SkySched: A Hierarchical and Scalable Reinforcement Learning Framework for Multi-UAV Vehicular Edge Computing Network

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.

Efficient dynamic cooperative deployment and task scheduling in multi-UAV-assisted MEC for dense dynamic environments.

With the rapid development of the Internet of Things (IoT) and mobile computing, edge computing has emerged as a promising paradigm for providing low-latency and energy-efficient services. However, in some extremely computation-intensive scenarios, conventional terrestrial edge computing may fail due to the insufficient computing capability of ground base stations. Fortunately, multi-UAV-assisted edge computing offers a promising solution to this challenge. Nevertheless, existing methods often struggle to provide efficient horizontal cooperative deployment for multiple UAVs with low computational overhead. To address this issue, this paper considers user randomness and inter-UAV collaboration, and proposes a low-complexity yet highly adaptive approach for cooperative deployment and task-scheduling optimization in multi-UAV-assisted edge computing systems. Specifically, we formulate the problem as a stochastic optimization problem that minimizes the energy consumption of ground users while ensuring UAV battery endurance and overall system performance. We then propose a dynamic cooperative deployment and task scheduling (DCDTS) algorithm that integrates K-means clustering with the Lyapunov optimization framework. Through Lyapunov optimization, the original dynamic optimization problem is transformed into a deterministic problem and further decomposed into multiple subproblems that can be solved in parallel. K-means is exploited to enable cooperative UAV deployment and user offloading decisions, while non-convex optimization and nonlinear programming are employed to solve the task-scheduling and resource-allocation subproblem. Extensive parameter analysis and comparative experiments demonstrate that the proposed dynamic cooperative deployment algorithm can effectively reduce user energy consumption while maintaining UAV energy constraints and system performance.

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

Hierarchical-Projected Scheduling for Mission-Critical Vehicular Edge Computing

An Intelligent DRL‐Based Framework for Reliable UAV Swarm Communications in Dynamic Environments

Unmanned Aerial Vehicle (UAV) networks are increasingly deployed in dynamic environments where reliable and low‐latency communication is critical. However, high mobility, intermittent connectivity, spectrum limitations, and energy constraints make conventional static communication protocols inadequate for maintaining stable, dependable links. To address these challenges, this paper proposes TRC‐MAPPO, a topology‐aware reliability‐constrained multi‐agent deep reinforcement learning framework for adaptive UAV swarm communication. The routing problem is formulated as a constrained decision‐making task that jointly considers packet delivery reliability, end‐to‐end delay, link stability, bandwidth usage, and energy consumption. Unlike single‐agent DRL baselines, TRC‐MAPPO represents the swarm as a dynamic communication graph and combines local relay selection with centralized training, enabling cooperative routing decisions under time‐varying network conditions. Simulations are conducted in a controlled, dynamic UAV environment, with the same mobility and traffic settings used for all methods. The proposed framework is compared with DQN and PPO over different swarm densities. Results show that TRC‐MAPPO achieves a higher packet delivery ratio, lower end‐to‐end delay, and more efficient energy behaviour, particularly in dense deployments. These findings indicate that topology‐aware cooperative learning can provide a scalable and practical solution for reliable UAV communication in future intelligent aerial and 6G‐enabled networks.