Multi-Scenario Task Offloading Algorithm Based on Meta-Reinforcement Learning

Aiming at the problem of task offloading in multiaccess edge computing (MEC) scenarios, this paper proposes a meta-reinforcement learning (Meta-RL)-based computational task offloading method. The algorithm adopts a two-layer architecture: the inner layer models the task offloading process as a Markov Decision Process (MDP), designs a reward function based on task latency and energy consumption, and designs a task offloading algorithm based on Proximal Policy Optimization (PPO) to make offloading decisions for each task in a single scenario and optimize the offloading performance within the scenario. The outer layer introduces the Meta-RL mechanism to optimize the initial parameters of the inner-layer neural network and learns multiple MDPs based on gradient descent to generate neural network parameters that can be applied to the intelligence of each scenario, so that the proposed algorithm can adapt to the offloading scenarios quickly. The proposed algorithm can quickly adapt to each offloading scenario. Simulation results show that the proposed algorithm improves the average cost by 14.7 % and 20.51 % compared with PPO and DDPG.