Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions

Researchers introduce CDQAC, an offline reinforcement learning algorithm that learns effective job scheduling policies from static, suboptimal datasets rather than requiring extensive online training interactions. The breakthrough demonstrates that scheduling performance depends primarily on state-action coverage rather than trajectory quality, enabling the algorithm to learn effectively from even simple random heuristics while requiring only 1-5% of original dataset size.

This research addresses a fundamental constraint in applying reinforcement learning to industrial scheduling problems: the computational cost of online training. Traditional online RL methods for Job Shop Scheduling and Flexible JSP require extensive interaction with simulated environments, making real-world deployment impractical. CDQAC bypasses this limitation by learning from pre-existing scheduling datasets, a significant advantage for industries with accumulated historical scheduling data but limited simulation resources.

The algorithmic innovation couples quantile-based critics with delayed policy updates to estimate return distributions for machine-operation pairs. However, the more surprising finding emerges from the empirical analysis: scheduling problems exhibit structural properties that favor broad behavioral coverage over trajectory quality. This insight contradicts conventional RL wisdom, which typically assumes data quality matters more than diversity. The dense reward structure aligned with makespan objectives and equal-length trajectories across different heuristics create conditions where a simple random scheduler generating diverse state-action pairs outperforms sophisticated genetic algorithm-generated policies.

For industrial applications, this work validates offline RL's viability in scheduling domains where simulation budgets constrain online learning. The sample efficiency—requiring only 1-5% of dataset size—makes the approach practical for implementation. Organizations maintaining scheduling logs can now leverage this data to train effective policies without rebuilding environments or acquiring new training interactions.

The broader implication extends beyond scheduling: understanding which problem structures favor coverage over quality could reshape how offline RL is applied across domains. Future research should investigate whether similar dynamics appear in other combinatorial optimization problems or remain specific to scheduling's structural characteristics.

• →CDQAC offline RL algorithm learns competitive scheduling policies from static, suboptimal datasets without requiring extensive online training
• →State-action coverage matters more than trajectory quality for scheduling, enabling simple random heuristics to outperform sophisticated methods
• →The approach requires only 1-5% of original dataset size while surpassing online and offline RL baselines on JSP and FJSP benchmarks
• →Dense reward structures and equal-length trajectories in scheduling create favorable conditions for learning from diverse behavioral data
• →Offline RL viability in scheduling could enable real-world deployment using accumulated historical scheduling logs