Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions
arXiv:2509.10303v2 Announce Type: replace Abstract: Online reinforcement learning (RL) approaches have demonstrated strong performance on Job Shop Scheduling (JSP) and Flexible JSP (FJSP) problems by learning scheduling policies through direct interaction with simulated environments. However, these methods often require extensive training interactions, limiting their sample efficiency and practical applicability. Motivated by this challenge, we introduce Conservative Discrete Quantile Actor-Critic (CDQAC), an offline RL algorithm that learns effective scheduling policies directly from static,
The continuous drive for more efficient AI training and deployment, especially in complex real-world applications like scheduling, is pushing research towards offline reinforcement learning solutions to overcome sample efficiency limitations.
This development allows AI systems to learn robust policies from existing datasets without costly real-time interaction, significantly expanding the practical applicability and deployment speed of AI in industrial and logistical operations.
AI models can now be trained effectively on historical data for critical operational tasks like scheduling, reducing the need for extensive and expensive simulation or live environment interaction during development.
- · Logistics and supply chain companies
- · Manufacturing sector
- · AI software developers
- · Robotics and automation industries
- · Companies reliant solely on online RL for operational optimization
Increased efficiency and cost reduction in complex scheduling and operational management across various industries.
Faster and broader adoption of AI-driven automation in environments previously deemed too expensive or risky for online reinforcement learning.
The acceleration of fully autonomous operational systems as AI can more readily learn and adapt from vast datasets of past operations.
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Read at arXiv cs.LG