Offline Reinforcement Learning Algorithm Achieves Efficient Job Scheduling from Suboptimal Data
Researchers have introduced CDQAC, an offline reinforcement learning algorithm that learns high-quality job shop scheduling policies from static, suboptimal datasets — including data generated by random heuristics. Unlike online RL methods that require extensive environment interaction, CDQAC achieves competitive performance using only 1–5% of the original training data. The findings suggest that broad state-action coverage matters more than data quality for offline RL in scheduling, challenging assumptions about the need for expert demonstrations.
A new offline reinforcement learning algorithm called Conservative Discrete Quantile Actor-Critic (CDQAC) has been proposed for solving Job Shop Scheduling (JSP) and Flexible JSP (FJSP) problems without requiring live environment interaction. The method combines a quantile-based critic with delayed policy updates to estimate return distributions over machine-operation pairs, enabling learning directly from fixed, suboptimal datasets. Experiments on standard JSP and FJSP benchmarks show CDQAC consistently outperforms both the heuristics used to generate its training data and state-of-the-art online and offline RL baselines. Notably, the algorithm achieves strong results using just 1–5% of the full dataset, indicating high sample efficiency. A key finding is that datasets generated by simple random heuristics — which provide broader state-action coverage — can outperform those generated by stronger heuristics like Genetic Algorithms, suggesting coverage diversity is the primary driver of offline RL performance in scheduling contexts. The authors attribute this to scheduling's dense reward signal aligned with makespan minimization and the equal-length trajectories produced across different heuristics, which together facilitate learning from diverse behavioral data.
What's missing
The paper is a preprint on arXiv and has not yet undergone formal peer review. Key open questions include how CDQAC scales to very large or real-world industrial scheduling instances, whether the coverage-over-quality finding generalizes to scheduling domains with sparser rewards or variable-length trajectories, and how the algorithm performs when deployed in dynamic environments where problem parameters change over time.
What different sources said
- arXiv cs.AICenter
Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions
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