Double DQN Reinforcement Learning-Based Computational Offloading and Resource Allocation for MEC

In recent years, numerous Deep Reinforcement Learning (DRL) neural network models have been proposed to optimize computational offloading and resource allocation in Mobile Edge Computing (MEC). However, the diversity of computational tasks and the complexity of 5G networks pose significant challenges for current DRL algorithms apply to MEC scenarios. This research focuses on a single MEC server-multi-user scenario and develops a realistic small-scale MEC offloading system. In order to alleviate the problem of overestimation of action value in current Deep Q-learning Network (DQN), we propose a normalized model of Complex network based on Double DQN (DDQN) algorithm to determine the optimal computational offloading and resource allocation strategy. Simulation results demonstrate that DDQN outperforms conventional approaches such as fixed parameter policies and DQN regarding convergence speed, energy consumption and latency. This research showcases the potential of DDQN for achieving efficient optimization in MEC environments.