Real-time Optimal Resource Allocation in Multiuser Mobile Edge Computing in Digital Twin Applications with Deep Reinforcement Learning : (Invited Paper)

We investigate the optimal resource allocation of mobile edge computing (MEC) with multiple Internet-of-Thing (IoT) devices in digital twin applications. Based on Markov decision process and model-free deep reinforcement learning (DRL) approach, we propose double deep RL-based online computation offloading method to implement the deep neural network that learns from interactions to solve the computation offloading and transmission latency problem in the dynamic MEC-aided IoT environments. In particular, we design an adaptive method for continuous action-state spaces to minimize the completion time and total energy consumption of the IoT devices for stochastic computation offloading task. The proposed real-time Lyapunov optimization and DRL algorithms achieve a low computational complexity and optimal processing time. Simulation results demonstrate that the proposed method can achieve near-optimal control performance with an enhanced energy consumption and significantly minimize the computation time.