Abstract: With the rapid development of edge computing,task offloading has become a crucial strategy for enhancing system performance and resource utilization.Existing deep learning-based offloading methods face challenges in real-world applications,such as low sample efficiency and poor adaptability to new environments.To address these issues,a task offloading method based on meta-reinforcement learning(MRL-PPO) is proposed,aiming to effectively solve the efficient offloading of heterogeneous tasks in edge computing while minimizing task delay and energy consumption.A sequence-to-sequence(Seq2Seq) network with an attention mechanism is designed,modeling offloading tasks as a directed acyclic graph(DAG).The encoder encodes the offloading tasks,and the decoder outputs different offloading decisions based on the context vector,addressing the complexity of network training caused by varying task sequence dimensions.The attention mechanism allows the model to dynamically focus on key features of the offloading tasks,improving decision accuracy and efficiency.To optimize the performance of the PPO algorithm in complex environments,an intrinsic reward learning algorithm is introduced.Experimental results demonstrate that the proposed algorithm outperforms existing methods in different tasks,and can quickly adapt to new environments,effectively reducing delay and energy consumption during task processing.

Key words: Edge computing, Meta reinforcement learning, Task offloading, Seq2Seq network, Attention mechanism