{"id":"https://openalex.org/W7162309303","doi":"https://doi.org/10.48550/arxiv.2605.23182","title":"Pure Exploration for a Good Policy in Reinforcement Learning with Bandit Feedback","display_name":"Pure Exploration for a Good Policy in Reinforcement Learning with Bandit Feedback","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162309303","doi":"https://doi.org/10.48550/arxiv.2605.23182"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.23182","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23182","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":"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.23182","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136929507","display_name":"Zitian Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zitian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136959285","display_name":"Wang Chi Cheung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheung, Wang Chi","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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.8129000067710876,"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"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.8129000067710876,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.09769999980926514,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.05079999938607216,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7688000202178955},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5981000065803528},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.54339998960495},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5274999737739563},{"id":"https://openalex.org/keywords/sample-complexity","display_name":"Sample complexity","score":0.5164999961853027},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.44609999656677246},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.43869999051094055},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.4359999895095825}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7688000202178955},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5981000065803528},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.54339998960495},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5274999737739563},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4713999927043915},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.44609999656677246},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.43869999051094055},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.4359999895095825},{"id":"https://openalex.org/C100279318","wikidata":"https://www.wikidata.org/wiki/Q467440","display_name":"Sample space","level":2,"score":0.4196999967098236},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41850000619888306},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3882000148296356},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.38749998807907104},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3357999920845032},{"id":"https://openalex.org/C136979486","wikidata":"https://www.wikidata.org/wiki/Q773483","display_name":"Existential quantification","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2842999994754791},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2757999897003174},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2662000060081482},{"id":"https://openalex.org/C176248197","wikidata":"https://www.wikidata.org/wiki/Q458526","display_name":"Probably approximately correct learning","level":4,"score":0.26089999079704285},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.23182","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23182","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.23182","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.23182","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":"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"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6092184782028198}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Pure":[0],"exploration":[1],"in":[2,59,148,169,185,192],"episodic":[3],"Reinforcement":[4],"Learning":[5],"has":[6],"primarily":[7],"focused":[8],"on":[9,130,177],"Best":[10],"Policy":[11,41],"Identification":[12,42],"(BPI),":[13],"which":[14,108],"seeks":[15],"to":[16,93,102,188,200],"identify":[17],"a":[18,29,45,54,67,121],"(near)-optimal":[19],"policy":[20,32,55,68,78],"with":[21,56,96],"high":[22],"confidence.":[23],"Motivated":[24],"by":[25],"practical":[26],"settings":[27],"where":[28,156],"``good":[30],"enough''":[31],"suffices,":[33],"we":[34],"study":[35],"an":[36,60,170],"alternate":[37],"objective":[38],"of":[39,113,145,204,209,220],"Good":[40],"(GPI).":[43],"For":[44],"given":[46],"reward":[47,58,168],"threshold":[48],"$\u03bc_0$,":[49],"GPI":[50,84],"only":[51],"requires":[52],"identifying":[53],"expected":[57,105,111,167],"episode":[61,160],"at":[62],"least":[63],"$\u03bc_0$":[64],"if":[65,75],"such":[66,77],"exists":[69,79],"(positive":[70],"instance),":[71],"or":[72],"declaring":[73],"None":[74],"no":[76],"(negative":[80],"instance).":[81],"We":[82,89,119,194],"formalize":[83],"under":[85],"the":[86,91,104,110,117,143,159,165,178,189,202,207,210,218],"fixed-confidence":[87],"setting.":[88],"require":[90],"output":[92],"be":[94],"correct":[95],"probability":[97],"$\\geq":[98],"1-\u03b4$,":[99],"and":[100,125,136,162,180,206],"seek":[101],"minimize":[103],"sample":[106,132,190],"complexity,":[107],"is":[109,152,158,164],"number":[112],"episodes":[114],"explored":[115],"for":[116,134,140],"output.":[118],"propose":[120],"novel":[122],"algorithm":[123],"BEE-GPI,":[124],"derive":[126],"theoretically-grounded":[127],"upper":[128,150],"bounds":[129],"its":[131],"complexity":[133,191],"positive":[135,141],"negative":[137],"instances.":[138],"Notably,":[139],"instances,":[142],"coefficient":[144,173],"$\\log":[146],"1/\u03b4$":[147],"our":[149,221],"bound":[151,198],"$O(H^2/(V^*":[153],"-":[154],"\u03bc_0)^2)$,":[155],"$H$":[157],"length":[161],"$V^*$":[163],"optimal":[166],"episode.":[171],"The":[172],"does":[174],"not":[175],"depend":[176],"action":[179],"state":[181],"space":[182],"sizes":[183],"otherwise,":[184],"sharp":[186],"contrast":[187],"BPI.":[193],"further":[195,216],"establish":[196],"lower":[197],"results":[199],"show":[201],"near-optimality":[203],"BEE-GPI":[205],"necessity":[208],"$1/(V^*":[211],"-\u03bc)^2$":[212],"term.":[213],"Numerical":[214],"experiments":[215],"validate":[217],"efficiency":[219],"approach.":[222]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-26T00:00:00"}