{"id":"https://openalex.org/W2782748722","doi":"https://doi.org/10.1109/tnnls.2017.2762720","title":"Motivated Optimal Developmental Learning for Sequential Tasks Without Using Rigid Time-Discounts","display_name":"Motivated Optimal Developmental Learning for Sequential Tasks Without Using Rigid Time-Discounts","publication_year":2018,"publication_date":"2018-01-11","ids":{"openalex":"https://openalex.org/W2782748722","doi":"https://doi.org/10.1109/tnnls.2017.2762720","mag":"2782748722","pmid":"https://pubmed.ncbi.nlm.nih.gov/29994173"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2017.2762720","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2017.2762720","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5003077831","display_name":"Dongshu Wang","orcid":"https://orcid.org/0000-0003-3145-4956"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dongshu Wang","raw_affiliation_strings":["Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019579988","display_name":"Yihai Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yihai Duan","raw_affiliation_strings":["Zhengzhou Yunhai Information Technology Company Ltd., Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhengzhou Yunhai Information Technology Company Ltd., Zhengzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102851268","display_name":"Juyang Weng","orcid":"https://orcid.org/0000-0003-1383-3872"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juyang Weng","raw_affiliation_strings":["MSU Neuroscience Program, East Lansing, MI, USA"],"affiliations":[{"raw_affiliation_string":"MSU Neuroscience Program, East Lansing, MI, USA","institution_ids":["https://openalex.org/I87216513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5003077831"],"corresponding_institution_ids":["https://openalex.org/I87216513"],"apc_list":null,"apc_paid":null,"fwci":2.469,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.88817309,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"29","issue":"10","first_page":"4917","last_page":"4931"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9991999864578247,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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":0.7992105484008789},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6538187861442566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6247950792312622},{"id":"https://openalex.org/keywords/temporal-difference-learning","display_name":"Temporal difference learning","score":0.5618926882743835},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4987924098968506},{"id":"https://openalex.org/keywords/sequence-learning","display_name":"Sequence learning","score":0.4846120774745941},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.4331081211566925}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7992105484008789},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6538187861442566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6247950792312622},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.5618926882743835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4987924098968506},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.4846120774745941},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.4331081211566925}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2017.2762720","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2017.2762720","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:29994173","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29994173","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G6984361360","display_name":null,"funder_award_id":"61174085","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W32403112","https://openalex.org/W274985342","https://openalex.org/W564663045","https://openalex.org/W1562462135","https://openalex.org/W1605444579","https://openalex.org/W1784034050","https://openalex.org/W1967085570","https://openalex.org/W1970127232","https://openalex.org/W1998172110","https://openalex.org/W2009646610","https://openalex.org/W2014318402","https://openalex.org/W2018500972","https://openalex.org/W2018930571","https://openalex.org/W2020278240","https://openalex.org/W2024532043","https://openalex.org/W2025797408","https://openalex.org/W2033607127","https://openalex.org/W2043615441","https://openalex.org/W2049765399","https://openalex.org/W2073320424","https://openalex.org/W2076063813","https://openalex.org/W2101524054","https://openalex.org/W2107726111","https://openalex.org/W2112796928","https://openalex.org/W2114524506","https://openalex.org/W2116085129","https://openalex.org/W2120536063","https://openalex.org/W2122410182","https://openalex.org/W2123408238","https://openalex.org/W2126891624","https://openalex.org/W2130690456","https://openalex.org/W2138518908","https://openalex.org/W2144982973","https://openalex.org/W2145339207","https://openalex.org/W2146871603","https://openalex.org/W2150330761","https://openalex.org/W2159261248","https://openalex.org/W2162608577","https://openalex.org/W2163605009","https://openalex.org/W2164424353","https://openalex.org/W2165626265","https://openalex.org/W2170302354","https://openalex.org/W2185417612","https://openalex.org/W2189914368","https://openalex.org/W2545941203","https://openalex.org/W4407063603","https://openalex.org/W6638181300","https://openalex.org/W6641756242","https://openalex.org/W6652504093","https://openalex.org/W6683634557","https://openalex.org/W6683912680","https://openalex.org/W6684191040","https://openalex.org/W6686542590","https://openalex.org/W6729479854"],"related_works":["https://openalex.org/W4400868993","https://openalex.org/W2145363145","https://openalex.org/W2341346307","https://openalex.org/W2154399718","https://openalex.org/W2768629321","https://openalex.org/W4384574988","https://openalex.org/W2130711276","https://openalex.org/W4321463377","https://openalex.org/W4308828368","https://openalex.org/W1528400370"],"abstract_inverted_index":{"Many":[0],"methods":[1],"for":[2,33,39,186,221],"reinforcement":[3,31],"learning":[4,32,106,156,185],"use":[5,11],"symbolic":[6,18],"representations-nonemergent-such":[7],"as":[8,198],"Q-learning.":[9,133],"We":[10,98],"emergent":[12,37,228],"representations":[13],"here,":[14],"without":[15],"human":[16],"handcrafted":[17],"states":[19],"(i.e.,":[20],"each":[21],"state":[22],"corresponds":[23],"to":[24,108],"a":[25,65,77,143,165],"different":[26,54],"location).":[27],"This":[28,124],"paper":[29],"models":[30],"hidden":[34,217],"neurons":[35,218],"in":[36,53,76,132,142,224],"networks":[38],"sequential":[40,48,137,187,222],"tasks.":[41],"In":[42],"this":[43,101,201],"paper,":[44],"their":[45],"influences":[46,209],"on":[47,68,216],"tasks":[49,223],"(e.g.,":[50,88,93],"robot":[51],"navigation":[52],"scenarios)":[55],"are":[56,193],"investigated":[57],"where":[58,117],"the":[59,71,83,109,112,128,147,150,178,203,208],"learned":[60,95],"value":[61],"and":[62,91,119,154,170,180,214],"results":[63,175],"of":[64,85,104,111,130,149,168,210],"behavior":[66],"rely":[67],"not":[69,190],"only":[70],"current":[72],"experience":[73,157],"just":[74],"like":[75],"pattern":[78],"recognition":[79],"(episodic)":[80],"but":[81],"also":[82],"prediction":[84],"future":[86],"experiences":[87],"delayed":[89],"rewards)":[90],"environments":[92],"previously":[94],"navigational":[96],"trajectories).":[97],"show":[99],"that":[100,177,206],"new":[102,125],"model":[103],"motivated":[105],"amounts":[107],"computation":[110],"maximum-likelihood":[113],"estimate":[114],"through":[115],"\"life\"":[116],"punishment":[118],"reward":[120],"have":[121],"increased":[122],"weights.":[123],"formulation":[126],"avoids":[127],"greediness":[129],"time-discount":[131],"Its":[134],"complex":[135],"nonlinear":[136],"optimization":[138],"has":[139],"been":[140],"solved":[141],"closed-form":[144],"procedure":[145],"under":[146],"condition":[148],"limited":[151,155],"computational":[152],"resources":[153],"so":[158],"far,":[159],"because":[160,189],"we":[161,199],"convert":[162],"it":[163],"into":[164],"simpler":[166],"problem":[167],"incremental":[169],"linear":[171],"estimation.":[172],"The":[173],"experimental":[174],"showed":[176],"serotonin":[179,213],"dopamine":[181],"systems":[182],"speed":[183],"up":[184],"tasks,":[188],"all":[191],"events":[192],"equally":[194],"important.":[195],"As":[196],"far":[197],"know,":[200],"is":[202],"first":[204],"work":[205],"studies":[207],"reinforcers":[211],"(via":[212],"dopamine)":[215],"(Y":[219],"neurons)":[220],"dynamic":[225],"scenarios":[226],"using":[227],"representations.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
