{"id":"https://openalex.org/W7141735919","doi":"https://doi.org/10.48550/arxiv.2603.25241","title":"Offline Decision Transformers for Neural Combinatorial Optimization: Surpassing Heuristics on the Traveling Salesman Problem","display_name":"Offline Decision Transformers for Neural Combinatorial Optimization: Surpassing Heuristics on the Traveling Salesman Problem","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7141735919","doi":"https://doi.org/10.48550/arxiv.2603.25241"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25241","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25241","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.25241","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071415078","display_name":"Hironori Ohigashi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ohigashi, Hironori","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5075393065","display_name":"Shinichiro Hamada","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hamada, Shinichiro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.18860000371932983,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.18860000371932983,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.08389999717473984,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.059300001710653305,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/travelling-salesman-problem","display_name":"Travelling salesman problem","score":0.6510999798774719},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.6435999870300293},{"id":"https://openalex.org/keywords/combinatorial-optimization","display_name":"Combinatorial optimization","score":0.4781000018119812},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47099998593330383},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4677000045776367},{"id":"https://openalex.org/keywords/2-opt","display_name":"2-opt","score":0.4154999852180481},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.3862999975681305}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6931999921798706},{"id":"https://openalex.org/C175859090","wikidata":"https://www.wikidata.org/wiki/Q322212","display_name":"Travelling salesman problem","level":2,"score":0.6510999798774719},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.6435999870300293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49639999866485596},{"id":"https://openalex.org/C52692508","wikidata":"https://www.wikidata.org/wiki/Q1333872","display_name":"Combinatorial optimization","level":2,"score":0.4781000018119812},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47099998593330383},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.46959999203681946},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C106472803","wikidata":"https://www.wikidata.org/wiki/Q291440","display_name":"2-opt","level":3,"score":0.4154999852180481},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37700000405311584},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C103888782","wikidata":"https://www.wikidata.org/wiki/Q2835802","display_name":"Lin\u2013Kernighan heuristic","level":4,"score":0.263700008392334},{"id":"https://openalex.org/C114073186","wikidata":"https://www.wikidata.org/wiki/Q2631895","display_name":"Automated planning and scheduling","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25241","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25241","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.25241","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25241","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5971433520317078,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Combinatorial":[0,15],"optimization":[1],"problems":[2],"like":[3],"the":[4,42,80,118,127,136],"Traveling":[5],"Salesman":[6],"Problem":[7],"are":[8],"critical":[9],"in":[10,139],"industry":[11],"yet":[12],"NP-hard.":[13],"Neural":[14],"Optimization":[16],"has":[17],"shown":[18],"promise,":[19],"but":[20,65],"its":[21],"reliance":[22],"on":[23],"online":[24],"reinforcement":[25],"learning":[26],"(RL)":[27],"hampers":[28],"deployment":[29],"and":[30,68,88,134],"underutilizes":[31],"decades":[32],"of":[33,55,85,96,129],"algorithmic":[34],"knowledge.":[35,142],"We":[36],"address":[37],"these":[38],"limitations":[39],"by":[40],"applying":[41],"offline":[43,130],"RL":[44,131],"framework,":[45],"Decision":[46],"Transformer,":[47],"to":[48,60,63,66,78,132],"learn":[49],"superior":[50],"strategies":[51],"directly":[52],"from":[53],"datasets":[54],"heuristic":[56],"solutions;":[57],"it":[58,122],"aims":[59],"not":[61],"only":[62],"imitate":[64],"synthesize":[67],"outperform":[69],"them.":[70],"Concretely,":[71],"we":[72],"(i)":[73],"integrate":[74],"a":[75],"Pointer":[76],"Network":[77],"handle":[79],"instance-dependent,":[81],"variable":[82],"action":[83],"space":[84],"node":[86],"selection,":[87],"(ii)":[89],"employ":[90],"expectile":[91],"regression":[92],"for":[93,101],"optimistic":[94],"conditioning":[95],"Return-to-Go,":[97],"which":[98],"is":[99,123],"crucial":[100],"instances":[102],"with":[103],"widely":[104],"varying":[105],"optimal":[106],"values.":[107],"Experiments":[108],"show":[109],"that":[110],"our":[111],"method":[112],"consistently":[113],"produces":[114],"higher-quality":[115],"tours":[116],"than":[117],"four":[119],"classical":[120],"heuristics":[121],"trained":[124],"on,":[125],"demonstrating":[126],"potential":[128],"unlock":[133],"exceed":[135],"performance":[137],"embedded":[138],"existing":[140],"domain":[141]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-28T00:00:00"}