ReSACO: A Meta Reinforcement Learning Method for Fast Offloading in Mobile Edge Computing

Mobile Edge Computing (MEC) has emerged as a promising paradigm for latency-sensitive and resource-intensive applications. However, due to the heterogeneous nature of applications, fluctuating workloads, and dynamic resource availability in MEC environments, making optimal offloading decisions remains a challenge. Traditional heuristics and methods based on artificial intelligence can be time consuming to train, which poses a challenge in continually evolving edge-cloud environments where rapid adaptation is critical. In this paper, we propose ReSACO, Reptile-based Soft Actor-Critic for Offloading, a meta-reinforcement learning framework that leverages the meta-learning algorithm with Soft Actor-Critic (SAC). By learning a generalizable policy across multiple scenarios, ReSACO can quickly adapt to new conditions with minimal retraining overhead, enabling robust offloading decisions in the face of unpredictable network fluctuations and resource constraints. We conducted extensive simulations using EdgeCloudSim to validate its performance. Experimental results show that ReSACO achieves up to 16% faster service time under heavy load while substantially reducing both network and VM-based failures. These findings demonstrate the effectiveness of combining meta-learning with entropy-regularized reinforcement learning to address the complexity and variability of edge-cloud environments, offering a scalable and efficient solution for ensuring low latency, high reliability, and fast adaptability in rapidly changing MEC deployments.