Cost-Optimized Periodic DAG-Structured Task Offloading in Multi-User MEC Systems Using Reinforcement Learning

Reinforcement Learning (RL) has emerged as a promising solution for task offloading due to its adaptability to dynamic environments and ability to reduce online computational overhead. Thereby, this article explores RL for optimizing periodic Directed Acyclic Graph (DAG) task offloading in multi-user Mobile Edge Computing (MEC) systems, aiming to minimize overall costs, including user device energy consumption and server computational charges. A key contribution of this work is the explicit modeling of user competition for limited edge resources, where concurrent access leads to dynamic contention, significantly affecting offloading latency and energy usage. However, this optimization task faces two main challenges: the high dimensionality of task states and the large action space, both of which increase learning complexity. To address this, we propose a dynamic and distributed Proximal Policy Optimization (PPO)-based offloading framework. An encoder is employed to map DAG node features and structural information into a lower-dimensional representation, reducing computational overhead and improving learning efficiency. Additionally, we incorporate behavioral cloning to imitate greedy policies as the PPO agent’s initial behavior, effectively narrowing the action space and accelerating convergence. By combining representation learning and imitation-based initialization, our method enables the PPO agent to quickly adapt to environmental dynamics, leveraging both prior knowledge and real-time feedback to make informed offloading decisions. Simulation results confirm that our approach achieves rapid convergence and outperforms existing baselines in cost reduction, demonstrating its effectiveness for periodic task offloading in MEC scenarios. The source code and implementation details are available at: https://github.com/xiaolutihua/GAT/tree/master .