{"id":"https://openalex.org/W7139937125","doi":"https://doi.org/10.48550/arxiv.2603.18396","title":"RE-SAC: Disentangling aleatoric and epistemic risks in bus fleet control: A stable and robust ensemble DRL approach","display_name":"RE-SAC: Disentangling aleatoric and epistemic risks in bus fleet control: A stable and robust ensemble DRL approach","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7139937125","doi":"https://doi.org/10.48550/arxiv.2603.18396"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.18396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18396","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.18396","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130238605","display_name":"Yifan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130227279","display_name":"Liang Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Liang","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/T10524","display_name":"Traffic control and management","score":0.7549999952316284,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.7549999952316284,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.10170000046491623,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.0210999995470047,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5475000143051147},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.4032000005245209},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.40059998631477356},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.39250001311302185},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.3675000071525574},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3450999855995178},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.31619998812675476},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.31349998712539673}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5475000143051147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5115000009536743},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.40059998631477356},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.382099986076355},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3675000071525574},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3474999964237213},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3450999855995178},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3431999981403351},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31769999861717224},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.29100000858306885},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C31531917","wikidata":"https://www.wikidata.org/wiki/Q915157","display_name":"Robust control","level":3,"score":0.2563999891281128},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.2540999948978424},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.18396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18396","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2603.18396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18396","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Bus":[0],"holding":[1],"control":[2],"is":[3,35],"challenging":[4],"due":[5],"to":[6,58,77,90,94,170,187],"stochastic":[7],"traffic":[8,203],"and":[9,46],"passenger":[10],"demand.":[11],"While":[12],"deep":[13],"reinforcement":[14],"learning":[15],"(DRL)":[16],"shows":[17],"promise,":[18],"standard":[19],"actor-critic":[20,74],"algorithms":[21],"suffer":[22],"from":[23,136],"Q-value":[24,182],"instability":[25,34],"in":[26,61,125,147,152,189],"volatile":[27],"environments.":[28],"A":[29],"key":[30],"source":[31],"of":[32,38,194],"this":[33],"the":[36,91,106,133,163],"conflation":[37],"two":[39],"distinct":[40],"uncertainties:":[41],"aleatoric":[42,97],"uncertainty":[43,48],"(irreducible":[44],"noise)":[45],"epistemic":[47,116],"(data":[49],"insufficiency).":[50],"Treating":[51],"these":[52,80],"as":[53,139],"a":[54,70,100,118,140,143,153],"single":[55],"risk":[56],"leads":[57],"value":[59,123],"underestimation":[60],"noisy":[62],"states,":[63],"causing":[64],"catastrophic":[65],"policy":[66],"collapse.":[67],"We":[68],"propose":[69],"robust":[71,107],"ensemble":[72,134],"soft":[73],"(RE-SAC)":[75],"framework":[76],"explicitly":[78],"disentangle":[79],"uncertainties.":[81],"RE-SAC":[82,161,179],"applies":[83],"Integral":[84],"Probability":[85],"Metric":[86],"(IPM)-based":[87],"weight":[88],"regularization":[89],"critic":[92],"network":[93],"hedge":[95],"against":[96],"risk,":[98,117],"providing":[99],"smooth":[101],"analytical":[102],"lower":[103],"bound":[104],"for":[105],"Bellman":[108],"operator":[109],"without":[110],"expensive":[111],"inner-loop":[112],"perturbations.":[113],"To":[114],"address":[115],"diversified":[119],"Q-ensemble":[120],"penalizes":[121],"overconfident":[122],"estimates":[124],"sparsely":[126],"covered":[127],"regions.":[128],"This":[129],"dual":[130],"mechanism":[131],"prevents":[132],"variance":[135],"misidentifying":[137],"noise":[138],"data":[141],"gap,":[142],"failure":[144],"mode":[145],"identified":[146],"our":[148],"ablation":[149],"study.":[150],"Experiments":[151],"realistic":[154],"bidirectional":[155],"bus":[156],"corridor":[157],"simulation":[158],"demonstrate":[159],"that":[160,178],"achieves":[162],"highest":[164],"cumulative":[165],"reward":[166],"(approx.":[167],"-0.4e6)":[168],"compared":[169],"vanilla":[171],"SAC":[172],"(-0.55e6).":[173],"Mahalanobis":[174],"rareness":[175],"analysis":[176],"confirms":[177],"reduces":[180],"Oracle":[181],"estimation":[183],"error":[184],"by":[185],"up":[186],"62%":[188],"rare":[190],"out-of-distribution":[191],"states":[192],"(MAE":[193],"1647":[195],"vs.":[196],"4343),":[197],"demonstrating":[198],"superior":[199],"robustness":[200],"under":[201],"high":[202],"variability.":[204]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-21T00:00:00"}
