{"id":"https://openalex.org/W7160247256","doi":"https://doi.org/10.48550/arxiv.2605.02103","title":"Bridging the Gap Between Average and Discounted TD Learning","display_name":"Bridging the Gap Between Average and Discounted TD Learning","publication_year":2026,"publication_date":"2026-05-03","ids":{"openalex":"https://openalex.org/W7160247256","doi":"https://doi.org/10.48550/arxiv.2605.02103"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.02103","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02103","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.02103","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037554085","display_name":"Haoxing Tian","orcid":"https://orcid.org/0009-0005-3087-3202"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Haoxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058269077","display_name":"Zaiwei Chen","orcid":"https://orcid.org/0000-0001-9915-5595"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zaiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135302899","display_name":"Ioannis Ch. Paschalidis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paschalidis, Ioannis Ch.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5005863931","display_name":"Alex Olshevsky","orcid":"https://orcid.org/0000-0002-5852-9789"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Olshevsky, Alex","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.49079999327659607,"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":0.49079999327659607,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.08869999647140503,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.06809999793767929,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.6963000297546387},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.5716000199317932},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5580999851226807},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5067999958992004},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4097000062465668},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.4041999876499176},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.37790000438690186},{"id":"https://openalex.org/keywords/bellman-equation","display_name":"Bellman equation","score":0.37299999594688416},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.36970001459121704}],"concepts":[{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.6963000297546387},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5782999992370605},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.5716000199317932},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5587000250816345},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5580999851226807},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5067999958992004},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4097000062465668},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.4041999876499176},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C14646407","wikidata":"https://www.wikidata.org/wiki/Q1430750","display_name":"Bellman equation","level":2,"score":0.37299999594688416},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.36970001459121704},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3662000000476837},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.36090001463890076},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3547999858856201},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3465000092983246},{"id":"https://openalex.org/C55479107","wikidata":"https://www.wikidata.org/wiki/Q97663916","display_name":"Stochastic approximation","level":3,"score":0.34450000524520874},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3255999982357025},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.3077999949455261},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.2930999994277954},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.28519999980926514},{"id":"https://openalex.org/C95763700","wikidata":"https://www.wikidata.org/wiki/Q578985","display_name":"Convergence of random variables","level":3,"score":0.27219998836517334},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26969999074935913},{"id":"https://openalex.org/C157709441","wikidata":"https://www.wikidata.org/wiki/Q1411887","display_name":"Uniform convergence","level":3,"score":0.263700008392334},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.25619998574256897},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.02103","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02103","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.02103","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02103","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"analysis":[1,104],"of":[2,31,44,88],"Temporal":[3],"Difference":[4],"(TD)":[5],"learning":[6],"in":[7,37,65,97,123,136,158,170],"the":[8,16,66,85,137,147,152,167,171],"average-reward":[9,67],"setting":[10],"faces":[11],"notable":[12],"theoretical":[13,50],"difficulties":[14],"because":[15],"Bellman":[17,93],"operator":[18],"is":[19,105,132],"not":[20,118],"contractive":[21],"with":[22,53,130],"respect":[23],"to":[24,84,99,108,134,161],"any":[25],"norm.":[26],"This":[27],"complicates":[28],"standard":[29],"analyses":[30],"stochastic":[32],"updates":[33],"that":[34],"are":[35],"effective":[36],"discounted":[38,138,172],"settings.":[39],"Although":[40],"a":[41,57,89],"considerable":[42],"body":[43],"literature":[45],"addresses":[46],"these":[47],"challenges,":[48],"existing":[49],"approaches":[51],"come":[52],"limitations.":[54],"We":[55],"introduce":[56],"novel":[58],"algorithm":[59,142],"designed":[60],"explicitly":[61],"for":[62],"policy":[63],"evaluation":[64],"setting,":[68],"utilizing":[69],"sampling":[70],"from":[71,155],"two":[72],"Markovian":[73],"trajectories.":[74],"Our":[75],"proposed":[76],"method":[77],"overcomes":[78],"previous":[79],"limitations":[80],"by":[81],"guaranteeing":[82],"convergence":[83,103,125],"unique":[86],"solution":[87],"properly":[90],"defined":[91],"projected":[92],"equation.":[94],"Notably,":[95],"and":[96,113,116,164],"contrast":[98],"earlier":[100],"work,":[101],"our":[102,141],"uniformly":[106],"applicable":[107],"both":[109],"linear":[110],"function":[111],"approximation":[112],"tabular":[114],"settings":[115],"does":[117],"involve":[119],"explicit":[120],"dimension-dependent":[121],"terms":[122],"its":[124],"bounds.":[126],"These":[127],"results":[128],"align":[129],"what":[131],"known":[133],"hold":[135],"setting.":[139,173],"Furthermore,":[140],"achieves":[143],"improved":[144],"dependence":[145],"on":[146],"problem's":[148],"condition":[149],"number,":[150],"reducing":[151],"sample":[153],"complexity":[154],"quartic,":[156],"as":[157],"prior":[159],"literature,":[160],"quadratic":[162],"scaling,":[163],"thus":[165],"matching":[166],"efficiency":[168],"seen":[169]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-06T00:00:00"}