{"id":"https://openalex.org/W7166012556","doi":"https://doi.org/10.48550/arxiv.2606.26327","title":"EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning","display_name":"EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning","publication_year":2026,"publication_date":"2026-06-24","ids":{"openalex":"https://openalex.org/W7166012556","doi":"https://doi.org/10.48550/arxiv.2606.26327"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.26327","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26327","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":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.2606.26327","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123515154","display_name":"Boyun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Boyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139394608","display_name":"Chao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139414691","display_name":"Kai Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Kai","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/T10714","display_name":"Software-Defined Networks and 5G","score":0.4553999900817871,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.4553999900817871,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.2565000057220459,"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/T10639","display_name":"Advanced Software Engineering Methodologies","score":0.041600000113248825,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8348000049591064},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6036999821662903},{"id":"https://openalex.org/keywords/inner-loop","display_name":"Inner loop","score":0.5814999938011169},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5580000281333923},{"id":"https://openalex.org/keywords/loop","display_name":"Loop (graph theory)","score":0.545799970626831},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.41679999232292175},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.39739999175071716},{"id":"https://openalex.org/keywords/random-search","display_name":"Random search","score":0.34769999980926514}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8348000049591064},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6725000143051147},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6036999821662903},{"id":"https://openalex.org/C58716799","wikidata":"https://www.wikidata.org/wiki/Q6035648","display_name":"Inner loop","level":3,"score":0.5814999938011169},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5580000281333923},{"id":"https://openalex.org/C184670325","wikidata":"https://www.wikidata.org/wiki/Q512604","display_name":"Loop (graph theory)","level":2,"score":0.545799970626831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4909999966621399},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C126661757","wikidata":"https://www.wikidata.org/wiki/Q4925641","display_name":"Random search","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3418999910354614},{"id":"https://openalex.org/C186886427","wikidata":"https://www.wikidata.org/wiki/Q5441213","display_name":"Feedback loop","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C2777466363","wikidata":"https://www.wikidata.org/wiki/Q17008971","display_name":"Design tool","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.30329999327659607},{"id":"https://openalex.org/C2780626000","wikidata":"https://www.wikidata.org/wiki/Q5936775","display_name":"Human-in-the-loop","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C98025372","wikidata":"https://www.wikidata.org/wiki/Q477538","display_name":"Systems architecture","level":3,"score":0.296099990606308},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2786000072956085},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C125583679","wikidata":"https://www.wikidata.org/wiki/Q755673","display_name":"Search algorithm","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.27239999175071716},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.26010000705718994},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.26327","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26327","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.26327","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26327","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"actor-critic":[1,43],"reinforcement":[2],"learning,":[3],"network":[4],"architectures":[5],"are":[6,144],"typically":[7],"manually":[8,108],"designed.":[9],"Automating":[10],"this":[11,77],"design":[12,25,85],"is":[13,27,80],"challenging":[14],"because":[15],"each":[16],"candidate":[17],"must":[18],"be":[19],"trained":[20],"before":[21],"evaluation,":[22],"and":[23,99,115,129,139],"the":[24,59,107,116,136,140],"space":[26],"open-ended.":[28],"To":[29],"address":[30],"these":[31],"challenges,":[32],"we":[33],"introduce":[34],"EVOM,":[35],"an":[36,53,66,83,91,111],"agentic":[37],"meta-evolution":[38,70,137],"framework":[39],"for":[40,146],"discovering":[41],"high-performance":[42],"architectures.":[44],"We":[45],"frame":[46],"architecture":[47,74,92],"search":[48,121],"as":[49,90],"a":[50],"bi-level":[51],"optimization:":[52],"inner":[54],"loop":[55,68,79,138],"trains":[56],"weights":[57],"via":[58],"low-fidelity":[60],"proximal":[61],"policy":[62,97,120],"optimization":[63],"(PPO),":[64],"while":[65],"outer":[67,78],"drives":[69],"by":[71,82],"iteratively":[72],"refining":[73],"programs.":[75],"Crucially,":[76],"powered":[81],"LLM-based":[84],"agent":[86],"that":[87,104,134],"operates":[88],"purely":[89],"designer,":[93],"completely":[94],"decoupled":[95],"from":[96],"execution":[98],"environment":[100],"control.":[101],"Experiments":[102],"reveal":[103],"EVOM":[105],"outperforms":[106],"designed":[109],"baseline,":[110],"LLM-guided":[112,118],"random":[113],"search,":[114],"state-of-the-art":[117],"programmatic":[119],"method":[122],"MLES,":[123],"delivering":[124],"superior":[125],"performance":[126],"on":[127],"Ant-v4":[128],"HalfCheetah-v4.":[130],"Ablation":[131],"studies":[132],"validate":[133],"both":[135],"LLM":[141],"Design":[142],"Agent":[143],"indispensable":[145],"final":[147],"performance.":[148]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-27T00:00:00"}