{"id":"https://openalex.org/W4415881580","doi":"https://doi.org/10.48550/arxiv.2510.15495","title":"OffSim: Offline Simulator for Model-based Offline Inverse Reinforcement Learning","display_name":"OffSim: Offline Simulator for Model-based Offline Inverse Reinforcement Learning","publication_year":2025,"publication_date":"2025-10-17","ids":{"openalex":"https://openalex.org/W4415881580","doi":"https://doi.org/10.48550/arxiv.2510.15495"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2510.15495","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.15495","pdf_url":"https://arxiv.org/pdf/2510.15495","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.15495","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081937055","display_name":"Woo Jin Ahn","orcid":"https://orcid.org/0000-0002-8261-8132"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ahn, Woo-Jin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101904094","display_name":"Seung Woo Baek","orcid":"https://orcid.org/0000-0001-8065-0887"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baek, Sang-Ryul","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100656213","display_name":"Yongjun Lee","orcid":"https://orcid.org/0000-0002-4780-5821"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Yong-Jun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015493373","display_name":"Hyun Duck Choi","orcid":"https://orcid.org/0000-0003-2811-4870"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Hyun-Duck","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5079659183","display_name":"Myo Taeg Lim","orcid":"https://orcid.org/0000-0003-2990-8066"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Myo-Taeg","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5081937055"],"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":0.8799999952316284,"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":0.8799999952316284,"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":0.016100000590085983,"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":0.008200000040233135,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7836999893188477},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5339000225067139},{"id":"https://openalex.org/keywords/offline-learning","display_name":"Offline learning","score":0.4214000105857849},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.39910000562667847},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.39320001006126404},{"id":"https://openalex.org/keywords/online-and-offline","display_name":"Online and offline","score":0.3431999981403351}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7836999893188477},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7591000199317932},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5403000116348267},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5339000225067139},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4431000053882599},{"id":"https://openalex.org/C2780490138","wikidata":"https://www.wikidata.org/wiki/Q7079636","display_name":"Offline learning","level":3,"score":0.4214000105857849},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C2780102126","wikidata":"https://www.wikidata.org/wiki/Q10928179","display_name":"Online and offline","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3190000057220459},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.27720001339912415},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.2538999915122986}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.15495","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.15495","pdf_url":"https://arxiv.org/pdf/2510.15495","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.15495","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.15495","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:oai:arXiv.org:2510.15495","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.15495","pdf_url":"https://arxiv.org/pdf/2510.15495","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Reinforcement":[0],"learning":[1,47],"algorithms":[2],"typically":[3],"utilize":[4],"an":[5,37,70,107],"interactive":[6],"simulator":[7],"(i.e.,":[8],"environment)":[9],"with":[10,99],"a":[11,41,65,93,111],"predefined":[12],"reward":[13,24,55,72,113],"function":[14,73],"for":[15,114],"policy":[16,94],"training.":[17],"Developing":[18],"such":[19],"simulators":[20],"and":[21,30,54,69,77,138],"manually":[22],"defining":[23],"functions,":[25],"however,":[26],"is":[27],"often":[28],"time-consuming":[29],"labor-intensive.":[31],"To":[32],"address":[33],"this,":[34],"we":[35,104],"propose":[36],"Offline":[38],"Simulator":[39],"(OffSim),":[40],"novel":[42],"model-based":[43],"offline":[44,95,132],"inverse":[45],"reinforcement":[46],"(IRL)":[48],"framework,":[49],"to":[50,74,117],"emulate":[51],"environmental":[52],"dynamics":[53],"structure":[56],"directly":[57],"from":[58],"expert-generated":[59],"state-action":[60],"trajectories.":[61],"OffSim":[62,89,125],"jointly":[63],"optimizes":[64],"high-entropy":[66],"transition":[67],"model":[68],"IRL-based":[71],"enhance":[75,118],"exploration":[76],"improve":[78],"the":[79,82,100],"generalizability":[80],"of":[81],"learned":[83,87],"reward.":[84],"Leveraging":[85],"these":[86],"components,":[88],"can":[90],"subsequently":[91],"train":[92],"without":[96],"further":[97],"interaction":[98],"real":[101],"environment.":[102],"Additionally,":[103],"introduce":[105],"OffSim$^+$,":[106],"extension":[108],"that":[109,124],"incorporates":[110],"marginal":[112],"multi-dataset":[115],"settings":[116],"exploration.":[119],"Extensive":[120],"MuJoCo":[121],"experiments":[122],"demonstrate":[123],"achieves":[126],"substantial":[127],"performance":[128],"gains":[129],"over":[130],"existing":[131],"IRL":[133],"methods,":[134],"confirming":[135],"its":[136],"efficacy":[137],"robustness.":[139]},"counts_by_year":[],"updated_date":"2026-03-28T08:17:26.163206","created_date":"2025-10-21T00:00:00"}