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Reinforcement learning has been applied in operation research and has shown promise in solving large combinatorial optimization problems.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Generalize a Small Pre-trained Model to Arbitrarily Large {TSP} Instances
Zhang-Hua Fu, Kai-Bin Qiu, and Hongyuan Zha · 2012
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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OpenAI Gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Learning combinatorial optimization algorithms over graphs
Hanjun Dai, Elias B Khalil, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
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RLlib: Abstractions for Distributed Reinforcement Learning
Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, and Ion Stoica · 2017
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Combinatorial optimization with graph convolutional networks and guided tree search
Zhuwen Li, Qifeng Chen, and Vladlen Koltun · 2018
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Reinforcement learning for solving the vehicle routing problem
Mohammadreza Nazari, Afshin Oroojlooy, Martin Takáč, and Lawrence V Snyder · 2018
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2019
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Learning to perform local rewriting for combinatorial optimization
Xinyun Chen and Yuandong Tian · 2019
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An efficient graph convolutional network technique for the travelling salesman problem
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson · 2019
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Attention, learn to solve routing problems!
Wouter Kool, Herke Van Hoof, and Max Welling · 2019
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Learning Improvement Heuristics for Solving the Travelling Salesman Problem
Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, and Andrew Lim · 2019
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Exploratory Combinatorial Optimization with Reinforcement Learning
Thomas D. Barrett, William R. Clements, Jakob N. Foerster, and A. I. Lvovsky · 2020
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OR-Gym: A Reinforcement Learning Library for Operations Research Problems, 2020
Christian D Hubbs, Hector D Perez, Owais Sarwar, Nikolaos V Sahinidis, Ignacio E Grossmann, and John M Wassick · 2020
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POMO: Policy optimization with multiple optima for reinforcement learning
Yeong Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, and Seungjai Min · 2020
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Deep Policy Dynamic Programming for Vehicle Routing Problems
Wouter Kool, Herke van Hoof, Joaquim A S Gromicho, and Max Welling · 2021
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Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, and Jing Tang · 2021
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Generalization in Deep {RL} for {TSP} Problems via Equivariance and Local Search
Wenbin Ouyang, Yisen Wang, Paul Weng, and Shaochen Han · 2021
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Stable-Baselines3: Reliable Reinforcement Learning Implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann · 2021
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graphenv: a Python library for reinforcement learning on graph search spaces
David Biagioni, Charles Edison Tripp, Struan Clark, Dmitry Duplyakin, Jeffrey Law, and Peter C St John · 2022
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A Learning-Based Iterative Method for Solving Vehicle Routing Problems
Hao Lu, Xingwen Zhang, and Shuang Yang · 2020
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GCOMB: Learning budget-constrained combinatorial algorithms over billion-sized graphs
Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu, and Ambuj Singh · 2020
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Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
Quentin Cappart, Thierry Moisan, Louis Martin Rousseau, Isabeau Prémont-Schwarz, and Andre A. Cire · 2021
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Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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{DI-engine: OpenDILab} Decision Intelligence Engine
DI-engine Contributors · 2021
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CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
Shengyi Huang, Rousslan Fernand, Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, and João G M Araújo
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What’s wrong with deep learning in tree search for combinatorial optimization
Maximilian Böther, Otto Kißig, Martin Taraz, Sarel Cohen, Karen Seidel, and Tobias Friedrich · 2022
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Efficient Active Search for Combinatorial Optimization Problems
André Hottung, Yeong-Dae Kwon, and Kevin Tierney · 2022
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The 37 Implementation Details of Proximal Policy Optimization
Shengyi Huang, Rousslan Fernand Julien Dossa, Antonin Raffin, Anssi Kanervisto, and Weixun Wang · 2022
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Recent Advances in Deep Learning for Routing Problems
Chaitanya K Joshi and Rishabh Anand · 2022
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Tianshou: A Highly Modularized Deep Reinforcement Learning Library
Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, and Jun Zhu · 2022
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