{"id":"https://openalex.org/W7148729830","doi":"https://doi.org/10.48550/arxiv.2604.01378","title":"Residuals-based Offline Reinforcement Learning","display_name":"Residuals-based Offline Reinforcement Learning","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148729830","doi":"https://doi.org/10.48550/arxiv.2604.01378"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.01378","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01378","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.2604.01378","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132878772","display_name":"Qing Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5016835982","display_name":"Xian Yu","orcid":"https://orcid.org/0000-0003-1059-5303"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Xian","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":0.8827999830245972,"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":0.8827999830245972,"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/T12794","display_name":"Adaptive Dynamic Programming Control","score":0.035599999129772186,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.007300000172108412,"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":0.8841999769210815},{"id":"https://openalex.org/keywords/offline-learning","display_name":"Offline learning","score":0.5968000292778015},{"id":"https://openalex.org/keywords/temporal-difference-learning","display_name":"Temporal difference learning","score":0.5113000273704529},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.475600004196167},{"id":"https://openalex.org/keywords/fixed-point","display_name":"Fixed point","score":0.41530001163482666},{"id":"https://openalex.org/keywords/action-selection","display_name":"Action selection","score":0.40700000524520874},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.3871999979019165},{"id":"https://openalex.org/keywords/online-and-offline","display_name":"Online and offline","score":0.38530001044273376},{"id":"https://openalex.org/keywords/bellman-equation","display_name":"Bellman equation","score":0.37700000405311584}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8841999769210815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7067000269889832},{"id":"https://openalex.org/C2780490138","wikidata":"https://www.wikidata.org/wiki/Q7079636","display_name":"Offline learning","level":3,"score":0.5968000292778015},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.554099977016449},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.5113000273704529},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.475600004196167},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42899999022483826},{"id":"https://openalex.org/C61445026","wikidata":"https://www.wikidata.org/wiki/Q217608","display_name":"Fixed point","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C166109690","wikidata":"https://www.wikidata.org/wiki/Q4677422","display_name":"Action selection","level":3,"score":0.40700000524520874},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.40380001068115234},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C2780102126","wikidata":"https://www.wikidata.org/wiki/Q10928179","display_name":"Online and offline","level":2,"score":0.38530001044273376},{"id":"https://openalex.org/C14646407","wikidata":"https://www.wikidata.org/wiki/Q1430750","display_name":"Bellman equation","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.3504999876022339},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.3441999852657318},{"id":"https://openalex.org/C55524764","wikidata":"https://www.wikidata.org/wiki/Q515173","display_name":"Contraction mapping","level":3,"score":0.320499986410141},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C37404715","wikidata":"https://www.wikidata.org/wiki/Q380679","display_name":"Dynamic programming","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.01378","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01378","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.2604.01378","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01378","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Offline":[0],"reinforcement":[1],"learning":[2,9,84],"(RL)":[3],"has":[4,34],"received":[5],"increasing":[6],"attention":[7],"for":[8,64],"policies":[10],"from":[11,51],"previously":[12],"collected":[13],"data":[14,47],"without":[15],"interaction":[16],"with":[17],"the":[18,136],"real":[19],"environment,":[20,133],"which":[21,108],"is":[22,100,112],"particularly":[23],"important":[24],"in":[25,83],"high-stakes":[26],"applications.":[27],"While":[28],"a":[29,59,73,101,122,130],"growing":[30],"body":[31],"of":[32,138],"work":[33],"developed":[35],"offline":[36,61,124,141],"RL":[37,62],"algorithms,":[38],"these":[39],"methods":[40],"often":[41],"rely":[42],"on":[43],"restrictive":[44],"assumptions":[45],"about":[46],"coverage":[48],"and":[49,67,104,115],"suffer":[50],"distribution":[52],"shift.":[53],"In":[54],"this":[55,97],"paper,":[56],"we":[57,71,134],"propose":[58],"residuals-based":[60,74,123,140],"framework":[63],"general":[65],"state":[66],"action":[68],"spaces.":[69],"Specifically,":[70],"define":[72],"Bellman":[75,98],"optimality":[76],"operator":[77,99],"that":[78,96],"explicitly":[79],"incorporates":[80],"estimation":[81],"error":[82],"transition":[85],"dynamics":[86],"into":[87],"policy":[88],"optimization":[89],"by":[90],"leveraging":[91],"empirical":[92],"residuals.":[93],"We":[94,119],"show":[95],"contraction":[102],"mapping":[103],"identify":[105],"conditions":[106],"under":[107],"its":[109],"fixed":[110],"point":[111],"asymptotically":[113],"optimal":[114],"possesses":[116],"finite-sample":[117],"guarantees.":[118],"further":[120],"develop":[121],"deep":[125],"Q-learning":[126],"(DQN)":[127],"algorithm.":[128,143],"Using":[129],"stochastic":[131],"CartPole":[132],"demonstrate":[135],"effectiveness":[137],"our":[139],"DQN":[142]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-04T00:00:00"}
