Priority-Aware Attention Meets Generative Flow Networks for Global Fixed-Priority Assignment

Ensuring timely task execution in multiprocessor systems under Global Fixed-Priority Scheduling (GFPS) remains a significant challenge. While traditional heuristic methods often struggle to derive feasible priority assignments for complex task sets, existing Deep Reinforcement Learning (DRL) approaches—though more powerful—suffer from two key limitations: (1) they fail to adequately capture relative priorities among high-priority tasks during encoding, and (2) their convergence to suboptimal policies due to sensitivity to local reward signals, an inherent drawback of reinforcement learning frameworks. To overcome these issues, we propose PAGFN, a novel learning-based framework that combines Priority-Aware Attention with Generative Flow Networks. We formulate the priority assignment problem as a Markov Decision Process (MDP) and employ an encoder-decoder architecture with auto-regressive decoding. Our framework introduces a priority-aware module to explicitly model task dependencies, enhancing assignment quality. Additionally, to mitigate sparse rewards, we integrate expert pretraining and prioritized experience replay, enabling diverse policy exploration without relying on intricate reward shaping. Experimental results on synthetic tasks show that PAGFN outperforms heuristic and learning-based baselines, particularly in scheduling previously unschedulable tasks, validating its effectiveness and scalability.