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In this work, we introduce Graph Pointer Networks (GPNs) trained using reinforcement learning (RL) for tackling the traveling salesman problem (TSP).
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Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song, ‘Learning combinatorial optimization algorithms over graphs’, in Advances in Neural Information Processing Systems
2017
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Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel, ‘Self-critical sequence training for image captioning’, in IEEE Conference on Computer Vision and Pattern Recognition
2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin, ‘Attention is all you need’, in Advances in neural information processing systems
2017
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Tuomas Haarnoja, Kristian Hartikainen, Pieter Abbeel, and Sergey Levine, ‘Latent space policies for hierarchical reinforcement learning’, in International Conference on Machine Learning
2018
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Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine, ‘Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor’, in International Conference on Machine Learning
2018
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2019
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Wouter Kool, Herke van Hoof, and Max Welling, ‘Attention, learn to solve routing problems!’, International Conference on Learning Representations
2019
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Wouter Kool, Herke van Hoof, and Max Welling, ‘Buy 4 reinforce samples, get a baseline for free!’, International Conference on Learning Representations
2019
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Quiang Ma, Suwen Ge, Danyang He, Darshan Thaker, and Iddo Drori, ‘GitHub Repository for Combinatorial Optimization by Graph Pointer Networksand Hierarchical Reinforcement Learning’, https://github.com/qiang-ma/graph-pointer-network
2019
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka, ‘How powerful are graph neural networks?’, International Conference on Learning Representations
2019
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