{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T08:31:39Z","timestamp":1773477099088,"version":"3.50.1"},"reference-count":44,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:00:00Z","timestamp":1716422400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:00:00Z","timestamp":1716422400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100016772","name":"KT","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016772","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003696","name":"Electronics and Telecommunications Research Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003696","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,23]]},"DOI":"10.1109\/eais58494.2024.10569999","type":"proceedings-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T17:55:01Z","timestamp":1719424501000},"page":"1-11","source":"Crossref","is-referenced-by-count":1,"title":["Learning to Escape: Multi-mode Policy Learning for the Traveling Salesmen Problem"],"prefix":"10.1109","author":[{"given":"Myoung Hoon","family":"Ha","sequence":"first","affiliation":[{"name":"KAIST,Center for Neuroscience-inspired AI,Daejeon,Korea"}]},{"given":"Seunggeun","family":"Chi","sequence":"additional","affiliation":[{"name":"Purdue University,School of Electrical & Computer Engineering,West Lafayette,USA"}]},{"given":"Sang Wan","family":"Lee","sequence":"additional","affiliation":[{"name":"KAIST,Department of Brain Cognitive Science,Daejeon,Korea"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Computers and intractability: a guide to the theory of NP-completeness","author":"Garey","year":"1979"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1287\/opre.21.2.498"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-2217(99)00284-2"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CIS.2011.24"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-27287-5_17"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1057\/jors.2012.37"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1057\/jors.2013.71"},{"key":"ref8","first-page":"2692","article-title":"Pointer Networks","volume":"28","author":"Vinyals","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref9","article-title":"Neural Combinatorial Optimization with Reinforcement Learning","author":"Bello","year":"2017","journal-title":"arXiv:1611.09940"},{"key":"ref10","first-page":"6351","article-title":"Learning combinatorial optimization algorithms over graphs","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems, ser. NIPS\u201917","author":"Dai"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93031-2_12"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100369"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/0167-6377(94)90065-5"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1057\/jors.2010.116"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-30698-8_5"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-31471-6_1"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s10732-017-9334-0"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"ref20","article-title":"Playing Atari with Deep Reinforcement Learning","author":"Mnih","year":"2013","journal-title":"arXiv:1312.5602"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cobeha.2018.12.012"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aav2975"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2020.07.063"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/595"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3068828"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3319619.3326852"},{"key":"ref27","article-title":"Gurobi Optimizer Reference Manual","year":"2018"},{"key":"ref28","article-title":"Attention, learn to solve routing problems!","volume-title":"International Conference on Learning Representations","author":"Kool"},{"key":"ref29","first-page":"1928","article-title":"Asynchronous Methods for Deep Reinforcement Learning","author":"Mnih","year":"2016","journal-title":"PMLR"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref31","first-page":"448","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","volume-title":"Proceedings of the 32nd International Conference on International Conference on Machine Learning","volume":"37","author":"Ioffe"},{"key":"ref32","first-page":"807","article-title":"Rectified linear units improve restricted boltzmann machines","volume-title":"Proceedings of the 27th International Conference on International Conference on Machine Learning","author":"Nair"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03456-w"},{"key":"ref34","first-page":"1126","article-title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","volume-title":"International Conference on Machine Learning","author":"Finn"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3449639.3459386"},{"key":"ref36","article-title":"A Survey of Meta-Reinforcement Learning","author":"Beck","year":"2023","journal-title":"arXiv:2301.08028"},{"key":"ref37","first-page":"3540","article-title":"FeUdal Networks for Hierarchical Reinforcement Learning","volume-title":"International Conference on Machine Learning","author":"Vezhnevets"},{"key":"ref38","first-page":"1407","article-title":"Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards","volume":"32","author":"Li","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3390\/make4010009"},{"key":"ref40","article-title":"Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks","author":"Nowak","year":"2018","journal-title":"arXiv:1706.07450"},{"key":"ref41","article-title":"Exact solutions to the nonlinear dynamics of learning in deep linear neural networks","author":"Saxe"},{"key":"ref42","article-title":"Automatic differentiation in PyTorch","author":"Paszke","year":"2017"},{"key":"ref43","article-title":"The Traveling Salesman Problem: A Computational Study","author":"Applegate","year":"2006"},{"key":"ref44","article-title":"An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems Technical report","author":"Helsgaun","year":"2017"}],"event":{"name":"2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)","location":"Madrid, Spain","start":{"date-parts":[[2024,5,23]]},"end":{"date-parts":[[2024,5,24]]}},"container-title":["2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10569136\/10569095\/10569999.pdf?arnumber=10569999","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T04:52:27Z","timestamp":1719550347000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10569999\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,23]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/eais58494.2024.10569999","relation":{},"subject":[],"published":{"date-parts":[[2024,5,23]]}}}