arXiv AI October 16, 2026 By Justin M. Cheney, Clement N. Nyirenda

Deep Reinforcement Learning for HPC Job Scheduling: A Statistical Evaluation

The study evaluates six deep reinforcement learning (DRL) approaches for high‑performance computing (HPC) job scheduling, comparing Proximal Policy Optimization (PPO), Advantage Actor‑Critic (A2C), and Deep Q‑Network (DQN) with and without action masking. Across two real Slurm workload traces—one homogeneous and one heterogeneous—masked DRL treatments complete evaluation, while unmasked ones are truncated. On the heterogeneous trace, MaskablePPO outperforms baseline heuristics in all three metrics, achieving waiting times within 10% of the best heuristics and a 22% lower maximum bounded slowdown than Last‑Come, First‑Served (LCFS).

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