamming UAV Swarms: A DRL Approach

Reliable link maintenance is currently a critical bottleneck for unmanned aerial vehicle (UAV) swarm communications in complex electromagnetic environments where UAVs encounter both external malicious jamming and internal interference. Most recent studies have treated trajectory design and resource scheduling as decoupled problems or employed standard deep reinforcement learning methods to handle static spectral scenarios. However, these approaches lead to frequent link breakages and slow convergence when dealing with dynamic topologies and spatiotemporal interference. To tackle this challenge, we proposes a joint spatial-spectral adaptive coordination (JSSAC) framework and a deep recurrent attentionbased Q-network (DARQN) approach, utilizing a multi-head attention mechanism to intelligently aggregate heterogeneous neighbor features, thereby enhancing the swarm's adaptability to dynamic network topology. Moreover, considering that the spatial distribution of drones fundamentally determines the upper bound of the signal quality, we designed a communicationaware potential field mechanism that incorporates real-time signal-to-interference-plus-noise ratio feedback. Simulation results demonstrate that compared to DQN and DRQN algorithms, the proposed algorithm achieves transmission success rates of over 92%, representing improvements of 17% and 8% respectively, while also accelerating convergence speed.

Agentic GenAI-Enabled Resource Allocation for Low-Altitude Embodied Intelligence

Low-altitude embodied intelligence (LAEI) has emerged as a promising solution for operational efficiency and sustainability of the emerging low-altitude economy via perception–reasoning–action loops. The ground base stations with limited service coverages fail to achieve ubiquitous connectivity in widespread environments. The aerial agents embedded in flying bodies ensure pervasive intelligence across dynamic three-dimensional spaces. However, the joint optimization of flight trajectories and resource allocation for hierarchical UAV networks introduces large state and action spaces, posing significant challenges for real-time mission execution. In this paper, we propose an agentic Generative Artificial Intelligence (GenAI)-based LAEI framework. In the framework, a joint optimization problem is formulated to minimize long-term average energy consumption while ensuring task queue stability and satisfying spatial kinematic constraints. The Lyapunov optimization technique decomposes the long-term energy minimization problem into deterministic per-slot sub-problems with low computational complexity. A diffusion-based GenAI algorithm synthesizes optimal trajectories through an iterative denoising process, where the model-based resource allocation problem serves as guidance to accelerate convergence. Finally, extensive simulation experiments indicate that the proposed GenAI-enabled algorithm outperforms other baseline schemes, delivering minimized energy consumption and enhanced resource utilization in dynamic low-altitude embodied intelligence environments.

Multi-Agent Reinforcement Learning for SLA-Aware Network Slicing in UAV-Enabled MEC

A predictive multi-agent Reinforcement Learning (RL) framework that proactively maintains SLA stability in UAV-enabled MEC through coordinated trajectory control and computation resource allocation and designs an SLA-aware reward function that explicitly penalizes both violation probability and duration across slices.

TRUAV: Distributed Multi-Agent Reinforcement Learning for Trajectory Planning and Routing Enhancement in UAV-Aided IoT-Enabled VANETs

Unmanned aerial vehicles (UAVs) have emerged as a key enabler of next-generation Internet of Things (IoT) ecosystems, offering flexible aerial relaying to extend connectivity across dynamic vehicular ad hoc networks (VANETs) in smart city environments. However, conventional centralized approaches for UAV trajectory planning require continuous global network state aggregation, making them impractical under bandwidth and energy constraints typical of dense urban deployments. In this article, we present TRUAV, a distributed multi-agent reinforcement learning framework based on independent tabular Q-learning for joint UAV trajectory planning and routing enhancement in UAV-aided VANETs. Each UAV is equipped with a local Q-learning agent that operates purely on locally observable information, including vehicle density, packet queue states, and neighbor UAV positions, thereby eliminating the need for global state exchange. A potential-game-inspired reward design encourages spatial diversity and routing-aware UAV positioning among interacting agents while accounting for energy consumption. Numerical simulations over a large urban area with 200 mobile vehicles show that the proposed TRUAV framework achieves network coverage and packet delivery ratios comparable to centralized deep reinforcement learning methods, while also improving relay delay and energy efficiency. Finally, we discuss emerging challenges and future research directions for distributed multi-agent UAV-assisted IoT systems.