A-MARL: Agile Multi-Agent Reinforcement Learning for Soft Real-Time Task Scheduling in Edge Computing

Modern soft real-time applications (SRTAs) impose heavy computational demands on embedded devices. While offloading workloads to Edge Computing (EC) resources is attractive, task scheduling remains challenging due to strict timing constraints, a vast search space, multiple conflicting objectives, and highly dynamic environments. Conventional heuristic and meta-heuristic algorithms struggle to adapt to these conditions. Although reinforcement learning (RL) suits dynamic environments, single-agent RL converges slowly on mediumand large-scale problems due to enormous action spaces and excessive exploration. We present Agile Multi-Agent Reinforcement Learning (A-MARL), which enhances Multi-Agent PPO by replacing conventional exploration with entropy-guided rule-based exploration. When policy entropy is high, A-MARL employs Shortest Processing Time (SPT) to guide exploration toward promising action space regions. This adaptive mechanism accelerates convergence and delivers schedules better suited for SRTAs in EC environments. Experiments on representative scenarios show A-MARL consistently outperforms state-of-the-art baselines across all evaluated metrics, demonstrating its effectiveness for SRTA task scheduling in EC.