{"id":"https://openalex.org/W7133552388","doi":"https://doi.org/10.48550/arxiv.2603.02628","title":"Post Hoc Extraction of Pareto Fronts for Continuous Control","display_name":"Post Hoc Extraction of Pareto Fronts for Continuous Control","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133552388","doi":"https://doi.org/10.48550/arxiv.2603.02628"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.02628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02628","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.02628","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118856128","display_name":"Raghav Thakar","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Thakar, Raghav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128074521","display_name":"Gaurav Dixit","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dixit, Gaurav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5084748531","display_name":"Kagan Tumer","orcid":"https://orcid.org/0009-0007-3809-7257"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tumer, Kagan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5118856128"],"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":0.5878999829292297,"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":0.5878999829292297,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.24940000474452972,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.02239999920129776,"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/pareto-principle","display_name":"Pareto principle","score":0.7361000180244446},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6435999870300293},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4641999900341034},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.444599986076355},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.41130000352859497},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.3921000063419342},{"id":"https://openalex.org/keywords/simplicity","display_name":"Simplicity","score":0.3709999918937683}],"concepts":[{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.7361000180244446},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6435999870300293},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6263999938964844},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.598800003528595},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4641999900341034},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C118127601","wikidata":"https://www.wikidata.org/wiki/Q3797610","display_name":"Pareto analysis","level":3,"score":0.3456000089645386},{"id":"https://openalex.org/C2778368411","wikidata":"https://www.wikidata.org/wiki/Q24662","display_name":"Retrofitting","level":2,"score":0.3450999855995178},{"id":"https://openalex.org/C38814450","wikidata":"https://www.wikidata.org/wiki/Q7136870","display_name":"Pareto interpolation","level":4,"score":0.3093000054359436},{"id":"https://openalex.org/C2986314615","wikidata":"https://www.wikidata.org/wiki/Q36829","display_name":"Pareto optimal","level":3,"score":0.2978000044822693},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.28949999809265137}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.02628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02628","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.02628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02628","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.9092172980308533,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Agents":[0],"in":[1,17,43],"the":[2,61,98,163,181,193,203],"real":[3],"world":[4],"must":[5,29],"often":[6,69],"balance":[7,153],"multiple":[8,38,154],"objectives,":[9],"such":[10],"as":[11],"speed,":[12],"stability,":[13],"and":[14,25,95,126,140,169,184],"energy":[15],"efficiency":[16],"continuous":[18],"control.":[19],"To":[20],"account":[21],"for":[22],"changing":[23],"conditions":[24],"preferences,":[26],"an":[27,110],"agent":[28],"ideally":[30],"learn":[31,92],"a":[32,51,72,79,116,136,142],"Pareto":[33,52,93,107,159],"frontier":[34,117],"of":[35,63,101,118,165,206],"policies":[36,119,151],"representing":[37],"optimal":[39],"trade-offs.":[40],"Recent":[41],"advances":[42],"multi-policy":[44],"multi-objective":[45,58,67,189],"reinforcement":[46],"learning":[47,50],"(MORL)":[48],"enable":[49],"front":[53,160],"directly,":[54],"but":[55],"require":[56],"full":[57],"consideration":[59],"from":[60,132],"start":[62],"training.":[64],"In":[65],"practice,":[66],"preferences":[68],"arise":[70],"after":[71],"policy":[73],"has":[74],"already":[75],"been":[76],"trained":[77],"on":[78,187],"single":[80],"specialised":[81],"objective.":[82],"Existing":[83],"MORL":[84,112,176],"methods":[85],"cannot":[86],"leverage":[87],"these":[88,172],"pre-trained":[89,122],"`specialists'":[90],"to":[91,148],"fronts":[94,200],"avoid":[96],"incurring":[97],"sample":[99,204],"costs":[100],"retraining.":[102],"We":[103,178],"introduce":[104],"Mixed":[105],"Advantage":[106],"Extraction":[108],"(MAPEX),":[109],"offline":[111],"method":[113],"that":[114,152],"constructs":[115],"by":[120],"reusing":[121],"specialist":[123,133],"policies,":[124,196],"critics,":[125],"replay":[127],"buffers.":[128],"MAPEX":[129,182,186,197],"combines":[130],"evaluations":[131],"critics":[134],"into":[135,174],"mixed":[137],"advantage":[138],"signal,":[139],"weights":[141],"behaviour":[143],"cloning":[144],"loss":[145],"with":[146],"it":[147],"train":[149],"new":[150],"objectives.":[155],"MAPEX's":[156],"post":[157],"hoc":[158],"extraction":[161],"preserves":[162],"simplicity":[164],"single-objective":[166],"off-policy":[167],"RL,":[168],"avoids":[170],"retrofitting":[171],"algorithms":[173],"complex":[175],"frameworks.":[177],"formally":[179],"describe":[180],"procedure":[183],"evaluate":[185],"five":[188],"MuJoCo":[190],"environments.":[191],"Given":[192],"same":[194],"starting":[195],"produces":[198],"comparable":[199],"at":[201],"$0.001\\%$":[202],"cost":[205],"established":[207],"baselines.":[208]},"counts_by_year":[],"updated_date":"2026-05-04T08:30:34.212998","created_date":"2026-03-05T00:00:00"}
