Offline Reinforcement Learning Algorithm Achieves Efficient Job Scheduling from Suboptimal Data
Researchers introduced CDQAC, an offline reinforcement learning algorithm that learns effective scheduling policies from static, suboptimal datasets without requiring extensive online training interactions. The method outperforms both traditional heuristics and state-of-the-art online RL baselines while requiring only 1-5% of the original dataset. This work demonstrates that scheduling problems can be solved efficiently through offline RL when datasets provide broad state-action coverage, even if individual trajectories are suboptimal.
A new offline reinforcement learning algorithm called Conservative Discrete Quantile Actor-Critic (CDQAC) has been developed to address the sample efficiency limitations of online RL approaches for job shop scheduling problems. The algorithm learns scheduling policies directly from static datasets of suboptimal solutions by coupling a quantile-based critic with delayed policy updates to estimate return distributions. Extensive experiments on both Job Shop Scheduling (JSP) and Flexible JSP (FJSP) benchmarks show that CDQAC consistently outperforms data-generating heuristics and surpasses existing offline and online RL baselines. A key finding is that offline RL performance in scheduling is governed primarily by state-action coverage breadth rather than individual trajectory quality, meaning that datasets from simple random heuristics with broader coverage can outperform those from stronger heuristics like Genetic Algorithms. This insight suggests that the dense rewards and equal-length trajectories characteristic of scheduling problems create favorable conditions for learning from diverse, suboptimal behaviors.
What's missing
The paper does not discuss computational complexity or wall-clock time comparisons between CDQAC and baseline methods, nor does it address potential scalability limitations to larger, more complex real-world scheduling instances beyond the benchmarks tested.
What different sources said
- arXiv cs.AICenter
Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions
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