wouterkool/attention-learn-to-route

Health v2 · maintenance only

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Adoption not part of the score

1383 stars · 389 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of the attention-based neural model from the ICLR 2019 paper 'Attention, Learn to Solve Routing Problems!', trained with REINFORCE and a greedy rollout baseline. It learns to solve combinatorial routing problems including TSP, VRP, Orienteering Problem, and (Stochastic) Prize Collecting TSP.

Use cases

• train a neural network to solve TSP instances
• learn to solve vehicle routing problems with deep learning
• reproduce results from the Attention Learn to Solve Routing Problems paper
• benchmark neural combinatorial optimization models
• experiment with REINFORCE and rollout baselines for routing problems
• generate and validate routing problem datasets

When to choose

• you want to reproduce or build on the ICLR 2019 attention model for routing problems
• you need pretrained models for TSP, VRP, OP, or PCTSP research
• you are studying neural combinatorial optimization with REINFORCE

When to avoid

• you need an actively maintained library - the author recommends rl4co or RLOR instead
• you need production routing solvers rather than research code
• you want exact or heuristic solvers like OR-Tools or LKH

Facets

library · maturity maintenance

machine-learning deep-learning reinforcement-learning llm-training machine-learning deep-learning python cross-platform combinatorial-optimization travelling-salesman-problem vehicle-routing-problem reinforcement-learning transformer attention-model iclr-2019 pytorch research-code algorithms research gpu

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