{"id":"https://openalex.org/W7128395922","doi":"https://doi.org/10.48550/arxiv.2602.06138","title":"Flow Matching for Offline Reinforcement Learning with Discrete Actions","display_name":"Flow Matching for Offline Reinforcement Learning with Discrete Actions","publication_year":2026,"publication_date":"2026-02-05","ids":{"openalex":"https://openalex.org/W7128395922","doi":"https://doi.org/10.48550/arxiv.2602.06138"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.06138","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008275929","display_name":"Fairoz Nower Khan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Khan, Fairoz Nower","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125397194","display_name":"Nabuat Zaman Nahim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nahim, Nabuat Zaman","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025491239","display_name":"Ruiquan Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Ruiquan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066140334","display_name":"Haibo Yang","orcid":"https://orcid.org/0009-0006-6521-2145"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Haibo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5085838919","display_name":"Peizhong Ju","orcid":"https://orcid.org/0000-0002-4569-3539"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ju, Peizhong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5008275929"],"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":0.9089000225067139,"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":0.9089000225067139,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.01720000058412552,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.012799999676644802,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.657800018787384},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6388999819755554},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.6022999882698059},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.5920000076293945},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5131000280380249},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4571000039577484},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.41659998893737793},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.39570000767707825}],"concepts":[{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.657800018787384},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6388999819755554},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6341000199317932},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.6022999882698059},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.5920000076293945},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5131000280380249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4912000000476837},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4571000039577484},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.41659998893737793},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.39570000767707825},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3763999938964844},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.35830000042915344},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32710000872612},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32440000772476196},{"id":"https://openalex.org/C2779136372","wikidata":"https://www.wikidata.org/wiki/Q10283002","display_name":"Information flow","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.3025999963283539},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C2780490138","wikidata":"https://www.wikidata.org/wiki/Q7079636","display_name":"Offline learning","level":3,"score":0.2937000095844269},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2849999964237213},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.2619999945163727},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.2612999975681305}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.06138","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.06138","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.06138","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.06138","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.7654784917831421,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generative":[0],"policies":[1],"based":[2],"on":[3],"diffusion":[4],"models":[5],"and":[6,121,148],"flow":[7,39,66],"matching":[8,40,67],"have":[9],"shown":[10],"strong":[11],"promise":[12],"for":[13],"offline":[14,34],"reinforcement":[15],"learning":[16],"(RL),":[17],"but":[18],"their":[19],"applicability":[20],"remains":[21],"largely":[22],"confined":[23],"to":[24,41,74,135],"continuous":[25,56],"action":[26,48,83,139],"spaces.":[27],"To":[28],"address":[29],"a":[30,42,64,86,142],"broader":[31],"range":[32],"of":[33,81],"RL":[35],"settings,":[36,76],"we":[37,54],"extend":[38,71],"general":[43],"framework":[44,130],"that":[45,108],"supports":[46],"discrete":[47,129],"spaces":[49,84],"with":[50,58],"multiple":[51,126],"objectives.":[52,127],"Specifically,":[53],"replace":[55],"flows":[57],"continuous-time":[59],"Markov":[60],"chains,":[61],"trained":[62],"using":[63],"Q-weighted":[65],"objective.":[68],"We":[69,90],"then":[70],"our":[72,109],"design":[73],"multi-agent":[75],"mitigating":[77],"the":[78,101],"exponential":[79],"growth":[80],"joint":[82],"via":[85],"factorized":[87],"conditional":[88],"path.":[89],"theoretically":[91],"show":[92],"that,":[93],"under":[94],"idealized":[95],"conditions,":[96],"optimizing":[97],"this":[98],"objective":[99],"recovers":[100],"optimal":[102],"policy.":[103],"Extensive":[104],"experiments":[105],"further":[106],"demonstrate":[107],"method":[110],"performs":[111],"robustly":[112],"in":[113],"practical":[114],"scenarios,":[115],"including":[116],"high-dimensional":[117],"control,":[118],"multi-modal":[119],"decision-making,":[120],"dynamically":[122],"changing":[123],"preferences":[124],"over":[125],"Our":[128],"can":[131],"also":[132],"be":[133],"applied":[134],"continuous-control":[136],"problems":[137],"through":[138],"quantization,":[140],"providing":[141],"flexible":[143],"trade-off":[144],"between":[145],"representational":[146],"complexity":[147],"performance.":[149]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-10T00:00:00"}
