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arXiv AI By Jayakrishnan K. Vasudevan (Rosenheim University of Applied Sciences), Jonathan Hoss (Rosenheim University of Applied Sciences), Noah Klarmann (Rosenheim University of Applied Sciences)

Curriculum Learning with GNN-based Reinforcement Learning for Job Shop Scheduling

Read the original on arXiv AI →

The paper investigates curriculum learning for graph neural network-based reinforcement learning applied to the job shop scheduling problem. By training policies on progressively larger instances (from 20×20 up to 30×30), the authors demonstrate that this approach reduces training time and improves performance compared to single-size training. Evaluation on unseen instances from 8×8 to 30×30 shows that curriculum learning lowers the mean optimality gap by about 8–9 percentage points and saves roughly 50 hours of training time at the largest target size.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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