{"id":"https://openalex.org/W2897522585","doi":"https://doi.org/10.1109/ivs.2018.8500603","title":"Automatically Generated Curriculum based Reinforcement Learning for Autonomous Vehicles in Urban Environment","display_name":"Automatically Generated Curriculum based Reinforcement Learning for Autonomous Vehicles in Urban Environment","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2897522585","doi":"https://doi.org/10.1109/ivs.2018.8500603","mag":"2897522585"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2018.8500603","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2018.8500603","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073672179","display_name":"Zhiqian Qiao","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiqian Qiao","raw_affiliation_strings":["Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040655097","display_name":"Katharina Muelling","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Katharina Muelling","raw_affiliation_strings":["The Robotics Institute, Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Robotics Institute, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015076162","display_name":"John M. Dolan","orcid":"https://orcid.org/0000-0003-2062-100X"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John M. Dolan","raw_affiliation_strings":["The Robotics Institute, Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Robotics Institute, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004972470","display_name":"Praveen Palanisamy","orcid":"https://orcid.org/0000-0001-9069-3071"},"institutions":[{"id":"https://openalex.org/I118136607","display_name":"General Motors (United States)","ror":"https://ror.org/05addee68","country_code":"US","type":"company","lineage":["https://openalex.org/I118136607"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Praveen Palanisamy","raw_affiliation_strings":["Research & Development, General Motors, Warren, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research & Development, General Motors, Warren, MI, USA","institution_ids":["https://openalex.org/I118136607"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088286364","display_name":"Priyantha Mudalige","orcid":null},"institutions":[{"id":"https://openalex.org/I118136607","display_name":"General Motors (United States)","ror":"https://ror.org/05addee68","country_code":"US","type":"company","lineage":["https://openalex.org/I118136607"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Priyantha Mudalige","raw_affiliation_strings":["Research & Development, General Motors, Warren, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research & Development, General Motors, Warren, MI, USA","institution_ids":["https://openalex.org/I118136607"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.3954,"has_fulltext":false,"cited_by_count":71,"citation_normalized_percentile":{"value":0.9469142,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1233","last_page":"1238"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9986000061035156,"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":0.9986000061035156,"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":0.9984999895095825,"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"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9908000230789185,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8938303589820862},{"id":"https://openalex.org/keywords/curriculum","display_name":"Curriculum","score":0.8285132646560669},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7589601278305054},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.72311931848526},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6183130741119385},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6079002618789673},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5546664595603943},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5352042317390442},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42194488644599915},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.3654520511627197},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15127336978912354},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.12344253063201904}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8938303589820862},{"id":"https://openalex.org/C47177190","wikidata":"https://www.wikidata.org/wiki/Q207137","display_name":"Curriculum","level":2,"score":0.8285132646560669},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7589601278305054},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.72311931848526},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6183130741119385},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6079002618789673},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5546664595603943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5352042317390442},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42194488644599915},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3654520511627197},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15127336978912354},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.12344253063201904},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2018.8500603","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2018.8500603","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8299999833106995}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W648786980","https://openalex.org/W1757796397","https://openalex.org/W2016151598","https://openalex.org/W2048226872","https://openalex.org/W2055501135","https://openalex.org/W2061562262","https://openalex.org/W2111299724","https://openalex.org/W2120581524","https://openalex.org/W2173248099","https://openalex.org/W2257979135","https://openalex.org/W2296073425","https://openalex.org/W2312609093","https://openalex.org/W2411577903","https://openalex.org/W2527692905","https://openalex.org/W2581240229","https://openalex.org/W2610757271","https://openalex.org/W2737215407","https://openalex.org/W2739770424","https://openalex.org/W2766447205","https://openalex.org/W2793154907","https://openalex.org/W2963262099","https://openalex.org/W2963311874","https://openalex.org/W2972758308","https://openalex.org/W4214717370","https://openalex.org/W4245108548","https://openalex.org/W4297580293","https://openalex.org/W4298857966","https://openalex.org/W6676416636","https://openalex.org/W6703271639"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W4362501864","https://openalex.org/W4306904969","https://openalex.org/W2233261550","https://openalex.org/W4380318855","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2138720691","https://openalex.org/W2997094352","https://openalex.org/W2031695474"],"abstract_inverted_index":{"We":[0,109],"address":[1],"the":[2,36,42,57,63,66,97,100,111,114,133],"problem":[3],"of":[4,44,65,107,113,122],"learning":[5,15,51],"autonomous":[6,25],"driving":[7],"behaviors":[8],"in":[9,31],"urban":[10,85],"intersections":[11],"using":[12,88],"deep":[13],"reinforcement":[14],"(DRL).":[16],"DRL":[17,89],"has":[18,52],"become":[19,39],"a":[20,82,92,104],"popular":[21],"choice":[22],"for":[23,84,99],"creating":[24,69],"agents":[26],"due":[27],"to":[28,55,94,120],"its":[29],"success":[30],"various":[32],"tasks.":[33,108],"However,":[34],"as":[35],"problems":[37],"tackled":[38],"more":[40],"complex,":[41],"number":[43],"training":[45,59,101,119,134],"iterations":[46],"necessary":[47],"increase":[48],"drastically.":[49],"Curriculum":[50],"been":[53],"shown":[54],"reduce":[56,132],"required":[58],"time":[60,135],"and":[61,90,126],"improve":[62],"performance":[64,112],"agent,":[67],"but":[68],"an":[70],"optimal":[71],"curriculum":[72,98,117],"often":[73],"requires":[74],"human":[75],"handcrafting.":[76],"In":[77],"this":[78],"work,":[79],"we":[80],"learn":[81],"policy":[83],"intersection":[86],"crossing":[87],"introduce":[91],"method":[93],"automatically":[95,115],"generate":[96],"process":[102],"from":[103],"candidate":[105],"set":[106],"compare":[110],"generated":[116,124],"(AGC)":[118],"those":[121],"randomly":[123],"sequences":[125],"show":[127],"that":[128],"AGC":[129],"can":[130],"significantly":[131],"while":[136],"achieving":[137],"similar":[138],"or":[139],"better":[140],"performance.":[141]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
