Multi-Scenario Task Scheduling Based on Heterogeneous-Agent Reinforcement Learning in Space-Air-Ground Integrated Network

With the advantage of robust resilience, large capacity, and strong adaptability, space-air-ground integrated network (SAGIN) can simultaneously support various task scenarios involving heterogeneous networks and diverse task demands. In this integrated network, the constraints from limited resources and dynamic environment pose challenges in fulfilling concurrent demands, and improper task scheduling strategy can lead to network resource wastage and task dissatisfaction. In this paper, we propose an adaptive solution for multiscenario joint scheduling in SAGIN. We construct a comprehensive task scheduling frame-work and propose the task relevance matrix for in-depth analysis. To achieve the goal of improving network resource utilization and task satisfaction, we formulate the joint optimization problem as a cooperative Markov game and propose a novel multi-scenario task scheduling algorithm based on heterogeneous-agent proximal policy optimization (HAPPO). Simulation results show that the proposed algorithm can achieve better performance by effectively improving resource utilization and reducing task delay, compared with two state-of-the-art multi-agent reinforcement learning algorithms and the random baseline.