基于DRL的MEC任务卸载与资源调度算法

为提高多接入边缘计算（MEC）任务卸载效率，提出了一个任务卸载和异构资源调度的联合优化模型.考虑异构的通信资源和计算资源，联合最小化用户的设备能耗、任务执行时延和付费，并利用深度强化学习（DRL）算法对该模型求最优的任务卸载算法.仿真结果表明，该优化算法比银行家算法的设备能耗、时延和付费的综合指标提升了27.6%.

Tasks Offloading and Resource Scheduling Algorithm Based on Deep Reinforcement Learning in MEC

In order to improve the task offloading efficiency in multi-access edge computing (MEC), a joint optimization model for task offloading and heterogeneous resource scheduling was proposed, considering the heterogeneous communication resources and computing resources, jointly minimizing the energy consumption of user equipment, task execution delay, and the payment. A deep reinforcement learning method is adopted in the model to obtain the optimal offloading algorithm. Simulations show that the proposed algorithm improves the comprehensive indexes of equipment energy consumption, delay, and payment by 27.6%, compared to the Banker's algorithm.