mlsanityData to 7 Oct 2026

On Learning Paradigms for the Travelling Salesman Problem

arXiv 1910.07210 ↗First posted 16 Oct 2019cs.LG stat.MLcited by 24

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Abstract (arXiv)

We explore the impact of learning paradigms on training deep neural networks for the Travelling Salesman Problem. We design controlled experiments to train supervised learning (SL) and reinforcement learning (RL) models on fixed graph sizes up to 100 nodes, and evaluate them on variable sized graphs up to 500 nodes. Beyond not needing labelled data, our results reveal favorable properties of RL over SL: RL training leads to better emergent generalization to variable graph sizes and is a key component for learning scale-invariant solvers for novel combinatorial problems.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Oct 2019.

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Citing papers per year: 2021: 8; 2022: 8; 2023: 3; 2024: 2; 2025: 1; 2026: 2.

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  1. 2022DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problemscited by 127
  2. 2021Deep Policy Dynamic Programming for Vehicle Routing Problemscited by 94
  3. 2023Unsupervised Learning for Solving the Travelling Salesman Problemcited by 57
  4. 2022Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculumcited by 27
  5. 2022On the Generalization of Neural Combinatorial Optimization Heuristicscited by 24
  6. 2024Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectivescited by 23
  7. 2021Analytics and Machine Learning in Vehicle Routing Researchcited by 22
  8. 2021An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problemcited by 22
  9. 2022Learning to Solve Vehicle Routing Problems: A Surveycited by 16
  10. 2021Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problemscited by 13
  11. 2021Curriculum Design for Teaching via Demonstrations: Theory and Applicationscited by 9
  12. 2022Unsupervised Training for Neural TSP Solvercited by 9
12 more
  1. 2021Maximizing Influence with Graph Neural Networkscited by 8
  2. 2021Improving Generalization of Deep Reinforcement Learning-based TSP Solverscited by 8
  3. 2021Generalization in Deep RL for TSP Problems via Equivariance and Local Searchcited by 8
  4. 2022Learning to Untangle Genome Assembly with Graph Convolutional Networkscited by 3
  5. 2022End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Locationcited by 3
  6. 2023Predicting Drivers' Route Trajectories in Last-Mile Delivery Using A Pair-wise Attention-based Pointer Neural Networkcited by 3
  7. 2022Neural Simulated Annealingcited by 2
  8. 2025Mechanistic Interpretability for Neural TSP Solverscited by 2
  9. 2023Multi-Start Team Orienteering Problem for UAS Mission Re-Planning with Data-Efficient Deep Reinforcement Learningcited by 0
  10. 2024Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy, Survey and Insightscited by 0
  11. 2026SEAFormer: A Spatial Proximity and Edge-Aware Transformer for Real-World Vehicle Routing Problemscited by 0
  12. 2026Offline Decision Transformers for Neural Combinatorial Optimization: Surpassing Heuristics on the Traveling Salesman Problemcited by 0