{"id":"https://openalex.org/W7161968403","doi":"https://doi.org/10.48550/arxiv.2605.21214","title":"Behavior-Consistent Deep Reinforcement Learning","display_name":"Behavior-Consistent Deep Reinforcement Learning","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7161968403","doi":"https://doi.org/10.48550/arxiv.2605.21214"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21214","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21214","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.2605.21214","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090201614","display_name":"Marcel Hussing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hussing, Marcel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136705279","display_name":"Liv G. d'Aliberti","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"d'Aliberti, Liv G.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136702850","display_name":"Claas Voelcker","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Voelcker, Claas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136685724","display_name":"Benjamin Eysenbach","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eysenbach, Benjamin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136632050","display_name":"Eric Eaton","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eaton, Eric","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":0.8295000195503235,"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":0.8295000195503235,"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":0.03440000116825104,"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":0.026599999517202377,"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":0.720300018787384},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.6628999710083008},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6340000033378601},{"id":"https://openalex.org/keywords/anchoring","display_name":"Anchoring","score":0.5737000107765198},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5228000283241272},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.48170000314712524},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.43720000982284546}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.720300018787384},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.6628999710083008},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6340000033378601},{"id":"https://openalex.org/C18483071","wikidata":"https://www.wikidata.org/wiki/Q168432","display_name":"Anchoring","level":2,"score":0.5737000107765198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5455999970436096},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5228000283241272},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48350000381469727},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.48170000314712524},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.43720000982284546},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.399399995803833},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.3474999964237213},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.326200008392334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30559998750686646},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.2865000069141388},{"id":"https://openalex.org/C62644790","wikidata":"https://www.wikidata.org/wiki/Q3454689","display_name":"Variance reduction","level":3,"score":0.2669999897480011},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26429998874664307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21214","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21214","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.2605.21214","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21214","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":{"Reinforcement":[0],"learning":[1],"(RL)":[2],"often":[3],"exhibits":[4],"high":[5],"variance":[6,172],"across":[7,56,149],"training":[8,57],"runs,":[9],"leading":[10],"to":[11,19,46,77,92],"unreliable":[12],"performance":[13],"and":[14,53],"posing":[15],"a":[16,67,78,130,139,167],"major":[17],"challenge":[18,30],"deployment":[20],"in":[21,166,170],"real-world":[22],"domains.":[23],"In":[24],"this":[25],"work,":[26],"we":[27,105,124,146],"address":[28],"the":[29,37,43,89,96,101],"of":[31,39,160],"cross-run":[32,143],"policy":[33,114],"divergence":[34,73,99,156],"by":[35,74,157],"formalizing":[36],"problem":[38],"behavior-consistent":[40],"RL,":[41],"where":[42],"objective":[44],"is":[45,62],"obtain":[47],"policies":[48],"that":[49,63,108,134,148],"are":[50],"both":[51],"high-performing":[52],"distributionally":[54],"similar":[55],"runs.":[58],"Our":[59],"key":[60],"observation":[61],"maximum-entropy":[64],"RL":[65],"provides":[66],"direct":[68],"mechanism":[69],"for":[70,85,142],"controlling":[71],"behavioral":[72],"anchoring":[75],"runs":[76],"common":[79],"(uniform)":[80],"prior.":[81],"We":[82],"prove":[83],"that,":[84],"Boltzmann":[86],"policies,":[87],"choosing":[88],"temperature":[90,132],"proportional":[91],"$Q$-function":[93],"disagreement":[94,137],"bounds":[95],"pairwise":[97],"KL":[98],"between":[100],"induced":[102],"policies.":[103],"However,":[104],"also":[106],"show":[107],"na\u00efvely":[109],"increasing":[110],"entropy":[111],"might":[112],"impair":[113],"optimization":[115],"while":[116],"amplifying":[117],"off-policy":[118],"error.":[119],"Building":[120],"upon":[121],"these":[122],"observations,":[123],"propose":[125],"$Q$-value":[126],"Expectile":[127],"Disagreement":[128],"(QED),":[129],"state-dependent":[131],"schedule":[133],"uses":[135],"double-critic":[136],"as":[138],"single-run":[140],"proxy":[141],"disagreement.":[144],"Empirically,":[145],"demonstrate":[147],"18":[150],"continuous-control":[151],"tasks,":[152],"QED":[153],"reduces":[154],"across-run":[155],"two":[158],"orders":[159],"magnitude":[161],"without":[162],"sacrificing":[163],"performance,":[164],"resulting":[165],"considerable":[168],"reduction":[169],"return":[171],"at":[173],"modest":[174],"sample-efficiency":[175],"costs.":[176]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-22T00:00:00"}