Multi-Latency Multi-Agent Task Offloading Strategy for Smart Education Platforms Based on Edge Computing

In smart education platforms, large-scale real-time interactive tasks usually involve complex subtask dependencies. The transmission, computation, and queuing delays generated by different subtasks are coupled with each other, making it difficult for traditional task offloading methods to achieve efficient scheduling. Meanwhile, multi-user concurrent requests further intensify edge computing resource competition. To address these challenges, this study proposes a task offloading scheme based on mobile edge computing (MEC) and multi-agent deep deterministic policy gradient (MADDPG). First, a smart education platform architecture including cloud nodes, edge nodes, and terminal devices is constructed to achieve cloud-edge-device collaborative computing. Second, addressing the shortcomings of existing task offloading methods that primarily focus on single latency metrics, ignore task dependencies, and lack flexibility in discrete offloading decisions, a MADDPG model is designed that integrates multi-latency perception, directed acyclic graph (DAG) task constraints, and continuous offloading ratio decision-making. This model incorporates task state, network state, and edge resource state into the agent decision-making process, achieving dynamic task offloading and resource optimization under multi-objective conditions. To validate the effectiveness of the proposed method, 19,200 task instances are generated using a DAG generator, and comparative experiments are conducted against ACO, GWO, DDPG, PPO, DQN, and Greedy Offloading methods. The results demonstrate that the proposed method outperforms all comparison approaches in terms of task completion time, energy consumption, task completion rate, and network adaptability. When the number of tasks reaches 50, the task completion time is reduced to 16.2 s, and the total energy consumption decreases to 0.95 J. When the number of users increases to 60, the task completion rate remains at 96.8%. The proposed scheme effectively addresses edge computing offloading problems under multi-task, multi-user, and multi-latency coupling conditions, providing a solution for intelligent resource scheduling in complex edge environments.