mlsanityData to 7 Oct 2026

A Reinforcement Learning Environment For Job-Shop Scheduling

arXiv 2104.03760 ↗First posted 8 Apr 2021cs.LG cs.AI cs.NEcited by 21

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Abstract (arXiv)

Scheduling is a fundamental task occurring in various automated systems applications, e.g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste. Nevertheless, finding such schedules is often intractable and cannot be achieved by Combinatorial Optimization Problem (COP) methods within a given time limit. Recent advances of Deep Reinforcement Learning (DRL) in learning complex behavior enable new COP application possibilities. This paper presents an efficient DRL environment for Job-Shop Scheduling -- an important problem in the field. Furthermore, we design a meaningful and compact state representation as well as a novel, simple dense reward function, closely related to the sparse make-span minimization criteria used by COP methods. We demonstrate that our approach significantly outperforms existing DRL methods on classic benchmark instances, coming close to state-of-the-art COP approaches.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 7 of its 25 references that are in the map. First posted on arXiv 8 Apr 2021.

Builds on

7 of 25 references.

References · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  3. 2015Trust Region Policy Optimizationcited by 2,503
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  5. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 612
  6. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 160
  7. 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 139

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 5; 2025: 4; 2026: 4.

21 papers.

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  1. 2022Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Schedulingcited by 20
  2. 2024Graph Neural Networks for Job Shop Scheduling Problems: A Surveycited by 15
  3. 2023An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programmingcited by 11
  4. 2024Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problemcited by 5
  5. 2022Decomposition Strategies and Multi-shot ASP Solving for Job-shop Schedulingcited by 3
  6. 2023Policy-Based Self-Competition for Planning Problemscited by 3
  7. 2023Solving the flexible job-shop scheduling problem through an enhanced deep reinforcement learning approachcited by 3
  8. 2024Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problemcited by 3
  9. 2026Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphscited by 3
  10. 2025A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraintscited by 2
  11. 2023Semiconductor Fab Scheduling with Self-Supervised and Reinforcement Learningcited by 1
  12. 2023A Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swappingcited by 1
9 more
  1. 2023Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problemcited by 1
  2. 2024Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problemcited by 1
  3. 2024Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogisticscited by 1
  4. 2025Scalability of Reinforcement Learning Methods for Dispatching in Semiconductor Frontend Fabs: A Comparison of Open-Source Models with Real Industry Datasetscited by 1
  5. 2025Unraveling the Rainbow: can value-based methods schedule?cited by 0
  6. 2025Deep Learning for Unrelated-Machines Scheduling: Handling Variable Dimensionscited by 0
  7. 2026Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modelingcited by 0
  8. 2026HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformerscited by 0
  9. 2026Curriculum Learning with GNN-based Reinforcement Learning for Job Shop Schedulingcited by 0