{"id":"https://openalex.org/W7166136393","doi":"https://doi.org/10.48550/arxiv.2606.26397","title":"Deterministic Pareto-Optimal Policy Synthesis for Multi-Objective Reinforcement Learning","display_name":"Deterministic Pareto-Optimal Policy Synthesis for Multi-Objective Reinforcement Learning","publication_year":2026,"publication_date":"2026-06-24","ids":{"openalex":"https://openalex.org/W7166136393","doi":"https://doi.org/10.48550/arxiv.2606.26397"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.26397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26397","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.26397","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039272852","display_name":"Aniruddha Joshi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joshi, Aniruddha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120025447","display_name":"Niklas Lauffer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lauffer, Niklas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139022524","display_name":"Sanjit Seshia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seshia, Sanjit","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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.8924999833106995,"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":0.8924999833106995,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.043699998408555984,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.008200000040233135,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision 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":0.822700023651123},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.6743999719619751},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.5016000270843506},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.48019999265670776},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4569000005722046},{"id":"https://openalex.org/keywords/monotonic-function","display_name":"Monotonic function","score":0.4503999948501587},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.44699999690055847},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.4171999990940094}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.822700023651123},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.6743999719619751},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6233000159263611},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5490999817848206},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.5016000270843506},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.48019999265670776},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4569000005722046},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.4503999948501587},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.44699999690055847},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.4171999990940094},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C21424316","wikidata":"https://www.wikidata.org/wiki/Q718621","display_name":"Chebyshev filter","level":2,"score":0.3686999976634979},{"id":"https://openalex.org/C2986314615","wikidata":"https://www.wikidata.org/wiki/Q36829","display_name":"Pareto optimal","level":3,"score":0.35899999737739563},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.358599990606308},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C71564387","wikidata":"https://www.wikidata.org/wiki/Q249514","display_name":"Chebyshev's inequality","level":5,"score":0.32269999384880066},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31940001249313354},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C14646407","wikidata":"https://www.wikidata.org/wiki/Q1430750","display_name":"Bellman equation","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.28790000081062317},{"id":"https://openalex.org/C28901747","wikidata":"https://www.wikidata.org/wiki/Q177571","display_name":"Decision theory","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C8170772","wikidata":"https://www.wikidata.org/wiki/Q842436","display_name":"Markov's inequality","level":5,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.26397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26397","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.26397","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26397","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"score":0.7447397708892822,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Real-world":[0],"decision-making":[1],"often":[2,32],"requires":[3],"balancing":[4],"multiple":[5],"conflicting":[6],"objectives,":[7],"a":[8,21,52,100,125,155],"challenge":[9],"that":[10,76,95,147],"standard":[11],"Reinforcement":[12],"Learning":[13],"(RL)":[14],"frequently":[15],"addresses":[16],"by":[17],"aggregating":[18],"rewards":[19],"into":[20],"single":[22],"scalar":[23],"signal.":[24],"While":[25],"effective":[26],"for":[27,68,127,157],"simple":[28],"tasks,":[29],"this":[30,48,77,104],"approach":[31],"fails":[33],"to":[34,63,99,111],"capture":[35],"the":[36,44,59,84,89,121,132],"full":[37],"spectrum":[38],"of":[39,103],"optimal":[40],"trade-offs,":[41,153],"known":[42],"as":[43],"Pareto":[45,91],"frontier.":[46,105],"In":[47],"paper,":[49],"we":[50,107],"introduce":[51],"novel":[53],"preference-conditioned":[54],"Bellman":[55],"operator,":[56],"motivated":[57],"from":[58,115],"Chebyshev":[60],"scalarization,":[61],"designed":[62],"compute":[64],"deterministic":[65,113,158],"Pareto-optimal":[66,134,159],"policies":[67,114],"Multi-Objective":[69],"Markov":[70],"Decision":[71],"Processes":[72],"(MOMDPs).":[73],"We":[74],"prove":[75],"operator":[78],"satisfies":[79],"an":[80],"enveloping":[81],"property,":[82],"where":[83],"estimated":[85],"value":[86],"functions":[87],"upper-bound":[88],"true":[90],"frontier,":[92],"and":[93],"demonstrate":[94],"it":[96],"monotonically":[97],"converges":[98],"coverage":[101],"set":[102],"Furthermore,":[106],"also":[108],"show":[109],"how":[110],"extract":[112],"these":[116],"converged":[117],"Q-estimates.":[118],"This":[119],"ensures":[120],"agent":[122],"can":[123],"recover":[124],"policy":[126,140,160],"any":[128],"given":[129],"preference,":[130],"capturing":[131],"entire":[133],"frontier":[135],"while":[136],"guaranteeing":[137],"each":[138],"synthesized":[139],"remains":[141],"approximately":[142],"Pareto-optimal.":[143],"Experimental":[144],"results":[145],"validate":[146],"our":[148],"algorithm":[149],"successfully":[150],"recovers":[151],"complex":[152],"providing":[154],"solution":[156],"synthesis.":[161]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-27T00:00:00"}
