PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning https://arxiv.org/abs/1611.09940
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Updated
May 29, 2018 - Python
PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning https://arxiv.org/abs/1611.09940
A PyTorch library for all things Reinforcement Learning (RL) for Combinatorial Optimization (CO)
Deep Reinforcement Learning for Multiobjective Optimization. Code for this paper
Large Language Models as Hyper-Heuristics for Combinatorial Optimization (CO)
[AAAI 2024] GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
This repo implements our paper, "Efficient Neural Neighborhood Search for Pickup and Delivery Problems", which has been accepted as short oral at IJCAI 2022.
The implementation code of our paper "Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation", accepted at NeurIPS2022.
L2O/NCO codes from CIAM Group at SUSTech, Shenzhen, China
[ICML 2024] "MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts"
[ICML'24 FM-Wild Oral] RouteFinder: Towards Foundation Models for Vehicle Routing Problems
[ICML 2023] Official code for "DevFormer: A Symmetric Transformer for Context-Aware Device Placement"
Official implementation of IJCAI'24 paper "Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy"
[IROS'24] EPH: Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
[ICML'24] Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More
Unofficial implemnetation of "Solving Quadratic Assignemt Problem using Deep Reinforcement Learning" (https://arxiv.org/abs/2310.01604)
The Neural Combinatorial Optimization Library (NCOLib) is an accessible software library designed to simplify the application of neural network models and deep learning algorithms to solve combinatorial optimization problems.
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