mlsanityData to 14 Oct 2026

ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

arXiv 2106.03051 ↗First posted 6 Jun 2021cs.LG cs.AI cs.MAcited by 31

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Abstract (arXiv)

We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems. We formulate these problems as a semi-MDP with episodic reward (makespan) and learn ScheduleNet, a decentralized decision-making policy that can effectively coordinate multiple agents to complete tasks. The decision making procedure of ScheduleNet includes: (1) representing the state of a scheduling problem with the agent-task graph, (2) extracting node embeddings for agent and tasks nodes, the important relational information among agents and tasks, by employing the type-aware graph attention (TGA), and (3) computing the assignment probability with the computed node embeddings. We validate the effectiveness of ScheduleNet as a general learning-based scheduler for solving various types of multi-agent scheduling tasks, including multiple salesman traveling problem (mTSP) and job shop scheduling problem (JSP).

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 23 of its 45 references that are in the map. First posted on arXiv 6 Jun 2021.

Builds on

23 of 45 references.

References · 23

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11 more
  1. 2019Learning Improvement Heuristics for Solving Routing Problemscited by 164
  2. 2020Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instancescited by 162
  3. 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 140
  4. 2020Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learningcited by 100
  5. 2021Deep Policy Dynamic Programming for Vehicle Routing Problemscited by 94
  6. 2020Learning What to Defer for Maximum Independent Setscited by 54
  7. 2021Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learningcited by 52
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 6; 2024: 9; 2025: 3; 2026: 7.

31 papers.

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  1. 2023Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalizationcited by 142
  2. 2022Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimizationcited by 119
  3. 2024Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvementcited by 34
  4. 2023Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimizationcited by 23
  5. 2022Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Schedulingcited by 20
  6. 2022Learning to Control Local Search for Combinatorial Optimizationcited by 18
  7. 2024DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problemscited by 18
  8. 2024Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomycited by 16
  9. 2024Graph Neural Networks for Job Shop Scheduling Problems: A Surveycited by 15
  10. 2025BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimizationcited by 14
  11. 2023Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problemcited by 12
  12. 2022Learning context-aware adaptive solvers to accelerate quadratic programmingcited by 7
19 more
  1. 2021DAN: Decentralized Attention-based Neural Network for the MinMax Multiple Traveling Salesman Problemcited by 6
  2. 2023Maximum Independent Set: Self-Training through Dynamic Programmingcited by 5
  3. 2024Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problemcited by 5
  4. 2023A Neural Separation Algorithm for the Rounded Capacity Inequalitiescited by 4
  5. 2024iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learningcited by 4
  6. 2023Policy-Based Self-Competition for Planning Problemscited by 3
  7. 2024Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problemcited by 3
  8. 2022Neuro CROSS exchange: Learning to CROSS exchange to solve realistic vehicle routing problemscited by 2
  9. 2024Offline reinforcement learning for job-shop scheduling problemscited by 2
  10. 2024Graph Q-Learning for Combinatorial Optimizationcited by 1
  11. 2025Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networkscited by 0
  12. 2025Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutionscited by 0
  13. 2026Variational Approach for Job Shop Schedulingcited by 0
  14. 2026Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modelingcited by 0
  15. 2026Construct, Merge, Solve & Adapt with Reinforcement Learning for the min-max Multiple Traveling Salesman Problemcited by 0
  16. 2026Agentic-Kube: A Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-Objective Kubernetes Schedulingcited by 0
  17. 2026GOAL: Graph-based Objective-Aligned Diffusion Solvers for Dynamic Multi-Objective Optimizationcited by 0
  18. 2026Low-Cost Labels, Reliable Choices: Rollout-Calibrated Hyper-Heuristics for Job Shop Schedulingcited by 0
  19. 2026DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agentscited by 0