HALO: Hierarchical Auction-assisted Learning for Offloading in SAGIN

MULTI-AGENT REINFORCEMENT LEARNING FOR TASK OFFLOADING AND RESOURCE ALLOCATION IN MEC SYSTEMS

This paper addresses the joint task offloading and resource allocation problem in multi-user MEC systems and proposes a decentralized control framework based on Multi-Agent Reinforcement Learning (MARL), which achieves lower total system cost and faster convergence than the full-local, full-offload, and heuristic baselines.

DNN task computation offloading and resource allocation optimization strategy based on probabilistic early exit

Joint AoI and SWIPT-Aware Scheduling via Multi- Agent Deep Reinforcement Learning

This work investigates the joint optimization of Age of Information (AoI) and energy harvesting (EH) in wireless edge computing systems, where edge servers not only process IoT data but also act as wireless power suppliers via simultaneous wireless information and power transfer (SWIPT). Building upon the asynchronous model-free fractional multi-agent reinforcement learning framework and the Lyapunov drift-plus-penalty (DPP) concept, we design a fractional-based reward function for AoI and construct a virtual queue to enforce long-term energy stability under battery storage constraints. The overall reward is formulated as a weighted sum, capturing the trade-off between timeliness and energy sustainability, with update decisions, task offloading, and power splitting ratios as key control variables. Simulation results demonstrate that the developed multi-agent deep reinforcement learning approach achieves superior AoI–energy trade-offs compared to related baseline algorithms. These findings highlight the effectiveness of our framework in balancing information freshness and sustainable energy harvesting under resource-constrained edge environments.

TWO-AGENT REINFORCEMENT LEARNING FOR TASK OFFLOADING IN IOT-MEC NETWORKS

The rapid proliferation of Internet of Things (IoT) devices has placed unprecedented pressure on the network edge, where applications such as augmented reality, real-time analytics, and autonomous navigation demand low latency and tight energy budgets that traditional cloud-centric architectures cannot meet. Multi-access Edge Computing (MEC) addresses this gap by relocating computation closer to end users, but the core question of where and how each task should be executed remains open: rulebased and single-objective offloading strategies fail to simultaneously balance service latency, energy efficiency, and user experience under dynamic, large-scale conditions. In this paper we propose TARLOT (Two-Agent Reinforcement Learning Offloading Tasks), a cooperative framework for threetier IoT–MEC–Cloud environments. TARLOT decouples the offloading decision from the resourceallocation problem and assigns each to a dedicated Q-learning agent, so that the two subproblems are specialised independently while still being optimised jointly. The framework is evaluated on PureEdgeSim under heterogeneous IoT workloads, device densities ranging from 200 to 2,400, and diverse application profiles, and is compared against five widely-used baselines (Random, Round-Robin, Trade-Off, Pure-Edge, and Pure-Cloud). At 2,400 devices, TARLOT delivers an average service time of 1.1 s (against 4.3 s for Pure-Cloud), a Quality of Experience of 0.77 (against 0.22 for Pure-Cloud), a task-failure rate below 2 % (against nearly 14 % for Pure-Cloud), and a per-device energy consumption of only 3.6 W (against 11.2 W for Pure-Cloud) — roughly a 68 % reduction. Balanced CPU utilisation across the local, edge, and cloud tiers further confirms that TARLOT prevents resource bottlenecks, establishing it as a practical solution for next-generation large-scale IoT deployments.

Intelligent Task Scheduling in Edge-Cloud Environments Using Double Deep Q-Network Reinforcement Learning

In today’s world, with the emergence of IoT devices and critical latency applications, there has been an increased demand for intelligent resource management in diverse computing environments [5][10]. Collaborative computing is the integration of cloud and edge computing, while the issue of how to schedule each task for execution is still an open problem. Traditional heuristics like Round Robin and greedy latency reduction are incapable of adapting to the stochastic and non-stationary characteristics of practical workloads [4][14]. In this paper, an intelligent task scheduling mechanism based on Double Deep Q-Network (DDQN) reinforcement learning [2] has been proposed. The agent observes a four-dimensional state encoding task characteristics and selects binary offloading decisions, guided by a shaped reward signal encoding multiple performance objectives. Experimental evaluation on a heterogeneous synthetic benchmark demonstrates that the proposed DDQN scheduler reduces SLA violations by approximately 85% relative to Round Robin and 72% relative to the greedy baseline, while achieving superior energy efficiency. These results confirm that deep reinforcement learning [1][17][18] provides a principled foundation for adaptive resource management in next-generation edge-cloud systems.

Energy-Efficient Task Offloading and Load Balancing for Multi-UAV-Assisted Vehicular Networks

The rapid growth of Internet of Vehicles (IoV) applications has imposed strict requirements on low-latency and energy-efficient computing services. This letter investigates a multi-Uncrewed Aerial Vehicle (UAV)-assisted IoV system, where multiple Mobile Edge Computing (MEC)-enabled UAVs (MUs) collaboratively provide computing services for vehicular terminals (VTs). To improve service capability, we propose an energy-efficient task offloading and load balancing scheme that jointly considers vehicle mobility, task offloading and migration, and computing resource allocation to formulate an optimization problem. To solve this problem, a collective learning (CL)-enabled multi-agent reinforcement learning (CL-MARL) algorithm is proposed, where each agent learns optimal policies through centralized training and collective cooperative learning. Simulation results demonstrate that the proposed scheme outperforms benchmark strategies in terms of energy efficiency, task completion rate, and load balancing.