Deep Reinforcement Learning-Driven Adaptive Task Offloading and Resource Allocation for UAV-Assisted Mobile Edge Computing

Mobile edge computing (MEC) has been widely applied in various Internet of Things (IoT) and mobile applications. In the event of network failures that prevent endusers (EUs) from establishing connections with MEC servers (MECSs), unmanned aerial vehicles (UAVs) can be deployed to provide computing services and data transmission. In UAV- assisted MEC systems, task offloading and resource allocation are two critical issues that are closely related and mutually influential, jointly determining the effectiveness of the system. Moreover, due to the complexity of UAV-assisted MEC systems, traditional methods cannot effectively address these two problems, and most existed research fails to fully consider them. In this paper, we consider a scenario of UAV-assisted MEC and propose an innovative deep reinforcement learning (DRL) approach that specifically targets the distinct characteristics of task offloading and resource allocation problems. We utilize the Deep Deterministic Policy Gradient (DDPG) method to solve the task offloading problem and introduce Long Short-Term Memory (LSTM) into DDPG to propose an LSTM-based DDPG (LDPG) method for resource allocation. By allowing partial parameter sharing between these two modules and alternating training, we effectively address the task offloading and resource allocation problems, maximizing system stability and minimizing energy consumption and service latency. Experimental results demonstrate that the performance of the DDPG-LDPG hybrid algorithm surpasses existing task offloading and resource allocation schemes, while exhibiting good scalability.