SEROS: Shared exploration and reward optimization task scheduling strategy for multi-agent collaboration in edge computing networks

Abstract

The integration of mobile edge computing (MEC) into dynamic wireless ad hoc networks has intensified the challenges of computational resource scheduling, particularly under queue load constraints. Current research primarily focuses on global average metrics while neglecting fairness in resource allocation among devices across cross-time slot task scenarios. This oversight leads to significant disparities in the resources allocated to different devices, with some devices consistently lacking computational resources due to imbalanced scheduling. To address these limitations, we propose SEROS (Shared Exploration and Reward Optimization Strategy), a multi-agent reinforcement learning framework designed for cross-timeslot task scheduling in MEC environments. The method dynamically balances local optimization objectives with global collaboration through a weighted shared reward mechanism while enhancing training efficiency via hybrid sample trajectory utilization, enabling adaptive task offloading decisions. First, we construct a mobile edge computing model incorporating queue load constraints to address cross-timeslot task scheduling challenges, improving resource utilization for time-sensitive workloads through delayed optimization objectives. Second, we design a collaborative incentive mechanism based on global–local reward balancing and develop a sample trajectory-sharing scheme to accelerate policy convergence while preserving agent specialization. Simulation experiments validate the effectiveness of SEROS, demonstrating that compared with baseline methods, the proposed approach exhibits superior comprehensive performance in task completion rate improved by 7% and inter-device completion rate concentration enhanced by 40%, along with stability and task completion time.