{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/offline-reinforcement-learning-for-learning","title":"Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling","arxiv_id":"2409.10589","date":"2024-09-16","proceeding":null,"authors":["Jesse van Remmerden","Zaharah Bukhsh","Yingqian Zhang"],"abstract":"The Job Shop Scheduling Problem (JSSP) is a complex combinatorial optimization problem. While online Reinforcement Learning (RL) has shown promise by quickly finding acceptable solutions for JSSP, it faces key limitations: it requires extensive training interactions from scratch leading to sample inefficiency, cannot leverage existing high-quality solutions, and often yields suboptimal results compared to traditional methods like Constraint Programming (CP). We introduce Offline Reinforcement Learning for Learning to Dispatch (Offline-LD), which addresses these limitations by learning from previously generated solutions. Our approach is motivated by scenarios where historical scheduling data and expert solutions are available, although our current evaluation focuses on benchmark problems. Offline-LD adapts two CQL-based Q-learning methods (mQRDQN and discrete mSAC) for maskable action spaces, introduces a novel entropy bonus modification for discrete SAC, and exploits reward normalization through preprocessing. Our experiments demonstrate that Offline-LD outperforms online RL on both generated and benchmark instances. Notably, by introducing noise into the expert dataset, we achieve similar or better results than those obtained from the expert dataset, suggesting that a more diverse training set is preferable because it contains counterfactual information.","url_abs":"https://arxiv.org/abs/2409.10589v2","url_pdf":"https://arxiv.org/pdf/2409.10589v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"offline-reinforcement-learning-for-learning","repo_url":"https://github.com/jesserem/Offline-LD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"combinatorial-optimization","task_name":"Combinatorial Optimization"},{"task_slug":"job-shop-scheduling","task_name":"Job Shop Scheduling"},{"task_slug":"q-learning","task_name":"Q-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"scheduling","task_name":"Scheduling"},{"task_slug":null,"task_name":"counterfactual"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"q-learning","method_name":"Q-Learning"},{"method_slug":"sac","method_name":"SAC"},{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}