Transformer-Assisted Deep Reinforcement Learning for Distributed Latency-Sensitive Task Offloading in Mobile Edge Computing

In this paper, we put forward a distributed, Transformer-assisted deep reinforcement learning scheme for latency-sensitive, mobility-aware and queue-aware task offloading in mobile edge computing systems. The proposed scheme adopts an attention-based transformer and deep reinforcement learning for minimizing the average cost and the task processing latency. Since the proposed scheme is distributed, there is no single point of failure in the system. Simulation results show that the proposed scheme could significantly outperform a number of baseline schemes in the literature.