{"id":"https://openalex.org/W7147686366","doi":"https://doi.org/10.48550/arxiv.2603.26982","title":"Online Statistical Inference of Constant Sample-averaged Q-Learning","display_name":"Online Statistical Inference of Constant Sample-averaged Q-Learning","publication_year":2026,"publication_date":"2026-03-27","ids":{"openalex":"https://openalex.org/W7147686366","doi":"https://doi.org/10.48550/arxiv.2603.26982"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2603.26982","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2603.26982","pdf_url":"https://arxiv.org/pdf/2603.26982","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2603.26982","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102665106","display_name":"Saunak Kumar Panda","orcid":"https://orcid.org/0009-0008-2399-2387","observed_orcids":["https://orcid.org/0009-0008-2399-2387"]},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Panda, Saunak Kumar","raw_affiliation_strings":["Department of Industrial Engineering University of Houston"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering University of Houston","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132573727","display_name":"Tong Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li, Tong","raw_affiliation_strings":["Department of Industrial Engineering University of Houston"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering University of Houston","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132685624","display_name":"Ruiqi Liu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]},{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liu, Ruiqi","raw_affiliation_strings":["Department of Industrial Engineering University of Houston","Department of Mathematics and Statistics Texas Tech University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering University of Houston","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Department of Mathematics and Statistics Texas Tech University","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063225585","display_name":"Yisha Xiang","orcid":"https://orcid.org/0000-0003-0696-2924","observed_orcids":["https://orcid.org/0000-0003-0696-2924"]},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang, Yisha","raw_affiliation_strings":["Department of Industrial Engineering University of Houston"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering University of Houston","institution_ids":["https://openalex.org/I44461941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.9970999956130981,"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":0.9970999956130981,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.002199999988079071,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11195","display_name":"Simulation Techniques and Applications","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/invariance-principle","display_name":"invariance principle","score":0.9440000057220459},{"id":"https://openalex.org/keywords/confidence-intervals","display_name":"confidence intervals","score":0.7170000076293945},{"id":"https://openalex.org/keywords/q-value-estimation","display_name":"Q-value estimation","score":0.7379999756813049},{"id":"https://openalex.org/keywords/variance-reduction","display_name":"variance reduction","score":0.5249999761581421},{"id":"https://openalex.org/keywords/sparse-rewards","display_name":"sparse rewards","score":0.6790000200271606},{"id":"https://openalex.org/keywords/grid-world","display_name":"grid world","score":0.6129999756813049},{"id":"https://openalex.org/keywords/coverage-rate","display_name":"coverage rate","score":0.41100001335144043}],"concepts":[{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.6746000051498413},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6276999711990356},{"id":"https://openalex.org/C95167961","wikidata":"https://www.wikidata.org/wiki/Q4483495","display_name":"Fiducial inference","level":5,"score":0.557699978351593},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5299000144004822},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5157999992370605},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.45320001244544983},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.44749999046325684},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4465000033378601},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.44290000200271606},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.4375},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4359000027179718},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.42980000376701355},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.42730000615119934},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4219000041484833},{"id":"https://openalex.org/C166785042","wikidata":"https://www.wikidata.org/wiki/Q190391","display_name":"Central limit theorem","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3928000032901764},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.3901999890804291},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.3601999878883362},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35359999537467957},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C2986577269","wikidata":"https://www.wikidata.org/wiki/Q11306265","display_name":"Random noise","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.28780001401901245},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26589998602867126},{"id":"https://openalex.org/C917703","wikidata":"https://www.wikidata.org/wiki/Q7239668","display_name":"Predictive inference","level":5,"score":0.2621000111103058},{"id":"https://openalex.org/C128669082","wikidata":"https://www.wikidata.org/wiki/Q583461","display_name":"Randomized algorithm","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2603.26982","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2603.26982","pdf_url":"https://arxiv.org/pdf/2603.26982","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2603.26982","doi":"https://doi.org/10.48550/arxiv.2603.26982","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26982","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2603.26982","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2603.26982","pdf_url":"https://arxiv.org/pdf/2603.26982","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G7995056702","display_name":"CAREER: Enhancing Environmental and Economic Sustainability of Additive Manufacturing-based Remanufacturing","funder_award_id":"2305486","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7147686366.pdf","grobid_xml":"https://content.openalex.org/works/W7147686366.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4410295167","https://openalex.org/W7203483596","https://openalex.org/W2980012679","https://openalex.org/W4416858271","https://openalex.org/W4311035298","https://openalex.org/W4289029590","https://openalex.org/W7112724140","https://openalex.org/W2105114545","https://openalex.org/W4200424214","https://openalex.org/W3045055178"],"abstract_inverted_index":{"Reinforcement":[0],"learning":[1],"algorithms":[2,18],"have":[3],"been":[4],"widely":[5],"used":[6],"for":[7,47,60,73,128],"decision-making":[8],"tasks":[9],"in":[10,28],"various":[11],"domains.":[12],"However,":[13],"the":[14,54,61,74,87,131],"performance":[15],"of":[16],"these":[17],"can":[19],"be":[20],"impacted":[21],"by":[22],"high":[23],"variance":[24],"and":[25,68,90,98,103,119],"instability,":[26],"particularly":[27],"environments":[29],"with":[30],"noise":[31],"or":[32],"sparse":[33],"rewards.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38],"propose":[39],"a":[40,48,110,115,120,125],"framework":[41],"to":[42,82],"perform":[43,83],"statistical":[44],"online":[45],"inference":[46,84],"sample-averaged":[49],"Q-learning":[50,94],"approach.":[51],"We":[52,79],"adapt":[53],"functional":[55],"central":[56],"limit":[57],"theorem":[58],"(FCLT)":[59],"modified":[62,88],"algorithm":[63],"under":[64],"some":[65],"general":[66],"conditions":[67],"then":[69],"construct":[70],"confidence":[71,104],"intervals":[72],"Q-values":[75],"via":[76],"random":[77,96],"scaling.":[78],"conduct":[80],"experiments":[81],"on":[85,107],"both":[86],"approach":[89],"its":[91],"traditional":[92],"counterpart,":[93],"using":[95],"scaling":[97],"report":[99],"their":[100],"coverage":[101],"rates":[102],"interval":[105],"widths":[106],"two":[108,132],"problems:":[109],"grid":[111],"world":[112],"problem":[113,123],"as":[114,124],"simple":[116],"toy":[117],"example":[118,127],"dynamic":[121],"resource-matching":[122],"real-world":[126],"comparison":[129],"between":[130],"solution":[133],"approaches.":[134]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-04-02T00:00:00"}