A PyTorch library for all things Reinforcement Learning (RL) for Combinatorial Optimization (CO)
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Updated
May 12, 2026 - Python
A PyTorch library for all things Reinforcement Learning (RL) for Combinatorial Optimization (CO)
PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning https://arxiv.org/abs/1611.09940
Recent research papers about Foundation Models for Combinatorial Optimization
[NeurIPS 2024] ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
[NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
Deep Reinforcement Learning for Multiobjective Optimization. Code for this paper
This repo implements our paper, "Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer", which has been accepted at NeurIPS 2021.
[TMLR 2025 → ICML 2026] RouteFinder: Towards Foundation Models for Vehicle Routing Problems
[AAAI 2024] GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
L2O/NCO codes from CIAM Group at SUSTech, Shenzhen, China
[ICML 2024] "MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts"
This repo implements our paper, "Efficient Neural Neighborhood Search for Pickup and Delivery Problems", which has been accepted as short oral at IJCAI 2022.
[IROS 2024] EPH: Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
The implementation code of our paper "Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation", accepted at NeurIPS2022.
[NeurIPS 2025] PARCO: Parallel AutoRegressive Combinatorial Optimization
This repo implements our paper, "Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt", which has been accepted at NeurIPS 2023.
[ICLR 2026] Neural Combinatorial Optimization for Real-World Routing
[AAMAS 2025 Oral] CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
Official repository for the TMLR paper "Self-Improvement for Neural Combinatorial Optimization: Sample Without Replacement, but Improvement"
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