Deep Reinforcement Learning Approach for UAV-Assisted Mobile Edge Computing Networks
Sangwon Hwang, Juseong Park, Hoon Lee, Mintae Kim, Inkyu Lee
Abstract
This paper studies a deep reinforcement learning (DRL) approach for the unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks where a UAV-mounted server offloads computation tasks of mobile users (MUs). We aim at minimizing the energy consumption of the MUs by adjusting UAV mobility, UAV-MU association, computation resource allocation, and task offloading rules. This requires an online and joint optimization of different types of variables constructing heterogeneous solution spaces. To realize real-time optimization strategies, we propose an online DRL method based on the twin-delayed deep deterministic policy gradient (TD3) framework. The joint optimization of heterogeneous action variables is tackled by a novel actor neural network that partitions the high-dimensional action set into several solution spaces. In addition, the proposed TD3 framework achieves adaptability to new task offloading requests through our proposed training and execution strategy. Numerical results verify the effectiveness of the proposed DRL architecture over benchmark schemes.