Coupling-Aware Edge-Cloud Computing for Multi-UAV Cooperative Sensing via Evolutionary Game-Based Deep Reinforcement Learning

作者：Yi-Shan Chen, Tong Zhou, Qing Xu, Guanjie Cheng · 发表于：IEEE Transactions on Cloud Computing · 年份：2026 · DOI：10.1109/tcc.2026.3697781

For multi-UAV cooperative perception applications in Intelligent Transportation Systems (ITS), edge-cloud collaborative computing is severely constrained by co-channel interference and limited edge computing resources, which induce strong network-level coupling among UAV offloading decisions. In distributed settings where global information is unavailable, this coupling exacerbates environmental non-stationarity during multi-agent training, leading to slow convergence and policy oscillations. To address these issues, we propose a distributed decision-making framework based on evolutionary game theory (EGT). Specifically, we first formulate the multi-UAV task offloading problem as an evolutionary game, capturing the payoff interdependence caused by wireless interference and edge resource contention. Subsequently, we apply replicator dynamics to analyze the evolution of offloading strategies, derive the interior evolutionary equilibrium, and establish its asymptotic stability. Finally, we develop an EGT-enhanced multi-agent deep reinforcement learning (EGT-MADQN) algorithm, which incorporates relative fitness derived from coupling-affected payoffs into strategy fusion through adaptive step-size modulation to to facilitate stable policy adaptation during training. Extensive simulation results demonstrate that the proposed method consistently outperforms baseline approaches in minimizing total cost and task latency while achieving superior convergence stability. It also exhibits ...