{"id":"https://openalex.org/W4390092410","doi":"https://doi.org/10.48550/arxiv.2312.12972","title":"From Past to Future: Rethinking Eligibility Traces","display_name":"From Past to Future: Rethinking Eligibility Traces","publication_year":2023,"publication_date":"2023-12-20","ids":{"openalex":"https://openalex.org/W4390092410","doi":"https://doi.org/10.48550/arxiv.2312.12972"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2312.12972","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.12972","pdf_url":"https://arxiv.org/pdf/2312.12972","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2312.12972","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050879301","display_name":"Dhawal Gupta","orcid":"https://orcid.org/0000-0002-2486-866X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gupta, Dhawal","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102155330","display_name":"Scott M. Jordan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jordan, Scott M.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091446143","display_name":"Shreyas Chaudhari","orcid":"https://orcid.org/0000-0002-8826-2253"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chaudhari, Shreyas","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108888223","display_name":"Bo Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Bo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066332280","display_name":"Philip S. Thomas","orcid":"https://orcid.org/0000-0002-9904-1800"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomas, Philip S.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5034701757","display_name":"Bruno Castro da Silva","orcid":"https://orcid.org/0000-0002-3708-5728"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"da Silva, Bruno Castro","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5050879301"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.951200008392334,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.951200008392334,"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/T11496","display_name":"Credit Risk and Financial Regulations","score":0.9490000009536743,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9453999996185303,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.7161555886268616},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.6878511905670166},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.6813610196113586},{"id":"https://openalex.org/keywords/bellman-equation","display_name":"Bellman equation","score":0.5608425140380859},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5563595294952393},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4958880841732025},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4669433832168579},{"id":"https://openalex.org/keywords/microeconomics","display_name":"Microeconomics","score":0.3712494969367981},{"id":"https://openalex.org/keywords/mathematical-economics","display_name":"Mathematical economics","score":0.37049606442451477},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.3348109722137451},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24626067280769348},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1767885386943817},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11584711074829102}],"concepts":[{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.7161555886268616},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.6878511905670166},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.6813610196113586},{"id":"https://openalex.org/C14646407","wikidata":"https://www.wikidata.org/wiki/Q1430750","display_name":"Bellman equation","level":2,"score":0.5608425140380859},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5563595294952393},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4958880841732025},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4669433832168579},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.3712494969367981},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.37049606442451477},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.3348109722137451},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24626067280769348},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1767885386943817},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11584711074829102},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2312.12972","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.12972","pdf_url":"https://arxiv.org/pdf/2312.12972","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2312.12972","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2312.12972","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2312.12972","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.12972","pdf_url":"https://arxiv.org/pdf/2312.12972","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4390092410.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2149537132","https://openalex.org/W2018871932","https://openalex.org/W641279757","https://openalex.org/W370975646","https://openalex.org/W1670566515","https://openalex.org/W4242022592","https://openalex.org/W596972243","https://openalex.org/W4313230280","https://openalex.org/W69751022","https://openalex.org/W1493109440"],"abstract_inverted_index":{"In":[0,118],"this":[1,42,102],"paper,":[2],"we":[3,18,53],"introduce":[4],"a":[5,48,141,154],"fresh":[6],"perspective":[7,156],"on":[8,157],"the":[9,21,45,57,78,89,93,113,124,165],"challenges":[10],"of":[11,23,47,115],"credit":[12,36],"assignment":[13,37],"and":[14,26,82,160],"policy":[15,116,134,173],"evaluation.":[16,117,174],"First,":[17],"delve":[19],"into":[20],"nuances":[22],"eligibility":[24,158],"traces":[25,159],"explore":[27],"instances":[28],"where":[29],"their":[30],"updates":[31],"may":[32],"result":[33],"in":[34,111,129],"unexpected":[35],"to":[38,55,92,100],"preceding":[39],"states.":[40],"From":[41],"investigation":[43],"emerges":[44],"concept":[46],"novel":[49,166],"value":[50,59,64,67,103,146,167],"function,":[51],"which":[52],"refer":[54],"as":[56],"\\emph{bidirectional":[58],"function}.":[60],"Unlike":[61],"traditional":[62],"state":[63,80],"functions,":[65,147],"bidirectional":[66],"functions":[68],"account":[69],"for":[70,172],"both":[71],"future":[72],"expected":[73,84],"returns":[74,85],"(rewards":[75],"anticipated":[76],"from":[77,88],"current":[79],"onward)":[81],"past":[83],"(cumulative":[86],"rewards":[87],"episode's":[90],"start":[91],"present).":[94],"We":[95],"derive":[96],"principled":[97],"update":[98],"equations":[99],"learn":[101],"function":[104,168],"and,":[105],"through":[106],"experimentation,":[107],"demonstrate":[108],"its":[109],"efficacy":[110],"enhancing":[112],"process":[114],"particular,":[119],"our":[120,151],"results":[121],"indicate":[122],"that":[123,143],"proposed":[125],"learning":[126],"approach":[127],"can,":[128],"certain":[130],"challenging":[131],"contexts,":[132],"perform":[133],"evaluation":[135],"more":[136],"rapidly":[137],"than":[138],"TD($\u03bb$)":[139],"--":[140],"method":[142],"learns":[144],"forward":[145],"$v^\u03c0$,":[148],"\\emph{directly}.":[149],"Overall,":[150],"findings":[152],"present":[153],"new":[155],"potential":[161],"advantages":[162],"associated":[163],"with":[164],"it":[169],"inspires,":[170],"especially":[171]},"counts_by_year":[],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}