{"id":"https://openalex.org/W7160283971","doi":"https://doi.org/10.48550/arxiv.2605.01778","title":"Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms","display_name":"Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms","publication_year":2026,"publication_date":"2026-05-03","ids":{"openalex":"https://openalex.org/W7160283971","doi":"https://doi.org/10.48550/arxiv.2605.01778"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.01778","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01778","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.2605.01778","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135357959","display_name":"Tian Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135294253","display_name":"Zhilong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhilong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124015328","display_name":"Zexuan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zexuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135389442","display_name":"Ruishuo Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Ruishuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135412332","display_name":"Yihao Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135291360","display_name":"Yang Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yang","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":0.8740000128746033,"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":0.8740000128746033,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.016899999231100082,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.012299999594688416,"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/adversarial-system","display_name":"Adversarial system","score":0.7149999737739563},{"id":"https://openalex.org/keywords/sample-complexity","display_name":"Sample complexity","score":0.6172999739646912},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5764999985694885},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.5152999758720398},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4530999958515167},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44339999556541443},{"id":"https://openalex.org/keywords/time-complexity","display_name":"Time complexity","score":0.37049999833106995},{"id":"https://openalex.org/keywords/polynomial","display_name":"Polynomial","score":0.3653999865055084}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7149999737739563},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.671999990940094},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.6172999739646912},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5928999781608582},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5764999985694885},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.5152999758720398},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45820000767707825},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4530999958515167},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44339999556541443},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3783000111579895},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.3653999865055084},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.349700003862381},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.33079999685287476},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.304500013589859},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2603999972343445},{"id":"https://openalex.org/C2987595161","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Optimization algorithm","level":2,"score":0.25940001010894775}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.01778","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01778","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.2605.01778","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01778","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Adversarial":[0],"imitation":[1,8],"learning":[2,89,126],"(AIL),":[3],"a":[4,50,76,148],"prominent":[5],"approach":[6],"in":[7],"learning,":[9],"has":[10],"achieved":[11],"significant":[12],"practical":[13,46,149,162],"success":[14],"powered":[15],"by":[16,62],"neural":[17],"network":[18],"approximation.":[19,73,146],"However,":[20],"existing":[21],"theoretical":[22,65,111],"analyses":[23],"of":[24,67,132,157],"AIL":[25,69,81,141,173],"are":[26],"primarily":[27],"confined":[28],"to":[29],"simplified":[30],"settings,":[31],"such":[32],"as":[33],"tabular":[34],"and":[35,39,55,107,122],"linear":[36],"function":[37,72,145],"approximation,":[38],"involve":[40],"complex":[41],"algorithmic":[42],"designs":[43],"that":[44,114,167],"impede":[45],"implementation.":[47,163],"This":[48,57],"creates":[49],"substantial":[51],"gap":[52,61],"between":[53],"theory":[54],"practice.":[56],"paper":[58],"bridges":[59],"this":[60,98],"exploring":[63],"the":[64,130,137,154],"underpinnings":[66],"online":[68,85],"with":[70,91],"general":[71,144],"We":[74],"introduce":[75],"novel":[77],"framework":[78],"called":[79],"optimization-based":[80],"(OPT-AIL),":[82],"which":[83],"performs":[84],"optimization":[86,93,156],"for":[87,94,125],"reward":[88],"coupled":[90],"optimism-regularized":[92],"policy":[95],"learning.":[96],"Within":[97],"framework,":[99],"we":[100],"develop":[101],"two":[102,158],"concrete":[103],"methods:":[104],"model-free":[105],"OPT-AIL":[106,151,168],"model-based":[108],"OPT-AIL.":[109],"Our":[110],"analysis":[112],"demonstrates":[113],"both":[115],"variants":[116],"achieve":[117],"polynomial":[118],"expert":[119],"sample":[120],"complexity":[121,124],"interaction":[123],"near-expert":[127],"policies.":[128],"To":[129],"best":[131],"our":[133],"knowledge,":[134],"they":[135],"represent":[136],"first":[138],"provably":[139],"efficient":[140],"methods":[142,174],"under":[143],"From":[147],"standpoint,":[150],"requires":[152],"only":[153],"approximate":[155],"objectives,":[159],"thereby":[160],"facilitating":[161],"Empirical":[164],"studies":[165],"demonstrate":[166],"outperforms":[169],"previous":[170],"state-of-the-art":[171],"deep":[172],"across":[175],"several":[176],"challenging":[177],"tasks.":[178]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}