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Task Offloading in Multi-UAV Edge Computing Using an Improved Multi- Agent Proximal Policy Optimization Approach

Year: 2026
Type: preprint
Abstract: With the rapid growth of UAV-enabled applications in inspection, logistics, and emergency communication, onboard computation and energy resources have become key bottlenecks for large-scale UAV swarms. Mobile edge computing (MEC) provides a practical way to relieve this pressure by offloading computation-intensive tasks to nearby edge and cloud servers. However, task offloading in multi-UAV environments remains challenging because of partial observability, time-varying wireless conditions, resource contention, and the need for coordinated decisions among multiple agents. To address these issues, this paper studies an energy-efficient task offloading problem for a heterogeneous UAV swarm and develops a three-layer computing architecture consisting of a UAV layer, an edge server layer, and a cloud layer. Based on this architecture, we propose an MAPPO-based framework, called SS-MAPPO, which combines a top-K sparse attention mechanism with a Gaussian-based latent policy representation. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), where each UAV makes offloading decisions according to its local task attributes, queue states, and observable server conditions. Simulation results show that SS-MAPPO reduces energy consumption, accelerates convergence, and improves decision stability compared with the evaluated baselines under the tested settings. These results indicate that the proposed framework is a promising approach for energy-aware collaborative offloading in dynamic multi-UAV edge computing environments.
DOI: 10.21203/rs.3.rs-10982608/v1
Language: English
Open Access status: green
OpenAlex ID: W7225656461
Cites: 29
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Related to: 10