{"id":"https://openalex.org/W7117155878","doi":"https://doi.org/10.48550/arxiv.2512.18558","title":"Distributionally Robust Multi-Agent Reinforcement Learning for Intelligent Traffic Control","display_name":"Distributionally Robust Multi-Agent Reinforcement Learning for Intelligent Traffic Control","publication_year":2025,"publication_date":"2025-12-21","ids":{"openalex":"https://openalex.org/W7117155878","doi":"https://doi.org/10.48550/arxiv.2512.18558"},"language":"en","primary_location":{"id":"pmh:oai:pure.rug.nl:openaire_cris_publications/93efbb4f-b0fe-4619-9b5c-60b722411463","is_oa":false,"landing_page_url":"https://hdl.handle.net/11370/93efbb4f-b0fe-4619-9b5c-60b722411463","pdf_url":null,"source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Pei, S, Borger, J, Kosay, A, Sayin, M O & Ahmed, S 2025 'Distributionally Robust Multi-Agent Reinforcement Learning for Intelligent Traffic Control' arXiv. https://doi.org/10.48550/arXiv.2512.18558","raw_type":"info:eu-repo/semantics/preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2512.18558","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121146960","display_name":"Shuwei Pei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pei, Shuwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121178250","display_name":"Joran Borger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Borger, Joran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115955504","display_name":"Arda Kosay","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kosay, Arda","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081851582","display_name":"Muhammed O. Sayin","orcid":"https://orcid.org/0000-0001-5779-3986"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sayin, Muhammed O.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5121216301","display_name":"Saeed Ahmed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmed, Saeed","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":0.5753999948501587,"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":0.5753999948501587,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.24580000340938568,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.02280000038444996,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social 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":0.8090999722480774},{"id":"https://openalex.org/keywords/controller","display_name":"Controller (irrigation)","score":0.5882999897003174},{"id":"https://openalex.org/keywords/queue","display_name":"Queue","score":0.5781000256538391},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.54830002784729},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5267999768257141},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4449000060558319},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.44279998540878296},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4226999878883362}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8090999722480774},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7179999947547913},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.5882999897003174},{"id":"https://openalex.org/C160403385","wikidata":"https://www.wikidata.org/wiki/Q220543","display_name":"Queue","level":2,"score":0.5781000256538391},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.54830002784729},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5267999768257141},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4226999878883362},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.4034999907016754},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3725000023841858},{"id":"https://openalex.org/C31531917","wikidata":"https://www.wikidata.org/wiki/Q915157","display_name":"Robust control","level":3,"score":0.36489999294281006},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C22684755","wikidata":"https://www.wikidata.org/wiki/Q847526","display_name":"Queueing theory","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3188999891281128},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3165999948978424},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.2921999990940094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28929999470710754},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C205875254","wikidata":"https://www.wikidata.org/wiki/Q17156857","display_name":"Decentralised system","level":3,"score":0.25450000166893005}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:pure.rug.nl:openaire_cris_publications/93efbb4f-b0fe-4619-9b5c-60b722411463","is_oa":false,"landing_page_url":"https://hdl.handle.net/11370/93efbb4f-b0fe-4619-9b5c-60b722411463","pdf_url":null,"source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Pei, S, Borger, J, Kosay, A, Sayin, M O & Ahmed, S 2025 'Distributionally Robust Multi-Agent Reinforcement Learning for Intelligent Traffic Control' arXiv. https://doi.org/10.48550/arXiv.2512.18558","raw_type":"info:eu-repo/semantics/preprint"},{"id":"pmh:oai:pure.rug.nl:publications/93efbb4f-b0fe-4619-9b5c-60b722411463","is_oa":false,"landing_page_url":"https://research.rug.nl/en/publications/93efbb4f-b0fe-4619-9b5c-60b722411463","pdf_url":null,"source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Pei, S, Borger, J, Kosay, A, Sayin, M O & Ahmed, S 2025 'Distributionally Robust Multi-Agent Reinforcement Learning for Intelligent Traffic Control' arXiv. https://doi.org/10.48550/arXiv.2512.18558","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2512.18558","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.18558","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.2512.18558","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.18558","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":[{"id":"https://metadata.un.org/sdg/11","score":0.4909552037715912,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Learning-based":[0],"traffic":[1,24,112],"signal":[2,39,93],"control":[3,40],"is":[4,83],"typically":[5],"optimized":[6],"for":[7,38],"average":[8,213],"performance":[9],"under":[10,22,168],"a":[11,31,42,48,74,86,96,123,138,175],"few":[12],"nominal":[13],"demand":[14,105,130,149],"patterns,":[15],"which":[16,80],"can":[17],"result":[18],"in":[19,68,79],"poor":[20],"behavior":[21],"atypical":[23],"conditions.":[25],"To":[26],"address":[27],"this,":[28],"we":[29,72,107,136,160],"develop":[30],"distributionally":[32,176,201],"robust":[33,177,202],"multi-agent":[34,76,164,178,203],"reinforcement":[35,165,179,204],"learning":[36,166,180,205],"framework":[37],"on":[41,152,189,195,229],"3x3":[43,50],"urban":[44],"grid":[45],"calibrated":[46],"from":[47,116],"contiguous":[49],"subarea":[51],"of":[52,126],"central":[53],"Athens":[54],"covered":[55],"by":[56,85],"the":[57,157,162,196,200,217,230],"pNEUMA":[58,117],"trajectory":[59],"dataset":[60],"(Barmpounakis":[61],"and":[62,118,128,211,225],"Geroliminis,":[63],"2020).":[64],"Our":[65],"approach":[66],"proceeds":[67],"three":[69],"stages.":[70],"First,":[71],"train":[73,137],"baseline":[75,163],"RL":[77],"controller":[78,158,206],"each":[81],"intersection":[82],"governed":[84],"proximal":[87],"policy":[88],"optimization":[89],"agent":[90],"with":[91],"discrete":[92],"phases,":[94],"using":[95],"centralized":[97],"training,":[98],"decentralized":[99],"execution":[100],"paradigm.":[101],"Second,":[102],"to":[103,146,173,216,221],"capture":[104],"uncertainty,":[106],"construct":[108],"eight":[109,184],"heterogeneous":[110],"origin-destination-based":[111],"scenarios-one":[113],"directly":[114],"derived":[115],"seven":[119],"synthetically":[120],"generated-to":[121],"span":[122],"wide":[124],"range":[125],"spatial":[127],"temporal":[129],"patterns.":[131],"Over":[132],"this":[133],"scenario":[134],"set,":[135],"contextual-bandit":[139],"worst-case":[140,171],"estimator":[141],"that":[142],"assigns":[143],"mixture":[144],"weights":[145],"estimate":[147],"adversarial":[148],"distributions":[150],"conditioned":[151],"context.":[153],"Finally,":[154],"without":[155],"modifying":[156],"architecture,":[159],"fine-tune":[161],"agents":[167],"these":[169],"estimated":[170],"mixtures":[172],"obtain":[174],"controller.":[181],"Across":[182],"all":[183],"scenarios,":[185],"as":[186,188],"well":[187],"an":[190],"unseen":[191],"validation":[192],"network":[193],"based":[194],"Sioux":[197],"Falls":[198],"configuration,":[199],"consistently":[207],"reduces":[208],"horizon-averaged":[209],"queues":[210,224],"increases":[212],"speeds":[214,228],"relative":[215],"baseline,":[218],"achieving":[219],"up":[220],"51%":[222],"shorter":[223],"38%":[226],"higher":[227],"worst-performing":[231],"scenarios.":[232]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-12-24T00:00:00"}
