Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions
📰 ArXiv cs.AI
Offline reinforcement learning can learn effective scheduling policies through random solutions, improving sample efficiency and practical applicability
Action Steps
- Apply offline reinforcement learning to scheduling problems using random solutions
- Configure Conservative Discrete Quantile Actor algorithms for improved sample efficiency
- Test the performance of learned scheduling policies on Job Shop Scheduling and Flexible JSP problems
- Compare the results with online reinforcement learning approaches to evaluate the benefits of offline learning
- Run simulations to evaluate the effectiveness of the learned policies in various scenarios
Who Needs to Know This
Researchers and practitioners working on reinforcement learning, scheduling, and optimization problems can benefit from this approach to improve the efficiency and effectiveness of their solutions
Key Insight
💡 Offline reinforcement learning can generalize beyond suboptimal solutions, learning effective scheduling policies through random solutions
Share This
🚀 Offline RL learns effective scheduling policies through random solutions! 💡 Improving sample efficiency and practical applicability #RL #Scheduling #Optimization
Key Takeaways
Offline reinforcement learning can learn effective scheduling policies through random solutions, improving sample efficiency and practical applicability
Full Article
Title: Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions
Abstract:
arXiv:2509.10303v2 Announce Type: replace-cross 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 Acto
Abstract:
arXiv:2509.10303v2 Announce Type: replace-cross 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 Acto
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