MEC- Assisted Task offloading using Meta-Reinforcement Learning for B5G/6G Network

The rapid growth of mobile data and computing demands has strained resource-constrained edge devices, particularly in supporting IoT applications. Mobile Edge Computing (MEC) offloading alleviates these challenges by shifting complex tasks to edge-cloud servers, reducing computational burdens and enhancing efficiency. The integration of 5G and 6G technologies further enhances MEC by providing ultra-low latency and high-bandwidth connections. Despite the use of deep learning methods in task offloading, current approaches struggle with slow learning and adaptability issues. To address these challenges, we introduce a Deep Meta Reinforcement Learning based Offloading (Deep Meta-RL) Framework. Formulating the task offloading problem as a Markov Decision Process (MDP) enables us to leverage the Deep Meta-RL algorithm for precise offloading decisions, reinforcement learning’s decision-making, and meta-learning’s adaptability. Simulation results demonstrate that Deep Meta-RL significantly outperforms traditional DQN algorithms, achieving a 16.75% improvement in rewards and reducing latency by 20%.