mlsanityData to 7 Oct 2026

Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems

arXiv 2012.10638 ↗First posted 19 Dec 2020cs.LG cs.AIcited by 100

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Abstract (arXiv)

We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM) to train multiple diverse policies, which effectively increases the chance of finding good solutions compared with existing methods that train only one policy. A customized beam search strategy is designed to fully exploit the diversity of MDAM. In addition, we propose an Embedding Glimpse layer in MDAM based on the recursive nature of construction, which can improve the quality of each policy by providing more informative embeddings. Extensive experiments on six different routing problems show that our method significantly outperforms the state-of-the-art deep learning based models.

Key facts. Cited by 100 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Dec 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 13; 2023: 22; 2024: 18; 2025: 24; 2026: 13.

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  1. 2023DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimizationcited by 216
  2. 2023Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalizationcited by 140
  3. 2022DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problemscited by 127
  4. 2022Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimizationcited by 118
  5. 2021Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformercited by 108
  6. 2021NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problemcited by 102
  7. 2021Learning Collaborative Policies to Solve NP-hard Routing Problemscited by 86
  8. 2022Simulation-guided Beam Search for Neural Combinatorial Optimizationcited by 84
  9. 2023GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-timecited by 82
  10. 2023Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Optcited by 81
  11. 2023BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimizationcited by 80
  12. 2023DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimizationcited by 77
38 more
  1. 2023RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmarkcited by 70
  2. 2023Towards Omni-generalizable Neural Methods for Vehicle Routing Problemscited by 68
  3. 2022Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillationcited by 61
  4. 2022Winner Takes It All: Training Performant RL Populations for Combinatorial Optimizationcited by 57
  5. 2024Ant Colony Sampling with GFlowNets for Combinatorial Optimizationcited by 53
  6. 2024INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformercited by 43
  7. 2023Combinatorial Optimization with Policy Adaptation using Latent Space Searchcited by 41
  8. 2024GOAL: A Generalist Combinatorial Optimization Agent Learnercited by 39
  9. 2025Rethinking Light Decoder-based Solvers for Vehicle Routing Problemscited by 36
  10. 2021Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learningcited by 35
  11. 2021Learning Large Neighborhood Search Policy for Integer Programmingcited by 35
  12. 2024PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimizationcited by 35
  13. 2024Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvementcited by 32
  14. 2021Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problemcited by 31
  15. 2022Learning to Solve Routing Problems via Distributionally Robust Optimizationcited by 31
  16. 2024Self-Improved Learning for Scalable Neural Combinatorial Optimizationcited by 29
  17. 2024Learning to Handle Complex Constraints for Vehicle Routing Problemscited by 29
  18. 2023ASP: Learn a Universal Neural Solver!cited by 27
  19. 2024Cross-Problem Learning for Solving Vehicle Routing Problemscited by 24
  20. 2023Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimizationcited by 23
  21. 2024Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectivescited by 23
  22. 2021An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problemcited by 22
  23. 2023Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimizationcited by 21
  24. 2021Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problemscited by 20
  25. 2022Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Schedulingcited by 20
  26. 2023Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speedcited by 19
  27. 2022Learning to Solve Vehicle Routing Problems: A Surveycited by 16
  28. 2025Learning to Reduce Search Space for Generalizable Neural Routing Solvercited by 14
  29. 2023Neural Multi-Objective Combinatorial Optimization with Diversity Enhancementcited by 13
  30. 2025MTL-KD: Multi-Task Learning Via Knowledge Distillation for Generalizable Neural Vehicle Routing Solvercited by 13
  31. 2022Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman Problemscited by 12
  32. 2023Continuation Path Learning for Homotopy Optimizationcited by 12
  33. 2025Preference Optimization for Combinatorial Optimization Problemscited by 11
  34. 2023Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handlingcited by 10
  35. 2024DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problemscited by 10
  36. 2025Adversarial Generative Flow Network for Solving Vehicle Routing Problemscited by 10
  37. 2025Learning to Insert for Constructive Neural Vehicle Routing Solvercited by 10
  38. 2025An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problemcited by 9

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