Optimization of edge computing task offloading based on multi-agent game

In a multi-access edge computing environment, optimizing task offloading under various constraints is a complex challenge. Traditional methods often neglect constraint relationships, leading to uneven resource allocation and suboptimal system performance. Additionally, these methods struggle to adapt to dynamic demands in edge computing. This paper introduces a novel distributed task offloading algorithm based on multi-agent deep reinforcement learning. This approach coordinates multiple agents in making task offloading decisions, aiming to achieve optimized task allocation while respecting constraints, thereby enhancing system performance and reducing latency and energy consumption. Simulation experiments in edge computing scenarios validate the effectiveness and stability of the proposed method. Results demonstrate that the multi-agent deep reinforcement learning approach outperforms traditional methods significantly. It excels in reducing task completion time and lowering terminal device energy consumption, affirming its effectiveness. This research offers a fresh perspective on task offloading strategies in edge computing, addressing limitations in traditional methods and providing a foundation for further exploration in this domain.